From 0541442558edb3771ec469b46bf236c0679a1cb2 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 13:47:33 +0100 Subject: [PATCH 001/111] add do_lower_case in examples --- examples/extract_features.py | 3 ++- examples/run_classifier.py | 6 +++++- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/examples/extract_features.py b/examples/extract_features.py index abe7fdffe7db..dbab934c0813 100644 --- a/examples/extract_features.py +++ b/examples/extract_features.py @@ -199,6 +199,7 @@ def main(): "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.") ## Other parameters + parser.add_argument("--do_lower_case", default=False, action='store_true', help="Set this flag if you are using an uncased model.") parser.add_argument("--layers", default="-1,-2,-3,-4", type=str) parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after WordPiece tokenization. Sequences longer " @@ -227,7 +228,7 @@ def main(): layer_indexes = [int(x) for x in args.layers.split(",")] - tokenizer = BertTokenizer.from_pretrained(args.bert_model) + tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) examples = read_examples(args.input_file) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index 2c83b4fe497f..52f3cd752d0d 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -376,6 +376,10 @@ def main(): default=False, action='store_true', help="Whether to run eval on the dev set.") + parser.add_argument("--do_lower_case", + default=False, + action='store_true', + help="Set this flag if you are using an uncased model.") parser.add_argument("--train_batch_size", default=32, type=int, @@ -473,7 +477,7 @@ def main(): processor = processors[task_name]() label_list = processor.get_labels() - tokenizer = BertTokenizer.from_pretrained(args.bert_model) + tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) train_examples = None num_train_steps = None From 298107fed79d143552ec4294cf4924301b1fc455 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 13:56:02 +0100 Subject: [PATCH 002/111] Added new bert models --- README.md | 4 +++- pytorch_pretrained_bert/modeling.py | 4 +++- pytorch_pretrained_bert/tokenization.py | 4 +++- 3 files changed, 9 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index eb337d8253f4..43c72efc4e45 100644 --- a/README.md +++ b/README.md @@ -175,7 +175,9 @@ where - `bert-base-uncased`: 12-layer, 768-hidden, 12-heads, 110M parameters - `bert-large-uncased`: 24-layer, 1024-hidden, 16-heads, 340M parameters - `bert-base-cased`: 12-layer, 768-hidden, 12-heads , 110M parameters - - `bert-base-multilingual`: 102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters + - `bert-large-cased`: 24-layer, 1024-hidden, 16-heads, 340M parameters + - `bert-base-multilingual-uncased`: (Orig, not recommended) 102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters + - `bert-base-multilingual-cased`: (New, recommended) 104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters - `bert-base-chinese`: Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M parameters - a path or url to a pretrained model archive containing: diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index 2d6dfa531dc5..30d940631cc3 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -42,7 +42,9 @@ 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz", 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz", 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz", - 'bert-base-multilingual': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual.tar.gz", + 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz", + 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz", + 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz", 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz", } CONFIG_NAME = 'bert_config.json' diff --git a/pytorch_pretrained_bert/tokenization.py b/pytorch_pretrained_bert/tokenization.py index c37a7e3b9ee3..fefdaa54a020 100644 --- a/pytorch_pretrained_bert/tokenization.py +++ b/pytorch_pretrained_bert/tokenization.py @@ -34,7 +34,9 @@ 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt", 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt", 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt", - 'bert-base-multilingual': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-vocab.txt", + 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt", + 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt", + 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt", 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt", } From 296f0061326c0c3d688dda73d0655452a700f1e6 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 13:56:53 +0100 Subject: [PATCH 003/111] added BertForTokenClassification model --- README.md | 14 +++++- pytorch_pretrained_bert/modeling.py | 66 +++++++++++++++++++++++++++++ 2 files changed, 78 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 43c72efc4e45..c41c07eb692e 100644 --- a/README.md +++ b/README.md @@ -153,7 +153,7 @@ Here is a detailed documentation of the classes in the package and how to use th | Sub-section | Description | |-|-| | [Loading Google AI's pre-trained weigths](#Loading-Google-AIs-pre-trained-weigths-and-PyTorch-dump) | How to load Google AI's pre-trained weight or a PyTorch saved instance | -| [PyTorch models](#PyTorch-models) | API of the six PyTorch model classes: `BertModel`, `BertForMaskedLM`, `BertForNextSentencePrediction`, `BertForPreTraining`, `BertForSequenceClassification` or `BertForQuestionAnswering` | +| [PyTorch models](#PyTorch-models) | API of the seven PyTorch model classes: `BertModel`, `BertForMaskedLM`, `BertForNextSentencePrediction`, `BertForPreTraining`, `BertForSequenceClassification` or `BertForQuestionAnswering` | | [Tokenizer: `BertTokenizer`](#Tokenizer-BertTokenizer) | API of the `BertTokenizer` class| | [Optimizer: `BertAdam`](#Optimizer-BertAdam) | API of the `BertAdam` class | @@ -188,6 +188,10 @@ where If `PRE_TRAINED_MODEL_NAME_OR_PATH` is a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the links [here](pytorch_pretrained_bert/modeling.py)) and stored in a cache folder to avoid future download (the cache folder can be found at `~/.pytorch_pretrained_bert/`). - `cache_dir` can be an optional path to a specific directory to download and cache the pre-trained model weights. This option is useful in particular when you are using distributed training: to avoid concurrent access to the same weights you can set for example `cache_dir='./pretrained_model_{}'.format(args.local_rank)` (see the section on distributed training for more information) +`Uncased` means that the text has been lowercased before WordPiece tokenization, e.g., `John Smith` becomes `john smith`. The Uncased model also strips out any accent markers. `Cased` means that the true case and accent markers are preserved. Typically, the Uncased model is better unless you know that case information is important for your task (e.g., Named Entity Recognition or Part-of-Speech tagging). For information about the Multilingual and Chinese model, see the [Multilingual README](https://github.com/google-research/bert/blob/master/multilingual.md) or the original TensorFlow repository. + +**When using an `uncased model`, make sure to pass `--do_lower_case` to the training scripts. (Or pass `do_lower_case=True` directly to FullTokenizer if you're using your own script.)** + Example: ```python model = BertForSequenceClassification.from_pretrained('bert-base-uncased') @@ -273,7 +277,13 @@ The sequence-level classifier is a linear layer that takes as input the last hid An example on how to use this class is given in the `run_classifier.py` script which can be used to fine-tune a single sequence (or pair of sequence) classifier using BERT, for example for the MRPC task. -#### 6. `BertForQuestionAnswering` +#### 6. `BertForTokenClassification` + +`BertForTokenClassification` is a fine-tuning model that includes `BertModel` and a token-level classifier on top of the `BertModel`. + +The token-level classifier is a linear layer that takes as input the last hidden state of the sequence. + +#### 7. `BertForQuestionAnswering` `BertForQuestionAnswering` is a fine-tuning model that includes `BertModel` with a token-level classifiers on top of the full sequence of last hidden states. diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index 30d940631cc3..473b7e702241 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -877,6 +877,72 @@ def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=No return logits +class BertForTokenClassification(PreTrainedBertModel): + """BERT model for token-level classification. + This module is composed of the BERT model with a linear layer on top of + the full hidden state of the last layer. + + Params: + `config`: a BertConfig class instance with the configuration to build a new model. + `num_labels`: the number of classes for the classifier. Default = 2. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `labels`: labels for the classification output: torch.LongTensor of shape [batch_size] + with indices selected in [0, ..., num_labels]. + + Outputs: + if `labels` is not `None`: + Outputs the CrossEntropy classification loss of the output with the labels. + if `labels` is `None`: + Outputs the classification logits. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + num_labels = 2 + + model = BertForTokenClassification(config, num_labels) + logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config, num_labels=2): + super(BertForTokenClassification, self).__init__(config) + self.num_labels = num_labels + self.bert = BertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, num_labels) + self.apply(self.init_bert_weights) + + def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None): + sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) + pooled_output = self.dropout(sequence_output) + logits = self.classifier(pooled_output) + + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + return loss, logits + else: + return logits + + class BertForQuestionAnswering(PreTrainedBertModel): """BERT model for Question Answering (span extraction). This module is composed of the BERT model with a linear layer on top of From 532a81d3d6b4b7b1ec71f3a119f3ff935a33f3c5 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 13:57:01 +0100 Subject: [PATCH 004/111] fixed doc_strings --- pytorch_pretrained_bert/modeling.py | 34 ++++++++++++++--------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index 473b7e702241..70a41d91e71c 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -569,10 +569,10 @@ class BertModel(PreTrainedBertModel): # Already been converted into WordPiece token ids input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) - token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) - config = modeling.BertConfig(vocab_size=32000, hidden_size=512, - num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) + config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) model = modeling.BertModel(config=config) all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask) @@ -658,10 +658,10 @@ class BertForPreTraining(PreTrainedBertModel): # Already been converted into WordPiece token ids input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) - token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) - config = BertConfig(vocab_size=32000, hidden_size=512, - num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) model = BertForPreTraining(config) masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask) @@ -721,10 +721,10 @@ class BertForMaskedLM(PreTrainedBertModel): # Already been converted into WordPiece token ids input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) - token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) - config = BertConfig(vocab_size=32000, hidden_size=512, - num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) model = BertForMaskedLM(config) masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask) @@ -785,8 +785,8 @@ class BertForNextSentencePrediction(PreTrainedBertModel): input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) - config = BertConfig(vocab_size=32000, hidden_size=512, - num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) model = BertForNextSentencePrediction(config) seq_relationship_logits = model(input_ids, token_type_ids, input_mask) @@ -845,10 +845,10 @@ class BertForSequenceClassification(PreTrainedBertModel): # Already been converted into WordPiece token ids input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) - token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) - config = BertConfig(vocab_size=32000, hidden_size=512, - num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) num_labels = 2 @@ -989,10 +989,10 @@ class BertForQuestionAnswering(PreTrainedBertModel): # Already been converted into WordPiece token ids input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) - token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) - config = BertConfig(vocab_size=32000, hidden_size=512, - num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) model = BertForQuestionAnswering(config) start_logits, end_logits = model(input_ids, token_type_ids, input_mask) From d6f06c03f4658a80bef76ae226494864b476e391 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 14:09:06 +0100 Subject: [PATCH 005/111] fixed loading pre-trained tokenizer from directory --- pytorch_pretrained_bert/modeling.py | 2 +- pytorch_pretrained_bert/tokenization.py | 25 ++++++++++++++----------- 2 files changed, 15 insertions(+), 12 deletions(-) diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index 70a41d91e71c..e8ad26a1c675 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -478,7 +478,7 @@ def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwarg "associated to this path or url.".format( pretrained_model_name, ', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()), - pretrained_model_name)) + archive_file)) return None if resolved_archive_file == archive_file: logger.info("loading archive file {}".format(archive_file)) diff --git a/pytorch_pretrained_bert/tokenization.py b/pytorch_pretrained_bert/tokenization.py index fefdaa54a020..c7ef20ddefcb 100644 --- a/pytorch_pretrained_bert/tokenization.py +++ b/pytorch_pretrained_bert/tokenization.py @@ -39,6 +39,7 @@ 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt", 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt", } +VOCAB_NAME = 'vocab.txt' def load_vocab(vocab_file): @@ -100,7 +101,7 @@ def convert_ids_to_tokens(self, ids): return tokens @classmethod - def from_pretrained(cls, pretrained_model_name, do_lower_case=True): + def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwargs): """ Instantiate a PreTrainedBertModel from a pre-trained model file. Download and cache the pre-trained model file if needed. @@ -109,16 +110,11 @@ def from_pretrained(cls, pretrained_model_name, do_lower_case=True): vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name] else: vocab_file = pretrained_model_name + if os.path.isdir(vocab_file): + vocab_file = os.path.join(vocab_file, VOCAB_NAME) # redirect to the cache, if necessary try: - resolved_vocab_file = cached_path(vocab_file) - if resolved_vocab_file == vocab_file: - logger.info("loading vocabulary file {}".format(vocab_file)) - else: - logger.info("loading vocabulary file {} from cache at {}".format( - vocab_file, resolved_vocab_file)) - # Instantiate tokenizer. - tokenizer = cls(resolved_vocab_file, do_lower_case) + resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir) except FileNotFoundError: logger.error( "Model name '{}' was not found in model name list ({}). " @@ -126,8 +122,15 @@ def from_pretrained(cls, pretrained_model_name, do_lower_case=True): "associated to this path or url.".format( pretrained_model_name, ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()), - pretrained_model_name)) - tokenizer = None + vocab_file)) + return None + if resolved_vocab_file == vocab_file: + logger.info("loading vocabulary file {}".format(vocab_file)) + else: + logger.info("loading vocabulary file {} from cache at {}".format( + vocab_file, resolved_vocab_file)) + # Instantiate tokenizer. + tokenizer = cls(resolved_vocab_file, *inputs, **kwargs) return tokenizer From c588453a0fc8469b96b0a055bd301516f0a33261 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 14:22:40 +0100 Subject: [PATCH 006/111] fix run_squad --- examples/run_squad.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/run_squad.py b/examples/run_squad.py index e3213189bfba..041a51a879fa 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -878,7 +878,7 @@ def main(): if args.local_rank == -1 or torch.distributed.get_rank() == 0: logger.info(" Saving train features into cached file %s", cached_train_features_file) with open(cached_train_features_file, "wb") as writer: - train_features = pickle.dump(train_features, writer) + pickle.dump(train_features, writer) logger.info("***** Running training *****") logger.info(" Num orig examples = %d", len(train_examples)) logger.info(" Num split examples = %d", len(train_features)) From 257a35134a1bd378b16aa985ee76675289ff439c Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 14:23:09 +0100 Subject: [PATCH 007/111] fix pickle dump in run_squad example --- examples/run_squad.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/run_squad.py b/examples/run_squad.py index e3213189bfba..041a51a879fa 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -878,7 +878,7 @@ def main(): if args.local_rank == -1 or torch.distributed.get_rank() == 0: logger.info(" Saving train features into cached file %s", cached_train_features_file) with open(cached_train_features_file, "wb") as writer: - train_features = pickle.dump(train_features, writer) + pickle.dump(train_features, writer) logger.info("***** Running training *****") logger.info(" Num orig examples = %d", len(train_examples)) logger.info(" Num split examples = %d", len(train_features)) From 7b3bb8c00f6dcf8ac9613cd2f3cdc71d57b0c275 Mon Sep 17 00:00:00 2001 From: Malte Pietsch Date: Fri, 30 Nov 2018 16:52:50 +0100 Subject: [PATCH 008/111] fix typo in input for masked lm loss function --- pytorch_pretrained_bert/modeling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index 2d6dfa531dc5..599d0023fc30 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -678,7 +678,7 @@ def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm if masked_lm_labels is not None and next_sentence_label is not None: loss_fct = CrossEntropyLoss(ignore_index=-1) - masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels(-1)) + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1)) next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) total_loss = masked_lm_loss + next_sentence_loss return total_loss From be57c8eeef69ec22edc577e6aba17eb8609ad65b Mon Sep 17 00:00:00 2001 From: Nirant Date: Sat, 1 Dec 2018 02:43:25 +0530 Subject: [PATCH 009/111] Fix internal hyperlink typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index eb337d8253f4..bbf835e65d2d 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ This implementation is provided with [Google's pre-trained models](https://githu | [Doc](#doc) | Detailed documentation | | [Examples](#examples) | Detailed examples on how to fine-tune Bert | | [Notebooks](#notebooks) | Introduction on the provided Jupyter Notebooks | -| [TPU](#tup) | Notes on TPU support and pretraining scripts | +| [TPU](#tpu) | Notes on TPU support and pretraining scripts | | [Command-line interface](#Command-line-interface) | Convert a TensorFlow checkpoint in a PyTorch dump | ## Installation From 7f7c41b0c17789eae82e5665008305298e7576b9 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 22:54:33 +0100 Subject: [PATCH 010/111] tests for all model classes with and without labels --- tests/modeling_test.py | 168 +++++++++++++++++++++++++++++++++++++++-- 1 file changed, 160 insertions(+), 8 deletions(-) diff --git a/tests/modeling_test.py b/tests/modeling_test.py index 48d56826f8e9..b5665121397d 100644 --- a/tests/modeling_test.py +++ b/tests/modeling_test.py @@ -22,7 +22,10 @@ import torch -from pytorch_pretrained_bert import BertConfig, BertModel +from pytorch_pretrained_bert import (BertConfig, BertModel, BertForMaskedLM, + BertForNextSentencePrediction, BertForPreTraining, + BertForQuestionAnswering, BertForSequenceClassification, + BertForTokenClassification) class BertModelTest(unittest.TestCase): @@ -35,6 +38,7 @@ def __init__(self, is_training=True, use_input_mask=True, use_token_type_ids=True, + use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, @@ -45,7 +49,9 @@ def __init__(self, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, + type_sequence_label_size=2, initializer_range=0.02, + num_labels=3, scope=None): self.parent = parent self.batch_size = batch_size @@ -53,6 +59,7 @@ def __init__(self, self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids + self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers @@ -63,10 +70,12 @@ def __init__(self, self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size + self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range + self.num_labels = num_labels self.scope = scope - def create_model(self): + def prepare_config_and_inputs(self): input_ids = BertModelTest.ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None @@ -77,6 +86,12 @@ def create_model(self): if self.use_token_type_ids: token_type_ids = BertModelTest.ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) + sequence_labels = None + token_labels = None + if self.use_labels: + sequence_labels = BertModelTest.ids_tensor([self.batch_size], self.type_sequence_label_size) + token_labels = BertModelTest.ids_tensor([self.batch_size, self.seq_length], self.num_labels) + config = BertConfig( vocab_size_or_config_json_file=self.vocab_size, hidden_size=self.hidden_size, @@ -90,10 +105,16 @@ def create_model(self): type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range) - model = BertModel(config=config) + return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels - all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask) + def check_loss_output(self, result): + self.parent.assertListEqual( + list(result["loss"].size()), + []) + def create_bert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels): + model = BertModel(config=config) + all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask) outputs = { "sequence_output": all_encoder_layers[-1], "pooled_output": pooled_output, @@ -101,13 +122,119 @@ def create_model(self): } return outputs - def check_output(self, result): + def check_bert_model_output(self, result): + self.parent.assertListEqual( + [size for layer in result["all_encoder_layers"] for size in layer.size()], + [self.batch_size, self.seq_length, self.hidden_size] * self.num_hidden_layers) self.parent.assertListEqual( list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]) - self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size]) + + def create_bert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels): + model = BertForMaskedLM(config=config) + loss = model(input_ids, token_type_ids, input_mask, token_labels) + prediction_scores = model(input_ids, token_type_ids, input_mask) + outputs = { + "loss": loss, + "prediction_scores": prediction_scores, + } + return outputs + + def check_bert_for_masked_lm_output(self, result): + self.parent.assertListEqual( + list(result["prediction_scores"].size()), + [self.batch_size, self.seq_length, self.vocab_size]) + + def create_bert_for_next_sequence_prediction(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels): + model = BertForNextSentencePrediction(config=config) + loss = model(input_ids, token_type_ids, input_mask, sequence_labels) + seq_relationship_score = model(input_ids, token_type_ids, input_mask) + outputs = { + "loss": loss, + "seq_relationship_score": seq_relationship_score, + } + return outputs + + def check_bert_for_next_sequence_prediction_output(self, result): + self.parent.assertListEqual( + list(result["seq_relationship_score"].size()), + [self.batch_size, 2]) + + + def create_bert_for_pretraining(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels): + model = BertForPreTraining(config=config) + loss = model(input_ids, token_type_ids, input_mask, token_labels, sequence_labels) + prediction_scores, seq_relationship_score = model(input_ids, token_type_ids, input_mask) + outputs = { + "loss": loss, + "prediction_scores": prediction_scores, + "seq_relationship_score": seq_relationship_score, + } + return outputs + + def check_bert_for_pretraining_output(self, result): + self.parent.assertListEqual( + list(result["prediction_scores"].size()), + [self.batch_size, self.seq_length, self.vocab_size]) + self.parent.assertListEqual( + list(result["seq_relationship_score"].size()), + [self.batch_size, 2]) + + + def create_bert_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels): + model = BertForQuestionAnswering(config=config) + loss = model(input_ids, token_type_ids, input_mask, sequence_labels, sequence_labels) + start_logits, end_logits = model(input_ids, token_type_ids, input_mask) + outputs = { + "loss": loss, + "start_logits": start_logits, + "end_logits": end_logits, + } + return outputs + + def check_bert_for_question_answering_output(self, result): + self.parent.assertListEqual( + list(result["start_logits"].size()), + [self.batch_size, self.seq_length]) + self.parent.assertListEqual( + list(result["end_logits"].size()), + [self.batch_size, self.seq_length]) + + + def create_bert_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels): + model = BertForSequenceClassification(config=config, num_labels=self.num_labels) + loss = model(input_ids, token_type_ids, input_mask, sequence_labels) + logits = model(input_ids, token_type_ids, input_mask) + outputs = { + "loss": loss, + "logits": logits, + } + return outputs + + def check_bert_for_sequence_classification_output(self, result): + self.parent.assertListEqual( + list(result["logits"].size()), + [self.batch_size, self.num_labels]) + + + def create_bert_for_token_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels): + model = BertForTokenClassification(config=config, num_labels=self.num_labels) + loss = model(input_ids, token_type_ids, input_mask, token_labels) + logits = model(input_ids, token_type_ids, input_mask) + outputs = { + "loss": loss, + "logits": logits, + } + return outputs + + def check_bert_for_token_classification_output(self, result): + self.parent.assertListEqual( + list(result["logits"].size()), + [self.batch_size, self.seq_length, self.num_labels]) + + def test_default(self): self.run_tester(BertModelTest.BertModelTester(self)) @@ -118,8 +245,33 @@ def test_config_to_json_string(self): self.assertEqual(obj["hidden_size"], 37) def run_tester(self, tester): - output_result = tester.create_model() - tester.check_output(output_result) + config_and_inputs = tester.prepare_config_and_inputs() + output_result = tester.create_bert_model(*config_and_inputs) + tester.check_bert_model_output(output_result) + + output_result = tester.create_bert_for_masked_lm(*config_and_inputs) + tester.check_bert_for_masked_lm_output(output_result) + tester.check_loss_output(output_result) + + output_result = tester.create_bert_for_next_sequence_prediction(*config_and_inputs) + tester.check_bert_for_next_sequence_prediction_output(output_result) + tester.check_loss_output(output_result) + + output_result = tester.create_bert_for_pretraining(*config_and_inputs) + tester.check_bert_for_pretraining_output(output_result) + tester.check_loss_output(output_result) + + output_result = tester.create_bert_for_question_answering(*config_and_inputs) + tester.check_bert_for_question_answering_output(output_result) + tester.check_loss_output(output_result) + + output_result = tester.create_bert_for_sequence_classification(*config_and_inputs) + tester.check_bert_for_sequence_classification_output(output_result) + tester.check_loss_output(output_result) + + output_result = tester.create_bert_for_token_classification(*config_and_inputs) + tester.check_bert_for_token_classification_output(output_result) + tester.check_loss_output(output_result) @classmethod def ids_tensor(cls, shape, vocab_size, rng=None, name=None): From 89d47230d74061d57b700eed056f6a6763315610 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 22:54:53 +0100 Subject: [PATCH 011/111] clean up classification model output --- examples/run_classifier.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index 52f3cd752d0d..23c2bea057ea 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -546,7 +546,7 @@ def main(): for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): batch = tuple(t.to(device) for t in batch) input_ids, input_mask, segment_ids, label_ids = batch - loss, _ = model(input_ids, segment_ids, input_mask, label_ids) + loss = model(input_ids, segment_ids, input_mask, label_ids) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. if args.fp16 and args.loss_scale != 1.0: From ed302a73f4cc365db4cd29d26ca722d605bc85a1 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 22:55:03 +0100 Subject: [PATCH 012/111] add new token classification model --- pytorch_pretrained_bert/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pytorch_pretrained_bert/__init__.py b/pytorch_pretrained_bert/__init__.py index 7850fa5555e5..fc9b15a12d31 100644 --- a/pytorch_pretrained_bert/__init__.py +++ b/pytorch_pretrained_bert/__init__.py @@ -1,6 +1,7 @@ from .tokenization import BertTokenizer, BasicTokenizer, WordpieceTokenizer from .modeling import (BertConfig, BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction, - BertForSequenceClassification, BertForQuestionAnswering) + BertForSequenceClassification, BertForTokenClassification, + BertForQuestionAnswering) from .optimization import BertAdam from .file_utils import PYTORCH_PRETRAINED_BERT_CACHE From d787c6be8c5fc88190f5723745f5e05d15f6b30c Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 22:55:26 +0100 Subject: [PATCH 013/111] improve docstrings and fix new token classification model --- pytorch_pretrained_bert/modeling.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index e8ad26a1c675..13666c86dffb 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -559,7 +559,7 @@ class BertModel(PreTrainedBertModel): of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size], - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding - to the last attention block, + to the last attention block of shape [batch_size, sequence_length, hidden_size], `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a classifier pretrained on top of the hidden state associated to the first character of the input (`CLF`) to train on the Next-Sentence task (see BERT's paper). @@ -650,8 +650,8 @@ class BertForPreTraining(PreTrainedBertModel): sentence classification loss. if `masked_lm_labels` or `next_sentence_label` is `None`: Outputs a tuple comprising - - the masked language modeling logits, and - - the next sentence classification logits. + - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and + - the next sentence classification logits of shape [batch_size, 2]. Example usage: ```python @@ -680,7 +680,7 @@ def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm if masked_lm_labels is not None and next_sentence_label is not None: loss_fct = CrossEntropyLoss(ignore_index=-1) - masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels(-1)) + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1)) next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) total_loss = masked_lm_loss + next_sentence_loss return total_loss @@ -714,7 +714,7 @@ class BertForMaskedLM(PreTrainedBertModel): if `masked_lm_labels` is `None`: Outputs the masked language modeling loss. if `masked_lm_labels` is `None`: - Outputs the masked language modeling logits. + Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size]. Example usage: ```python @@ -776,7 +776,7 @@ class BertForNextSentencePrediction(PreTrainedBertModel): Outputs the total_loss which is the sum of the masked language modeling loss and the next sentence classification loss. if `next_sentence_label` is `None`: - Outputs the next sentence classification logits. + Outputs the next sentence classification logits of shape [batch_size, 2]. Example usage: ```python @@ -838,7 +838,7 @@ class BertForSequenceClassification(PreTrainedBertModel): if `labels` is not `None`: Outputs the CrossEntropy classification loss of the output with the labels. if `labels` is `None`: - Outputs the classification logits. + Outputs the classification logits of shape [batch_size, num_labels]. Example usage: ```python @@ -872,7 +872,7 @@ def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=No if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) - return loss, logits + return loss else: return logits @@ -904,7 +904,7 @@ class BertForTokenClassification(PreTrainedBertModel): if `labels` is not `None`: Outputs the CrossEntropy classification loss of the output with the labels. if `labels` is `None`: - Outputs the classification logits. + Outputs the classification logits of shape [batch_size, sequence_length, num_labels]. Example usage: ```python @@ -938,7 +938,7 @@ def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=No if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) - return loss, logits + return loss else: return logits @@ -982,7 +982,7 @@ class BertForQuestionAnswering(PreTrainedBertModel): Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions. if `start_positions` or `end_positions` is `None`: Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end - position tokens. + position tokens of shape [batch_size, sequence_length]. Example usage: ```python From 258eb500861b0f594f1aeb580268705f888e115f Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 22:55:33 +0100 Subject: [PATCH 014/111] bump up version --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 9b2a67883200..fc793b53e695 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name="pytorch_pretrained_bert", - version="0.2.0", + version="0.3.0", author="Thomas Wolf, Victor Sanh, Tim Rault, Google AI Language Team Authors", author_email="thomas@huggingface.co", description="PyTorch version of Google AI BERT model with script to load Google pre-trained models", From 511bce58bd93d2cbc6eef21773b99b7a35b0d814 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 22:56:02 +0100 Subject: [PATCH 015/111] update new token classification model --- pytorch_pretrained_bert/modeling.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index 13666c86dffb..3af5854072fe 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -932,8 +932,8 @@ def __init__(self, config, num_labels=2): def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None): sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) - pooled_output = self.dropout(sequence_output) - logits = self.classifier(pooled_output) + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) if labels is not None: loss_fct = CrossEntropyLoss() From 52ff0590ffc3825a6bd003685e05c0f06bc480a2 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 23:01:10 +0100 Subject: [PATCH 016/111] tup => tpu --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c41c07eb692e..08d45ad5d38b 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ This implementation is provided with [Google's pre-trained models](https://githu | [Doc](#doc) | Detailed documentation | | [Examples](#examples) | Detailed examples on how to fine-tune Bert | | [Notebooks](#notebooks) | Introduction on the provided Jupyter Notebooks | -| [TPU](#tup) | Notes on TPU support and pretraining scripts | +| [TPU](#tpu) | Notes on TPU support and pretraining scripts | | [Command-line interface](#Command-line-interface) | Convert a TensorFlow checkpoint in a PyTorch dump | ## Installation From f9f3bdd60bc84d9cd399c7df5ad6b7d47da8e5a9 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 30 Nov 2018 23:05:18 +0100 Subject: [PATCH 017/111] update readme --- README.md | 25 +++++++++++++------------ 1 file changed, 13 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 08d45ad5d38b..f105e0e35b96 100644 --- a/README.md +++ b/README.md @@ -46,13 +46,14 @@ python -m pytest -sv tests/ This package comprises the following classes that can be imported in Python and are detailed in the [Doc](#doc) section of this readme: -- Six PyTorch models (`torch.nn.Module`) for Bert with pre-trained weights (in the [`modeling.py`](./pytorch_pretrained_bert/modeling.py) file): - - [`BertModel`](./pytorch_pretrained_bert/modeling.py#L535) - raw BERT Transformer model (**fully pre-trained**), - - [`BertForMaskedLM`](./pytorch_pretrained_bert/modeling.py#L689) - BERT Transformer with the pre-trained masked language modeling head on top (**fully pre-trained**), - - [`BertForNextSentencePrediction`](./pytorch_pretrained_bert/modeling.py#L750) - BERT Transformer with the pre-trained next sentence prediction classifier on top (**fully pre-trained**), - - [`BertForPreTraining`](./pytorch_pretrained_bert/modeling.py#L618) - BERT Transformer with masked language modeling head and next sentence prediction classifier on top (**fully pre-trained**), - - [`BertForSequenceClassification`](./pytorch_pretrained_bert/modeling.py#L812) - BERT Transformer with a sequence classification head on top (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**), - - [`BertForQuestionAnswering`](./pytorch_pretrained_bert/modeling.py#L877) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**). +- Seven PyTorch models (`torch.nn.Module`) for Bert with pre-trained weights (in the [`modeling.py`](./pytorch_pretrained_bert/modeling.py) file): + - [`BertModel`](./pytorch_pretrained_bert/modeling.py#L537) - raw BERT Transformer model (**fully pre-trained**), + - [`BertForMaskedLM`](./pytorch_pretrained_bert/modeling.py#L691) - BERT Transformer with the pre-trained masked language modeling head on top (**fully pre-trained**), + - [`BertForNextSentencePrediction`](./pytorch_pretrained_bert/modeling.py#L752) - BERT Transformer with the pre-trained next sentence prediction classifier on top (**fully pre-trained**), + - [`BertForPreTraining`](./pytorch_pretrained_bert/modeling.py#L620) - BERT Transformer with masked language modeling head and next sentence prediction classifier on top (**fully pre-trained**), + - [`BertForSequenceClassification`](./pytorch_pretrained_bert/modeling.py#L814) - BERT Transformer with a sequence classification head on top (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**), + - [`BertForTokenClassification`](./pytorch_pretrained_bert/modeling.py#L880) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**), + - [`BertForQuestionAnswering`](./pytorch_pretrained_bert/modeling.py#L946) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**). - Three tokenizers (in the [`tokenization.py`](./pytorch_pretrained_bert/tokenization.py) file): - `BasicTokenizer` - basic tokenization (punctuation splitting, lower casing, etc.), @@ -167,7 +168,7 @@ model = BERT_CLASS.from_pretrain(PRE_TRAINED_MODEL_NAME_OR_PATH, cache_dir=None) where -- `BERT_CLASS` is either the `BertTokenizer` class (to load the vocabulary) or one of the six PyTorch model classes (to load the pre-trained weights): `BertModel`, `BertForMaskedLM`, `BertForNextSentencePrediction`, `BertForPreTraining`, `BertForSequenceClassification` or `BertForQuestionAnswering`, and +- `BERT_CLASS` is either the `BertTokenizer` class (to load the vocabulary) or one of the seven PyTorch model classes (to load the pre-trained weights): `BertModel`, `BertForMaskedLM`, `BertForNextSentencePrediction`, `BertForPreTraining`, `BertForSequenceClassification`, `BertForTokenClassification` or `BertForQuestionAnswering`, and - `PRE_TRAINED_MODEL_NAME_OR_PATH` is either: - the shortcut name of a Google AI's pre-trained model selected in the list: @@ -177,13 +178,13 @@ where - `bert-base-cased`: 12-layer, 768-hidden, 12-heads , 110M parameters - `bert-large-cased`: 24-layer, 1024-hidden, 16-heads, 340M parameters - `bert-base-multilingual-uncased`: (Orig, not recommended) 102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters - - `bert-base-multilingual-cased`: (New, recommended) 104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters + - `bert-base-multilingual-cased`: **(New, recommended)** 104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters - `bert-base-chinese`: Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M parameters - a path or url to a pretrained model archive containing: - - - `bert_config.json` a configuration file for the model, and - - `pytorch_model.bin` a PyTorch dump of a pre-trained instance `BertForPreTraining` (saved with the usual `torch.save()`) + + - `bert_config.json` a configuration file for the model, and + - `pytorch_model.bin` a PyTorch dump of a pre-trained instance `BertForPreTraining` (saved with the usual `torch.save()`) If `PRE_TRAINED_MODEL_NAME_OR_PATH` is a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the links [here](pytorch_pretrained_bert/modeling.py)) and stored in a cache folder to avoid future download (the cache folder can be found at `~/.pytorch_pretrained_bert/`). - `cache_dir` can be an optional path to a specific directory to download and cache the pre-trained model weights. This option is useful in particular when you are using distributed training: to avoid concurrent access to the same weights you can set for example `cache_dir='./pretrained_model_{}'.format(args.local_rank)` (see the section on distributed training for more information) From 8a8aa59d8cad1b4e61b1bdffddb97f09260996d9 Mon Sep 17 00:00:00 2001 From: Davide Fiocco Date: Sat, 1 Dec 2018 01:00:05 +0100 Subject: [PATCH 018/111] Update finetuning example adding --do_lower_case Should be consistent with the fact that an uncased model is used --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index f105e0e35b96..8d5b60353ab9 100644 --- a/README.md +++ b/README.md @@ -377,6 +377,7 @@ python run_classifier.py \ --task_name MRPC \ --do_train \ --do_eval \ + --do_lower_case \ --data_dir $GLUE_DIR/MRPC/ \ --bert_model bert-base-uncased \ --max_seq_length 128 \ From dc13e276eec4ae8f316c82a190095380ad19e3c5 Mon Sep 17 00:00:00 2001 From: Davide Fiocco Date: Sat, 1 Dec 2018 01:02:16 +0100 Subject: [PATCH 019/111] Point typo fix --- examples/run_classifier.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index 23c2bea057ea..8e136da37b07 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -416,7 +416,7 @@ def main(): parser.add_argument('--gradient_accumulation_steps', type=int, default=1, - help="Number of updates steps to accumualte before performing a backward/update pass.") + help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument('--optimize_on_cpu', default=False, action='store_true', From e60e8a606837ff7f49e583de8492e55575155eb6 Mon Sep 17 00:00:00 2001 From: Davide Fiocco Date: Sun, 2 Dec 2018 12:38:26 +0100 Subject: [PATCH 020/111] Correct assignement for logits in classifier example I tried to address https://github.com/huggingface/pytorch-pretrained-BERT/issues/76 should be correct, but there's likely a more efficient way. --- examples/run_classifier.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index 8e136da37b07..a5e7d2c30df5 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -605,7 +605,8 @@ def main(): label_ids = label_ids.to(device) with torch.no_grad(): - tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask, label_ids) + tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids) + logits = model(input_ids, segment_ids, input_mask) logits = logits.detach().cpu().numpy() label_ids = label_ids.to('cpu').numpy() From 3113e967dbc37fee99524190615bf9466f5c8f1d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Tue, 4 Dec 2018 13:40:38 +0100 Subject: [PATCH 021/111] Adding links to examples files. --- README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 8d5b60353ab9..5f03f52b141b 100644 --- a/README.md +++ b/README.md @@ -207,8 +207,8 @@ model = BertForSequenceClassification.from_pretrained('bert-base-uncased') The inputs and output are **identical to the TensorFlow model inputs and outputs**. We detail them here. This model takes as *inputs*: - -- `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary (see the tokens preprocessing logic in the scripts `extract_features.py`, `run_classifier.py` and `run_squad.py`), and +[`modeling.py`](./pytorch_pretrained_bert/modeling.py) +- `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary (see the tokens preprocessing logic in the scripts [`extract_features.py`](./pytorch_pretrained_bert/examples/extract_features.py), [`run_classifier.py`](./pytorch_pretrained_bert/examples/run_classifier.py) and [`run_squad.py`](./pytorch_pretrained_bert/examples/run_squad.py)), and - `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details). - `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if some input sequence lengths are smaller than the max input sequence length of the current batch. It's the mask that we typically use for attention when a batch has varying length sentences. - `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`. @@ -222,7 +222,7 @@ This model *outputs* a tuple composed of: - `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a classifier pretrained on top of the hidden state associated to the first character of the input (`CLF`) to train on the Next-Sentence task (see BERT's paper). -An example on how to use this class is given in the `extract_features.py` script which can be used to extract the hidden states of the model for a given input. +An example on how to use this class is given in the [`extract_features.py`](./pytorch_pretrained_bert/examples/extract_features.py) script which can be used to extract the hidden states of the model for a given input. #### 2. `BertForPreTraining` @@ -276,7 +276,7 @@ An example on how to use this class is given in the `extract_features.py` script The sequence-level classifier is a linear layer that takes as input the last hidden state of the first character in the input sequence (see Figures 3a and 3b in the BERT paper). -An example on how to use this class is given in the `run_classifier.py` script which can be used to fine-tune a single sequence (or pair of sequence) classifier using BERT, for example for the MRPC task. +An example on how to use this class is given in the [`run_classifier.py`](./pytorch_pretrained_bert/examples/run_classifier.py) script which can be used to fine-tune a single sequence (or pair of sequence) classifier using BERT, for example for the MRPC task. #### 6. `BertForTokenClassification` @@ -290,7 +290,7 @@ The token-level classifier is a linear layer that takes as input the last hidden The token-level classifier takes as input the full sequence of the last hidden state and compute several (e.g. two) scores for each tokens that can for example respectively be the score that a given token is a `start_span` and a `end_span` token (see Figures 3c and 3d in the BERT paper). -An example on how to use this class is given in the `run_squad.py` script which can be used to fine-tune a token classifier using BERT, for example for the SQuAD task. +An example on how to use this class is given in the [`run_squad.py`](./pytorch_pretrained_bert/examples/run_squad.py) script which can be used to fine-tune a token classifier using BERT, for example for the SQuAD task. ### Tokenizer: `BertTokenizer` @@ -341,14 +341,14 @@ The optimizer accepts the following arguments: BERT-base and BERT-large are respectively 110M and 340M parameters models and it can be difficult to fine-tune them on a single GPU with the recommended batch size for good performance (in most case a batch size of 32). -To help with fine-tuning these models, we have included five techniques that you can activate in the fine-tuning scripts `run_classifier.py` and `run_squad.py`: gradient-accumulation, multi-gpu training, distributed training, optimize on CPU and 16-bits training . For more details on how to use these techniques you can read [the tips on training large batches in PyTorch](https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255) that I published earlier this month. +To help with fine-tuning these models, we have included five techniques that you can activate in the fine-tuning scripts [`run_classifier.py`](./pytorch_pretrained_bert/examples/run_classifier.py) and [`run_squad.py`](./pytorch_pretrained_bert/examples/run_squad.py): gradient-accumulation, multi-gpu training, distributed training, optimize on CPU and 16-bits training . For more details on how to use these techniques you can read [the tips on training large batches in PyTorch](https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255) that I published earlier this month. Here is how to use these techniques in our scripts: - **Gradient Accumulation**: Gradient accumulation can be used by supplying a integer greater than 1 to the `--gradient_accumulation_steps` argument. The batch at each step will be divided by this integer and gradient will be accumulated over `gradient_accumulation_steps` steps. - **Multi-GPU**: Multi-GPU is automatically activated when several GPUs are detected and the batches are splitted over the GPUs. - **Distributed training**: Distributed training can be activated by supplying an integer greater or equal to 0 to the `--local_rank` argument (see below). -- **Optimize on CPU**: The Adam optimizer stores 2 moving average of the weights of the model. If you keep them on GPU 1 (typical behavior), your first GPU will have to store 3-times the size of the model. This is not optimal for large models like `BERT-large` and means your batch size is a lot lower than it could be. This option will perform the optimization and store the averages on the CPU/RAM to free more room on the GPU(s). As the most computational intensive operation is usually the backward pass, this doesn't have a significant impact on the training time. Activate this option with `--optimize_on_cpu` on the `run_squad.py` script. +- **Optimize on CPU**: The Adam optimizer stores 2 moving average of the weights of the model. If you keep them on GPU 1 (typical behavior), your first GPU will have to store 3-times the size of the model. This is not optimal for large models like `BERT-large` and means your batch size is a lot lower than it could be. This option will perform the optimization and store the averages on the CPU/RAM to free more room on the GPU(s). As the most computational intensive operation is usually the backward pass, this doesn't have a significant impact on the training time. Activate this option with `--optimize_on_cpu` on the [`run_squad.py`](./pytorch_pretrained_bert/examples/run_squad.py) script. - **16-bits training**: 16-bits training, also called mixed-precision training, can reduce the memory requirement of your model on the GPU by using half-precision training, basically allowing to double the batch size. If you have a recent GPU (starting from NVIDIA Volta architecture) you should see no decrease in speed. A good introduction to Mixed precision training can be found [here](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) and a full documentation is [here](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html). In our scripts, this option can be activated by setting the `--fp16` flag and you can play with loss scaling using the `--loss_scaling` flag (see the previously linked documentation for details on loss scaling). If the loss scaling is too high (`Nan` in the gradients) it will be automatically scaled down until the value is acceptable. The default loss scaling is 128 which behaved nicely in our tests. Note: To use *Distributed Training*, you will need to run one training script on each of your machines. This can be done for example by running the following command on each server (see [the above mentioned blog post]((https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255)) for more details): @@ -493,7 +493,7 @@ A command-line interface is provided to convert a TensorFlow checkpoint in a PyT You can convert any TensorFlow checkpoint for BERT (in particular [the pre-trained models released by Google](https://github.com/google-research/bert#pre-trained-models)) in a PyTorch save file by using the [`./pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py`](convert_tf_checkpoint_to_pytorch.py) script. -This CLI takes as input a TensorFlow checkpoint (three files starting with `bert_model.ckpt`) and the associated configuration file (`bert_config.json`), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using `torch.load()` (see examples in `extract_features.py`, `run_classifier.py` and `run_squad.py`). +This CLI takes as input a TensorFlow checkpoint (three files starting with `bert_model.ckpt`) and the associated configuration file (`bert_config.json`), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using `torch.load()` (see examples in [`extract_features.py`](./pytorch_pretrained_bert/examples/extract_features.py), [`run_classifier.py`](./pytorch_pretrained_bert/examples/run_classifier.py) and [`run_squad.py`]((./pytorch_pretrained_bert/examples/run_squad.py))). You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow checkpoint (the three files starting with `bert_model.ckpt`) but be sure to keep the configuration file (`bert_config.json`) and the vocabulary file (`vocab.txt`) as these are needed for the PyTorch model too. From 0a7c8bdcacf2debae34977e478d00040faa83892 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Tue, 4 Dec 2018 13:43:56 +0100 Subject: [PATCH 022/111] Fixing badly formatted links. --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 5f03f52b141b..85adf9c7594e 100644 --- a/README.md +++ b/README.md @@ -208,7 +208,7 @@ The inputs and output are **identical to the TensorFlow model inputs and outputs We detail them here. This model takes as *inputs*: [`modeling.py`](./pytorch_pretrained_bert/modeling.py) -- `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary (see the tokens preprocessing logic in the scripts [`extract_features.py`](./pytorch_pretrained_bert/examples/extract_features.py), [`run_classifier.py`](./pytorch_pretrained_bert/examples/run_classifier.py) and [`run_squad.py`](./pytorch_pretrained_bert/examples/run_squad.py)), and +- `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary (see the tokens preprocessing logic in the scripts [`extract_features.py`](./examples/extract_features.py), [`run_classifier.py`](./examples/run_classifier.py) and [`run_squad.py`](./examples/run_squad.py)), and - `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details). - `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if some input sequence lengths are smaller than the max input sequence length of the current batch. It's the mask that we typically use for attention when a batch has varying length sentences. - `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`. @@ -222,7 +222,7 @@ This model *outputs* a tuple composed of: - `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a classifier pretrained on top of the hidden state associated to the first character of the input (`CLF`) to train on the Next-Sentence task (see BERT's paper). -An example on how to use this class is given in the [`extract_features.py`](./pytorch_pretrained_bert/examples/extract_features.py) script which can be used to extract the hidden states of the model for a given input. +An example on how to use this class is given in the [`extract_features.py`](./examples/extract_features.py) script which can be used to extract the hidden states of the model for a given input. #### 2. `BertForPreTraining` @@ -276,7 +276,7 @@ An example on how to use this class is given in the [`extract_features.py`](./py The sequence-level classifier is a linear layer that takes as input the last hidden state of the first character in the input sequence (see Figures 3a and 3b in the BERT paper). -An example on how to use this class is given in the [`run_classifier.py`](./pytorch_pretrained_bert/examples/run_classifier.py) script which can be used to fine-tune a single sequence (or pair of sequence) classifier using BERT, for example for the MRPC task. +An example on how to use this class is given in the [`run_classifier.py`](./examples/run_classifier.py) script which can be used to fine-tune a single sequence (or pair of sequence) classifier using BERT, for example for the MRPC task. #### 6. `BertForTokenClassification` @@ -290,7 +290,7 @@ The token-level classifier is a linear layer that takes as input the last hidden The token-level classifier takes as input the full sequence of the last hidden state and compute several (e.g. two) scores for each tokens that can for example respectively be the score that a given token is a `start_span` and a `end_span` token (see Figures 3c and 3d in the BERT paper). -An example on how to use this class is given in the [`run_squad.py`](./pytorch_pretrained_bert/examples/run_squad.py) script which can be used to fine-tune a token classifier using BERT, for example for the SQuAD task. +An example on how to use this class is given in the [`run_squad.py`](./examples/run_squad.py) script which can be used to fine-tune a token classifier using BERT, for example for the SQuAD task. ### Tokenizer: `BertTokenizer` @@ -341,14 +341,14 @@ The optimizer accepts the following arguments: BERT-base and BERT-large are respectively 110M and 340M parameters models and it can be difficult to fine-tune them on a single GPU with the recommended batch size for good performance (in most case a batch size of 32). -To help with fine-tuning these models, we have included five techniques that you can activate in the fine-tuning scripts [`run_classifier.py`](./pytorch_pretrained_bert/examples/run_classifier.py) and [`run_squad.py`](./pytorch_pretrained_bert/examples/run_squad.py): gradient-accumulation, multi-gpu training, distributed training, optimize on CPU and 16-bits training . For more details on how to use these techniques you can read [the tips on training large batches in PyTorch](https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255) that I published earlier this month. +To help with fine-tuning these models, we have included five techniques that you can activate in the fine-tuning scripts [`run_classifier.py`](./examples/run_classifier.py) and [`run_squad.py`](./examples/run_squad.py): gradient-accumulation, multi-gpu training, distributed training, optimize on CPU and 16-bits training . For more details on how to use these techniques you can read [the tips on training large batches in PyTorch](https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255) that I published earlier this month. Here is how to use these techniques in our scripts: - **Gradient Accumulation**: Gradient accumulation can be used by supplying a integer greater than 1 to the `--gradient_accumulation_steps` argument. The batch at each step will be divided by this integer and gradient will be accumulated over `gradient_accumulation_steps` steps. - **Multi-GPU**: Multi-GPU is automatically activated when several GPUs are detected and the batches are splitted over the GPUs. - **Distributed training**: Distributed training can be activated by supplying an integer greater or equal to 0 to the `--local_rank` argument (see below). -- **Optimize on CPU**: The Adam optimizer stores 2 moving average of the weights of the model. If you keep them on GPU 1 (typical behavior), your first GPU will have to store 3-times the size of the model. This is not optimal for large models like `BERT-large` and means your batch size is a lot lower than it could be. This option will perform the optimization and store the averages on the CPU/RAM to free more room on the GPU(s). As the most computational intensive operation is usually the backward pass, this doesn't have a significant impact on the training time. Activate this option with `--optimize_on_cpu` on the [`run_squad.py`](./pytorch_pretrained_bert/examples/run_squad.py) script. +- **Optimize on CPU**: The Adam optimizer stores 2 moving average of the weights of the model. If you keep them on GPU 1 (typical behavior), your first GPU will have to store 3-times the size of the model. This is not optimal for large models like `BERT-large` and means your batch size is a lot lower than it could be. This option will perform the optimization and store the averages on the CPU/RAM to free more room on the GPU(s). As the most computational intensive operation is usually the backward pass, this doesn't have a significant impact on the training time. Activate this option with `--optimize_on_cpu` on the [`run_squad.py`](./examples/run_squad.py) script. - **16-bits training**: 16-bits training, also called mixed-precision training, can reduce the memory requirement of your model on the GPU by using half-precision training, basically allowing to double the batch size. If you have a recent GPU (starting from NVIDIA Volta architecture) you should see no decrease in speed. A good introduction to Mixed precision training can be found [here](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) and a full documentation is [here](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html). In our scripts, this option can be activated by setting the `--fp16` flag and you can play with loss scaling using the `--loss_scaling` flag (see the previously linked documentation for details on loss scaling). If the loss scaling is too high (`Nan` in the gradients) it will be automatically scaled down until the value is acceptable. The default loss scaling is 128 which behaved nicely in our tests. Note: To use *Distributed Training*, you will need to run one training script on each of your machines. This can be done for example by running the following command on each server (see [the above mentioned blog post]((https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255)) for more details): @@ -493,7 +493,7 @@ A command-line interface is provided to convert a TensorFlow checkpoint in a PyT You can convert any TensorFlow checkpoint for BERT (in particular [the pre-trained models released by Google](https://github.com/google-research/bert#pre-trained-models)) in a PyTorch save file by using the [`./pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py`](convert_tf_checkpoint_to_pytorch.py) script. -This CLI takes as input a TensorFlow checkpoint (three files starting with `bert_model.ckpt`) and the associated configuration file (`bert_config.json`), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using `torch.load()` (see examples in [`extract_features.py`](./pytorch_pretrained_bert/examples/extract_features.py), [`run_classifier.py`](./pytorch_pretrained_bert/examples/run_classifier.py) and [`run_squad.py`]((./pytorch_pretrained_bert/examples/run_squad.py))). +This CLI takes as input a TensorFlow checkpoint (three files starting with `bert_model.ckpt`) and the associated configuration file (`bert_config.json`), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using `torch.load()` (see examples in [`extract_features.py`](./examples/extract_features.py), [`run_classifier.py`](./examples/run_classifier.py) and [`run_squad.py`]((./examples/run_squad.py))). You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow checkpoint (the three files starting with `bert_model.ckpt`) but be sure to keep the configuration file (`bert_config.json`) and the vocabulary file (`vocab.txt`) as these are needed for the PyTorch model too. From 793262e8ec8ebd3cee806b444cd9daafa6856317 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Wed, 5 Dec 2018 17:52:39 +0100 Subject: [PATCH 023/111] Removing trailing whitespaces. --- examples/run_classifier.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index a5e7d2c30df5..b2b8ac2630e0 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -35,7 +35,7 @@ from pytorch_pretrained_bert.optimization import BertAdam from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE -logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', +logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt = '%m/%d/%Y %H:%M:%S', level = logging.INFO) logger = logging.getLogger(__name__) @@ -409,14 +409,14 @@ def main(): type=int, default=-1, help="local_rank for distributed training on gpus") - parser.add_argument('--seed', - type=int, + parser.add_argument('--seed', + type=int, default=42, help="random seed for initialization") parser.add_argument('--gradient_accumulation_steps', type=int, default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.") + help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument('--optimize_on_cpu', default=False, action='store_true', @@ -487,7 +487,7 @@ def main(): len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) # Prepare model - model = BertForSequenceClassification.from_pretrained(args.bert_model, + model = BertForSequenceClassification.from_pretrained(args.bert_model, cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank)) if args.fp16: model.half() From c6d9d5394e6bf461f09e5f3e9b08e333961e590b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Wed, 5 Dec 2018 17:53:09 +0100 Subject: [PATCH 024/111] Simplifying code for easier understanding. --- examples/run_classifier.py | 34 ++++++++++------------------------ 1 file changed, 10 insertions(+), 24 deletions(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index b2b8ac2630e0..7cfa39dabfc3 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -196,9 +196,7 @@ def _create_examples(self, lines, set_type): def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer): """Loads a data file into a list of `InputBatch`s.""" - label_map = {} - for (i, label) in enumerate(label_list): - label_map[label] = i + label_map = {label : i for i, label in enumerate(label_list)} features = [] for (ex_index, example) in enumerate(examples): @@ -207,8 +205,6 @@ def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer tokens_b = None if example.text_b: tokens_b = tokenizer.tokenize(example.text_b) - - if tokens_b: # Modifies `tokens_a` and `tokens_b` in place so that the total # length is less than the specified length. # Account for [CLS], [SEP], [SEP] with "- 3" @@ -216,7 +212,7 @@ def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer else: # Account for [CLS] and [SEP] with "- 2" if len(tokens_a) > max_seq_length - 2: - tokens_a = tokens_a[0:(max_seq_length - 2)] + tokens_a = tokens_a[:(max_seq_length - 2)] # The convention in BERT is: # (a) For sequence pairs: @@ -236,22 +232,12 @@ def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer # For classification tasks, the first vector (corresponding to [CLS]) is # used as as the "sentence vector". Note that this only makes sense because # the entire model is fine-tuned. - tokens = [] - segment_ids = [] - tokens.append("[CLS]") - segment_ids.append(0) - for token in tokens_a: - tokens.append(token) - segment_ids.append(0) - tokens.append("[SEP]") - segment_ids.append(0) + tokens = ["[CLS]"] + tokens_a + ["[SEP]"] + segment_ids = [0] * len(tokens) if tokens_b: - for token in tokens_b: - tokens.append(token) - segment_ids.append(1) - tokens.append("[SEP]") - segment_ids.append(1) + tokens += tokens_b + ["[SEP]"] + segment_ids += [1] * (len(tokens_b) + 1) input_ids = tokenizer.convert_tokens_to_ids(tokens) @@ -260,10 +246,10 @@ def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer input_mask = [1] * len(input_ids) # Zero-pad up to the sequence length. - while len(input_ids) < max_seq_length: - input_ids.append(0) - input_mask.append(0) - segment_ids.append(0) + padding = [0] * (max_seq_length - len(input_ids)) + input_ids += padding + input_mask += padding + segment_ids += padding assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length From a994bf4076667d6885ee0596c35c90af297ad7b7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Wed, 5 Dec 2018 18:16:30 +0100 Subject: [PATCH 025/111] Fixing related to issue #83. --- examples/run_classifier.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index 7cfa39dabfc3..475ab54c9687 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -423,6 +423,12 @@ def main(): "mrpc": MrpcProcessor, } + num_labels_task = { + "cola": 2, + "mnli": 3, + "mrpc": 2, + } + if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() @@ -461,6 +467,7 @@ def main(): raise ValueError("Task not found: %s" % (task_name)) processor = processors[task_name]() + num_labels = num_labels_task[task_name] label_list = processor.get_labels() tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) @@ -474,7 +481,8 @@ def main(): # Prepare model model = BertForSequenceClassification.from_pretrained(args.bert_model, - cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank)) + cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank), + num_labels = num_labels) if args.fp16: model.half() model.to(device) From fa7daa247d6dde0e68af418beaf14d3e04dcd0b5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Thu, 6 Dec 2018 13:14:33 +0100 Subject: [PATCH 026/111] Fixing the commentary of the `SquadExample` class. --- examples/run_squad.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/run_squad.py b/examples/run_squad.py index 041a51a879fa..e47730043e9d 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -45,7 +45,7 @@ class SquadExample(object): - """A single training/test example for simple sequence classification.""" + """A single training/test example for the Squad dataset.""" def __init__(self, qas_id, From 7183cded4e10fd35ee1737d738c4a657db28ee1f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Thu, 6 Dec 2018 13:39:44 +0100 Subject: [PATCH 027/111] SwagExample class. --- examples/run_swag.py | 78 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 78 insertions(+) create mode 100644 examples/run_swag.py diff --git a/examples/run_swag.py b/examples/run_swag.py new file mode 100644 index 000000000000..37212b4e8641 --- /dev/null +++ b/examples/run_swag.py @@ -0,0 +1,78 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""BERT finetuning runner.""" + +class SwagExample(object): + """A single training/test example for the SWAG dataset.""" + def __init__(self, + swag_id, + context_sentence, + start_ending, + ending_0, + ending_1, + ending_2, + ending_3, + label = None): + self.swag_id = swag_id + self.context_sentence = context_sentence + self.start_ending = start_ending + self.ending_0 = ending_0 + self.ending_1 = ending_1 + self.ending_2 = ending_2 + self.ending_3 = ending_3 + self.label = label + + def __str__(self): + return self.__repr__() + + def __repr__(self): + l = [ + f'swag_id: {self.swag_id}', + f'context_sentence: {self.context_sentence}', + f'start_ending: {self.start_ending}', + f'ending_0: {self.ending_0}', + f'ending_1: {self.ending_1}', + f'ending_2: {self.ending_2}', + f'ending_3: {self.ending_3}', + ] + + if self.label is not None: + l.append(f'label: {self.label}') + + return ', '.join(l) + +if __name__ == "__main__": + e = SwagExample( + 3416, + 'Members of the procession walk down the street holding small horn brass instruments.', + 'A drum line', + 'passes by walking down the street playing their instruments.', + 'has heard approaching them.', + "arrives and they're outside dancing and asleep.", + 'turns the lead singer watches the performance.', + ) + print(e) + + e = SwagExample( + 3416, + 'Members of the procession walk down the street holding small horn brass instruments.', + 'A drum line', + 'passes by walking down the street playing their instruments.', + 'has heard approaching them.', + "arrives and they're outside dancing and asleep.", + 'turns the lead singer watches the performance.', + 0 + ) + print(e) From 83fdbd6043127bb7bf814bbe2e0dd65b05e60d95 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Thu, 6 Dec 2018 14:02:46 +0100 Subject: [PATCH 028/111] Adding read_swag_examples to load the dataset. --- examples/run_swag.py | 53 ++++++++++++++++++++++++++------------------ 1 file changed, 31 insertions(+), 22 deletions(-) diff --git a/examples/run_swag.py b/examples/run_swag.py index 37212b4e8641..9fa5bad0504e 100644 --- a/examples/run_swag.py +++ b/examples/run_swag.py @@ -14,6 +14,9 @@ # limitations under the License. """BERT finetuning runner.""" +import pandas as pd + + class SwagExample(object): """A single training/test example for the SWAG dataset.""" def __init__(self, @@ -53,26 +56,32 @@ def __repr__(self): return ', '.join(l) -if __name__ == "__main__": - e = SwagExample( - 3416, - 'Members of the procession walk down the street holding small horn brass instruments.', - 'A drum line', - 'passes by walking down the street playing their instruments.', - 'has heard approaching them.', - "arrives and they're outside dancing and asleep.", - 'turns the lead singer watches the performance.', - ) - print(e) +def read_swag_examples(input_file, is_training): + input_df = pd.read_csv(input_file) + + if is_training and 'label' not in input_df.columns: + raise ValueError( + "For training, the input file must contain a label column.") - e = SwagExample( - 3416, - 'Members of the procession walk down the street holding small horn brass instruments.', - 'A drum line', - 'passes by walking down the street playing their instruments.', - 'has heard approaching them.', - "arrives and they're outside dancing and asleep.", - 'turns the lead singer watches the performance.', - 0 - ) - print(e) + examples = [ + SwagExample( + swag_id = row['fold-ind'], + context_sentence = row['sent1'], + start_ending = row['sent2'], + ending_0 = row['ending0'], + ending_1 = row['ending1'], + ending_2 = row['ending2'], + ending_3 = row['ending3'], + label = row['label'] if is_training else None + ) for _, row in input_df.iterrows() + ] + + return examples + + +if __name__ == "__main__": + examples = read_swag_examples('data/train.csv', True) + print(len(examples)) + for example in examples[:5]: + print('###########################') + print(example) From f2b873e995e36732e41f2484b990b8109c239cd6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Thu, 6 Dec 2018 15:40:47 +0100 Subject: [PATCH 029/111] convert_examples_to_features code and small improvements. --- examples/run_swag.py | 154 ++++++++++++++++++++++++++++++++++++++----- 1 file changed, 138 insertions(+), 16 deletions(-) diff --git a/examples/run_swag.py b/examples/run_swag.py index 9fa5bad0504e..5a92f811b434 100644 --- a/examples/run_swag.py +++ b/examples/run_swag.py @@ -16,6 +16,15 @@ import pandas as pd +import logging + +from pytorch_pretrained_bert.tokenization import BertTokenizer + +logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', + datefmt = '%m/%d/%Y %H:%M:%S', + level = logging.INFO) +logger = logging.getLogger(__name__) + class SwagExample(object): """A single training/test example for the SWAG dataset.""" @@ -31,10 +40,12 @@ def __init__(self, self.swag_id = swag_id self.context_sentence = context_sentence self.start_ending = start_ending - self.ending_0 = ending_0 - self.ending_1 = ending_1 - self.ending_2 = ending_2 - self.ending_3 = ending_3 + self.endings = [ + ending_0, + ending_1, + ending_2, + ending_3, + ] self.label = label def __str__(self): @@ -42,19 +53,37 @@ def __str__(self): def __repr__(self): l = [ - f'swag_id: {self.swag_id}', - f'context_sentence: {self.context_sentence}', - f'start_ending: {self.start_ending}', - f'ending_0: {self.ending_0}', - f'ending_1: {self.ending_1}', - f'ending_2: {self.ending_2}', - f'ending_3: {self.ending_3}', + f"swag_id: {self.swag_id}", + f"context_sentence: {self.context_sentence}", + f"start_ending: {self.start_ending}", + f"ending_0: {self.endings[0]}", + f"ending_1: {self.endings[1]}", + f"ending_2: {self.endings[2]}", + f"ending_3: {self.endings[3]}", ] if self.label is not None: - l.append(f'label: {self.label}') + l.append(f"label: {self.label}") + + return ", ".join(l) + + +class InputFeatures(object): + def __init__(self, + unique_id, + example_id, + input_ids, + input_mask, + segment_ids, + label_id + ): + self.unique_id = unique_id + self.example_id = example_id + self.input_ids = input_ids + self.input_mask = input_mask + self.segment_ids = segment_ids + self.label_id = label_id - return ', '.join(l) def read_swag_examples(input_file, is_training): input_df = pd.read_csv(input_file) @@ -67,7 +96,9 @@ def read_swag_examples(input_file, is_training): SwagExample( swag_id = row['fold-ind'], context_sentence = row['sent1'], - start_ending = row['sent2'], + start_ending = row['sent2'], # in the swag dataset, the + # common beginning of each + # choice is stored in "sent2". ending_0 = row['ending0'], ending_1 = row['ending1'], ending_2 = row['ending2'], @@ -79,9 +110,100 @@ def read_swag_examples(input_file, is_training): return examples +def convert_examples_to_features(examples, tokenizer, max_seq_length, + is_training): + """Loads a data file into a list of `InputBatch`s.""" + + # Swag is a multiple choice task. To perform this task using Bert, + # we will use the formatting proposed in "Improving Language + # Understanding by Generative Pre-Training" and suggested by + # @jacobdevlin-google in this issue + # https://github.com/google-research/bert/issues/38. + # + # Each choice will correspond to a sample on which we run the + # inference. For a given Swag example, we will create the 4 + # following inputs: + # - [CLS] context [SEP] choice_1 [SEP] + # - [CLS] context [SEP] choice_2 [SEP] + # - [CLS] context [SEP] choice_3 [SEP] + # - [CLS] context [SEP] choice_4 [SEP] + # The model will output a single value for each input. To get the + # final decision of the model, we will run a softmax over these 4 + # outputs. + features = [] + for example_index, example in enumerate(examples): + context_tokens = tokenizer.tokenize(example.context_sentence) + start_ending_tokens = tokenizer.tokenize(example.start_ending) + + choices_features = [] + for ending_index, ending in enumerate(example.endings): + # We create a copy of the context tokens in order to be + # able to shrink it according to ending_tokens + context_tokens_choice = context_tokens[:] + ending_tokens = start_ending_tokens + tokenizer.tokenize(ending) + # Modifies `context_tokens_choice` and `ending_tokens` in + # place so that the total length is less than the + # specified length. Account for [CLS], [SEP], [SEP] with + # "- 3" + _truncate_seq_pair(context_tokens, ending_tokens, max_seq_length - 3) + + tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"] + segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1) + + input_ids = tokenizer.convert_tokens_to_ids(tokens) + input_mask = [1] * len(input_ids) + + # Zero-pad up to the sequence length. + padding = [0] * (max_seq_length - len(input_ids)) + input_ids += padding + input_mask += padding + segment_ids += padding + + assert len(input_ids) == max_seq_length + assert len(input_mask) == max_seq_length + assert len(segment_ids) == max_seq_length + + choices_features.append((tokens, input_ids, input_mask, segment_ids)) + + label = example.label + if example_index < 5: + logger.info("*** Example ***") + logger.info(f"swag_id: {example.swag_id}") + for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features): + logger.info(f"choice: {choice_idx}") + logger.info(f"tokens: {' '.join(tokens)}") + logger.info(f"input_ids: {' '.join(map(str, input_ids))}") + logger.info(f"input_mask: {' '.join(map(str, input_mask))}") + logger.info(f"segment_ids: {' '.join(map(str, segment_ids))}") + if is_training: + logger.info(f"label: {label}") + + + +def _truncate_seq_pair(tokens_a, tokens_b, max_length): + """Truncates a sequence pair in place to the maximum length.""" + + # This is a simple heuristic which will always truncate the longer sequence + # one token at a time. This makes more sense than truncating an equal percent + # of tokens from each, since if one sequence is very short then each token + # that's truncated likely contains more information than a longer sequence. + while True: + total_length = len(tokens_a) + len(tokens_b) + if total_length <= max_length: + break + if len(tokens_a) > len(tokens_b): + tokens_a.pop() + else: + tokens_b.pop() + + if __name__ == "__main__": - examples = read_swag_examples('data/train.csv', True) + is_training = True + max_seq_length = 80 + examples = read_swag_examples('data/train.csv', is_training) print(len(examples)) for example in examples[:5]: - print('###########################') + print("###########################") print(example) + tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") + convert_examples_to_features(examples, tokenizer, max_seq_length, is_training) From 0812aee2c3c4d9d364ea204ef2533cb166e5301d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Thu, 6 Dec 2018 15:53:07 +0100 Subject: [PATCH 030/111] Fixing problems in convert_examples_to_features. --- examples/run_swag.py | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/examples/run_swag.py b/examples/run_swag.py index 5a92f811b434..06169a3e9bdf 100644 --- a/examples/run_swag.py +++ b/examples/run_swag.py @@ -70,20 +70,13 @@ def __repr__(self): class InputFeatures(object): def __init__(self, - unique_id, example_id, - input_ids, - input_mask, - segment_ids, - label_id + choices_features, + label ): - self.unique_id = unique_id self.example_id = example_id - self.input_ids = input_ids - self.input_mask = input_mask - self.segment_ids = segment_ids - self.label_id = label_id - + self.choices_features = choices_features + self.label = label def read_swag_examples(input_file, is_training): input_df = pd.read_csv(input_file) @@ -145,7 +138,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length, # place so that the total length is less than the # specified length. Account for [CLS], [SEP], [SEP] with # "- 3" - _truncate_seq_pair(context_tokens, ending_tokens, max_seq_length - 3) + _truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3) tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"] segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1) @@ -178,7 +171,15 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length, if is_training: logger.info(f"label: {label}") + features.append( + InputFeatures( + example_id = example.swag_id, + choices_features = choices_features, + label = label + ) + ) + return features def _truncate_seq_pair(tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" @@ -206,4 +207,4 @@ def _truncate_seq_pair(tokens_a, tokens_b, max_length): print("###########################") print(example) tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") - convert_examples_to_features(examples, tokenizer, max_seq_length, is_training) + features = convert_examples_to_features(examples, tokenizer, max_seq_length, is_training) From c45d8ac55439decd059d697e21daf27e85ac3412 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Thu, 6 Dec 2018 16:01:28 +0100 Subject: [PATCH 031/111] Storing the feature of each choice as a dict for readability. --- examples/run_swag.py | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/examples/run_swag.py b/examples/run_swag.py index 06169a3e9bdf..f8494f3a1fe1 100644 --- a/examples/run_swag.py +++ b/examples/run_swag.py @@ -73,9 +73,17 @@ def __init__(self, example_id, choices_features, label + ): self.example_id = example_id - self.choices_features = choices_features + self.choices_features = [ + { + 'input_ids': input_ids, + 'input_mask': input_mask, + 'segment_ids': segment_ids + } + for _, input_ids, input_mask, segment_ids in choices_features + ] self.label = label def read_swag_examples(input_file, is_training): @@ -181,6 +189,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length, return features + def _truncate_seq_pair(tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" @@ -207,4 +216,11 @@ def _truncate_seq_pair(tokens_a, tokens_b, max_length): print("###########################") print(example) tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") - features = convert_examples_to_features(examples, tokenizer, max_seq_length, is_training) + features = convert_examples_to_features(examples[:500], tokenizer, max_seq_length, is_training) + for i in range(10): + choice_feature_list = features[i].choices_features + for choice_idx, choice_feature in enumerate(choice_feature_list): + print(f'choice_idx: {choice_idx}') + print(f'input_ids: {" ".join(map(str, choice_feature["input_ids"]))}') + print(f'input_mask: {" ".join(map(str, choice_feature["input_mask"]))}') + print(f'segment_ids: {" ".join(map(str, choice_feature["segment_ids"]))}') From fc5a38ac92c74577523665d8f8602c45ae8dcd8b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Thu, 6 Dec 2018 18:42:23 +0100 Subject: [PATCH 032/111] Adding the BertForMultipleChoiceClass. --- pytorch_pretrained_bert/__init__.py | 4 +- pytorch_pretrained_bert/modeling.py | 69 +++++++++++++++++++++++++++++ 2 files changed, 71 insertions(+), 2 deletions(-) diff --git a/pytorch_pretrained_bert/__init__.py b/pytorch_pretrained_bert/__init__.py index fc9b15a12d31..e1ecabf31dbd 100644 --- a/pytorch_pretrained_bert/__init__.py +++ b/pytorch_pretrained_bert/__init__.py @@ -1,7 +1,7 @@ from .tokenization import BertTokenizer, BasicTokenizer, WordpieceTokenizer from .modeling import (BertConfig, BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction, - BertForSequenceClassification, BertForTokenClassification, - BertForQuestionAnswering) + BertForSequenceClassification, BertForMultipleChoice, + BertForTokenClassification, BertForQuestionAnswering) from .optimization import BertAdam from .file_utils import PYTORCH_PRETRAINED_BERT_CACHE diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index 3af5854072fe..e23e2c1a1c42 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -877,6 +877,75 @@ def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=No return logits +class BertForMultipleChoice(PreTrainedBertModel): + """BERT model for multiple choice tasks. + This module is composed of the BERT model with a linear layer on top of + the pooled output. + + Params: + `config`: a BertConfig class instance with the configuration to build a new model. + `num_choices`: the number of classes for the classifier. Default = 2. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] + with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` + and type 1 corresponds to a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `labels`: labels for the classification output: torch.LongTensor of shape [batch_size] + with indices selected in [0, ..., num_choices]. + + Outputs: + if `labels` is not `None`: + Outputs the CrossEntropy classification loss of the output with the labels. + if `labels` is `None`: + Outputs the classification logits of shape [batch_size, num_labels]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]]) + input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]]) + token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]]) + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + num_choices = 2 + + model = BertForMultipleChoice(config, num_choices) + logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config, num_choices=2): + super(BertForMultipleChoice, self).__init__(config) + self.num_choices = num_choices + self.bert = BertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, 1) + self.apply(self.init_bert_weights) + + def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None): + flat_input_ids = input_ids.view(-1, input_ids.size(-1)) + flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) + flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) + _, pooled_output = self.bert(flat_input_ids, flat_token_type_ids, flat_attention_mask, output_all_encoded_layers=False) + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + reshaped_logits = logits.view(-1, self.num_choices) + + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels) + return loss + else: + return reshaped_logits + + class BertForTokenClassification(PreTrainedBertModel): """BERT model for token-level classification. This module is composed of the BERT model with a linear layer on top of From 63c45056aa2568a0bc0f8f6d97e6d90bbc4d4b4b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Thu, 6 Dec 2018 18:53:05 +0100 Subject: [PATCH 033/111] Finishing the code for the Swag task. --- examples/run_swag.py | 360 ++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 342 insertions(+), 18 deletions(-) diff --git a/examples/run_swag.py b/examples/run_swag.py index f8494f3a1fe1..8ebb506e4aad 100644 --- a/examples/run_swag.py +++ b/examples/run_swag.py @@ -17,8 +17,20 @@ import pandas as pd import logging +import os +import argparse +import random +from tqdm import tqdm, trange + +import numpy as np +import torch +from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler +from torch.utils.data.distributed import DistributedSampler from pytorch_pretrained_bert.tokenization import BertTokenizer +from pytorch_pretrained_bert.modeling import BertForMultipleChoice +from pytorch_pretrained_bert.optimization import BertAdam +from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt = '%m/%d/%Y %H:%M:%S', @@ -86,6 +98,7 @@ def __init__(self, ] self.label = label + def read_swag_examples(input_file, is_training): input_df = pd.read_csv(input_file) @@ -110,7 +123,6 @@ def read_swag_examples(input_file, is_training): return examples - def convert_examples_to_features(examples, tokenizer, max_seq_length, is_training): """Loads a data file into a list of `InputBatch`s.""" @@ -189,7 +201,6 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length, return features - def _truncate_seq_pair(tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" @@ -206,21 +217,334 @@ def _truncate_seq_pair(tokens_a, tokens_b, max_length): else: tokens_b.pop() +def accuracy(out, labels): + outputs = np.argmax(out, axis=1) + return np.sum(outputs == labels) + +def select_field(features, field): + return [ + [ + choice[field] + for choice in feature.choices_features + ] + for feature in features + ] + +def copy_optimizer_params_to_model(named_params_model, named_params_optimizer): + """ Utility function for optimize_on_cpu and 16-bits training. + Copy the parameters optimized on CPU/RAM back to the model on GPU + """ + for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model): + if name_opti != name_model: + logger.error("name_opti != name_model: {} {}".format(name_opti, name_model)) + raise ValueError + param_model.data.copy_(param_opti.data) + +def set_optimizer_params_grad(named_params_optimizer, named_params_model, test_nan=False): + """ Utility function for optimize_on_cpu and 16-bits training. + Copy the gradient of the GPU parameters to the CPU/RAMM copy of the model + """ + is_nan = False + for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model): + if name_opti != name_model: + logger.error("name_opti != name_model: {} {}".format(name_opti, name_model)) + raise ValueError + if param_model.grad is not None: + if test_nan and torch.isnan(param_model.grad).sum() > 0: + is_nan = True + if param_opti.grad is None: + param_opti.grad = torch.nn.Parameter(param_opti.data.new().resize_(*param_opti.data.size())) + param_opti.grad.data.copy_(param_model.grad.data) + else: + param_opti.grad = None + return is_nan + +def main(): + parser = argparse.ArgumentParser() + + ## Required parameters + parser.add_argument("--data_dir", + default=None, + type=str, + required=True, + help="The input data dir. Should contain the .csv files (or other data files) for the task.") + parser.add_argument("--bert_model", default=None, type=str, required=True, + help="Bert pre-trained model selected in the list: bert-base-uncased, " + "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.") + parser.add_argument("--output_dir", + default=None, + type=str, + required=True, + help="The output directory where the model checkpoints will be written.") + + ## Other parameters + parser.add_argument("--max_seq_length", + default=128, + type=int, + help="The maximum total input sequence length after WordPiece tokenization. \n" + "Sequences longer than this will be truncated, and sequences shorter \n" + "than this will be padded.") + parser.add_argument("--do_train", + default=False, + action='store_true', + help="Whether to run training.") + parser.add_argument("--do_eval", + default=False, + action='store_true', + help="Whether to run eval on the dev set.") + parser.add_argument("--do_lower_case", + default=False, + action='store_true', + help="Set this flag if you are using an uncased model.") + parser.add_argument("--train_batch_size", + default=32, + type=int, + help="Total batch size for training.") + parser.add_argument("--eval_batch_size", + default=8, + type=int, + help="Total batch size for eval.") + parser.add_argument("--learning_rate", + default=5e-5, + type=float, + help="The initial learning rate for Adam.") + parser.add_argument("--num_train_epochs", + default=3.0, + type=float, + help="Total number of training epochs to perform.") + parser.add_argument("--warmup_proportion", + default=0.1, + type=float, + help="Proportion of training to perform linear learning rate warmup for. " + "E.g., 0.1 = 10%% of training.") + parser.add_argument("--no_cuda", + default=False, + action='store_true', + help="Whether not to use CUDA when available") + parser.add_argument("--local_rank", + type=int, + default=-1, + help="local_rank for distributed training on gpus") + parser.add_argument('--seed', + type=int, + default=42, + help="random seed for initialization") + parser.add_argument('--gradient_accumulation_steps', + type=int, + default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.") + parser.add_argument('--optimize_on_cpu', + default=False, + action='store_true', + help="Whether to perform optimization and keep the optimizer averages on CPU") + parser.add_argument('--fp16', + default=False, + action='store_true', + help="Whether to use 16-bit float precision instead of 32-bit") + parser.add_argument('--loss_scale', + type=float, default=128, + help='Loss scaling, positive power of 2 values can improve fp16 convergence.') + + args = parser.parse_args() + + if args.local_rank == -1 or args.no_cuda: + device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") + n_gpu = torch.cuda.device_count() + else: + device = torch.device("cuda", args.local_rank) + n_gpu = 1 + # Initializes the distributed backend which will take care of sychronizing nodes/GPUs + torch.distributed.init_process_group(backend='nccl') + if args.fp16: + logger.info("16-bits training currently not supported in distributed training") + args.fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496) + logger.info("device %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1)) + + if args.gradient_accumulation_steps < 1: + raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format( + args.gradient_accumulation_steps)) + + args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps) + + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if n_gpu > 0: + torch.cuda.manual_seed_all(args.seed) + + if not args.do_train and not args.do_eval: + raise ValueError("At least one of `do_train` or `do_eval` must be True.") + + if os.path.exists(args.output_dir) and os.listdir(args.output_dir): + raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) + os.makedirs(args.output_dir, exist_ok=True) + + # task_name = args.task_name.lower() + + # if task_name not in processors: + # raise ValueError("Task not found: %s" % (task_name)) + + # processor = processors[task_name]() + # label_list = processor.get_labels() + + tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) + + train_examples = None + num_train_steps = None + if args.do_train: + train_examples = read_swag_examples(os.path.join(args.data_dir, 'train.csv'), is_training = True) + num_train_steps = int( + len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) + + # Prepare model + model = BertForMultipleChoice.from_pretrained(args.bert_model, + cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank), + num_choices = 4 + ) + if args.fp16: + model.half() + model.to(device) + if args.local_rank != -1: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], + output_device=args.local_rank) + elif n_gpu > 1: + model = torch.nn.DataParallel(model) + + # Prepare optimizer + if args.fp16: + param_optimizer = [(n, param.clone().detach().to('cpu').float().requires_grad_()) \ + for n, param in model.named_parameters()] + elif args.optimize_on_cpu: + param_optimizer = [(n, param.clone().detach().to('cpu').requires_grad_()) \ + for n, param in model.named_parameters()] + else: + param_optimizer = list(model.named_parameters()) + no_decay = ['bias', 'gamma', 'beta'] + optimizer_grouped_parameters = [ + {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01}, + {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0} + ] + t_total = num_train_steps + if args.local_rank != -1: + t_total = t_total // torch.distributed.get_world_size() + optimizer = BertAdam(optimizer_grouped_parameters, + lr=args.learning_rate, + warmup=args.warmup_proportion, + t_total=t_total) + + global_step = 0 + if args.do_train: + train_features = convert_examples_to_features( + train_examples, tokenizer, args.max_seq_length, True) + logger.info("***** Running training *****") + logger.info(" Num examples = %d", len(train_examples)) + logger.info(" Batch size = %d", args.train_batch_size) + logger.info(" Num steps = %d", num_train_steps) + all_input_ids = torch.tensor(select_field(train_features, 'input_ids'), dtype=torch.long) + all_input_mask = torch.tensor(select_field(train_features, 'input_mask'), dtype=torch.long) + all_segment_ids = torch.tensor(select_field(train_features, 'segment_ids'), dtype=torch.long) + all_label = torch.tensor([f.label for f in train_features], dtype=torch.long) + train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label) + if args.local_rank == -1: + train_sampler = RandomSampler(train_data) + else: + train_sampler = DistributedSampler(train_data) + train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) + + model.train() + for _ in trange(int(args.num_train_epochs), desc="Epoch"): + tr_loss = 0 + nb_tr_examples, nb_tr_steps = 0, 0 + for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): + batch = tuple(t.to(device) for t in batch) + input_ids, input_mask, segment_ids, label_ids = batch + loss = model(input_ids, segment_ids, input_mask, label_ids) + if n_gpu > 1: + loss = loss.mean() # mean() to average on multi-gpu. + if args.fp16 and args.loss_scale != 1.0: + # rescale loss for fp16 training + # see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html + loss = loss * args.loss_scale + if args.gradient_accumulation_steps > 1: + loss = loss / args.gradient_accumulation_steps + loss.backward() + tr_loss += loss.item() + nb_tr_examples += input_ids.size(0) + nb_tr_steps += 1 + if (step + 1) % args.gradient_accumulation_steps == 0: + if args.fp16 or args.optimize_on_cpu: + if args.fp16 and args.loss_scale != 1.0: + # scale down gradients for fp16 training + for param in model.parameters(): + if param.grad is not None: + param.grad.data = param.grad.data / args.loss_scale + is_nan = set_optimizer_params_grad(param_optimizer, model.named_parameters(), test_nan=True) + if is_nan: + logger.info("FP16 TRAINING: Nan in gradients, reducing loss scaling") + args.loss_scale = args.loss_scale / 2 + model.zero_grad() + continue + optimizer.step() + copy_optimizer_params_to_model(model.named_parameters(), param_optimizer) + else: + optimizer.step() + model.zero_grad() + global_step += 1 + + if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): + eval_examples = read_swag_examples(os.path.join(args.data_dir, 'val.csv'), is_training = True) + eval_features = convert_examples_to_features( + eval_examples, tokenizer, args.max_seq_length, True) + logger.info("***** Running evaluation *****") + logger.info(" Num examples = %d", len(eval_examples)) + logger.info(" Batch size = %d", args.eval_batch_size) + all_input_ids = torch.tensor(select_field(eval_features, 'input_ids'), dtype=torch.long) + all_input_mask = torch.tensor(select_field(eval_features, 'input_mask'), dtype=torch.long) + all_segment_ids = torch.tensor(select_field(eval_features, 'segment_ids'), dtype=torch.long) + all_label = torch.tensor([f.label for f in eval_features], dtype=torch.long) + eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label) + # Run prediction for full data + eval_sampler = SequentialSampler(eval_data) + eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) + + model.eval() + eval_loss, eval_accuracy = 0, 0 + nb_eval_steps, nb_eval_examples = 0, 0 + for input_ids, input_mask, segment_ids, label_ids in eval_dataloader: + input_ids = input_ids.to(device) + input_mask = input_mask.to(device) + segment_ids = segment_ids.to(device) + label_ids = label_ids.to(device) + + with torch.no_grad(): + tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids) + logits = model(input_ids, segment_ids, input_mask) + + logits = logits.detach().cpu().numpy() + label_ids = label_ids.to('cpu').numpy() + tmp_eval_accuracy = accuracy(logits, label_ids) + + eval_loss += tmp_eval_loss.mean().item() + eval_accuracy += tmp_eval_accuracy + + nb_eval_examples += input_ids.size(0) + nb_eval_steps += 1 + + eval_loss = eval_loss / nb_eval_steps + eval_accuracy = eval_accuracy / nb_eval_examples + + result = {'eval_loss': eval_loss, + 'eval_accuracy': eval_accuracy, + 'global_step': global_step, + 'loss': tr_loss/nb_tr_steps} + + output_eval_file = os.path.join(args.output_dir, "eval_results.txt") + with open(output_eval_file, "w") as writer: + logger.info("***** Eval results *****") + for key in sorted(result.keys()): + logger.info(" %s = %s", key, str(result[key])) + writer.write("%s = %s\n" % (key, str(result[key]))) + if __name__ == "__main__": - is_training = True - max_seq_length = 80 - examples = read_swag_examples('data/train.csv', is_training) - print(len(examples)) - for example in examples[:5]: - print("###########################") - print(example) - tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") - features = convert_examples_to_features(examples[:500], tokenizer, max_seq_length, is_training) - for i in range(10): - choice_feature_list = features[i].choices_features - for choice_idx, choice_feature in enumerate(choice_feature_list): - print(f'choice_idx: {choice_idx}') - print(f'input_ids: {" ".join(map(str, choice_feature["input_ids"]))}') - print(f'input_mask: {" ".join(map(str, choice_feature["input_mask"]))}') - print(f'segment_ids: {" ".join(map(str, choice_feature["segment_ids"]))}') + main() From 6a26e19ea3fe797eca051db140cdaafc154b1873 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Thu, 6 Dec 2018 19:15:08 +0100 Subject: [PATCH 034/111] Updating README.md with SWAG example informations. --- README.md | 37 +++++++++++++++++++++++++++++++++---- 1 file changed, 33 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 85adf9c7594e..c77fe84d51a5 100644 --- a/README.md +++ b/README.md @@ -52,8 +52,9 @@ This package comprises the following classes that can be imported in Python and - [`BertForNextSentencePrediction`](./pytorch_pretrained_bert/modeling.py#L752) - BERT Transformer with the pre-trained next sentence prediction classifier on top (**fully pre-trained**), - [`BertForPreTraining`](./pytorch_pretrained_bert/modeling.py#L620) - BERT Transformer with masked language modeling head and next sentence prediction classifier on top (**fully pre-trained**), - [`BertForSequenceClassification`](./pytorch_pretrained_bert/modeling.py#L814) - BERT Transformer with a sequence classification head on top (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**), - - [`BertForTokenClassification`](./pytorch_pretrained_bert/modeling.py#L880) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**), - - [`BertForQuestionAnswering`](./pytorch_pretrained_bert/modeling.py#L946) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**). + - [`BertForMultipleChoice`](./pytorch_pretrained_bert/modeling.py#L893) - BERT Transformer with a multiple choice head on top (used for task like Swag) (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**), + - [`BertForTokenClassification`](./pytorch_pretrained_bert/modeling.py#L949) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**), + - [`BertForQuestionAnswering`](./pytorch_pretrained_bert/modeling.py#L1102) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**). - Three tokenizers (in the [`tokenization.py`](./pytorch_pretrained_bert/tokenization.py) file): - `BasicTokenizer` - basic tokenization (punctuation splitting, lower casing, etc.), @@ -72,6 +73,7 @@ The repository further comprises: - [`extract_features.py`](./examples/extract_features.py) - Show how to extract hidden states from an instance of `BertModel`, - [`run_classifier.py`](./examples/run_classifier.py) - Show how to fine-tune an instance of `BertForSequenceClassification` on GLUE's MRPC task, - [`run_squad.py`](./examples/run_squad.py) - Show how to fine-tune an instance of `BertForQuestionAnswering` on SQuAD v1.0 task. + - [`run_swag.py`](./examples/run_swag.py) - Show how to fine-tune an instance of `BertForMultipleChoice` on Swag task. These examples are detailed in the [Examples](#examples) section of this readme. @@ -278,13 +280,23 @@ The sequence-level classifier is a linear layer that takes as input the last hid An example on how to use this class is given in the [`run_classifier.py`](./examples/run_classifier.py) script which can be used to fine-tune a single sequence (or pair of sequence) classifier using BERT, for example for the MRPC task. -#### 6. `BertForTokenClassification` +#### 6. `BertForMultipleChoice` + +`BertForMultipleChoice` is a fine-tuning model that includes `BertModel` and a linear layer on top of the `BertModel`. + +The linear layer outputs a single value for each choice of a multiple choice problem, then all the output corresponding to an instance are passed through a softmax to get the model choice. + +This implementation is largely inspired by the work of OpenAI in [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) and the answer of Jacob Devlin in the following [issue](https://github.com/google-research/bert/issues/38). + +An example on how to use this class is given in the [`run_swag.py`](./examples/run_swag.py) script which can be used to fine-tune a multiple choice classifier using BERT, for example for the Swag task. + +#### 7. `BertForTokenClassification` `BertForTokenClassification` is a fine-tuning model that includes `BertModel` and a token-level classifier on top of the `BertModel`. The token-level classifier is a linear layer that takes as input the last hidden state of the sequence. -#### 7. `BertForQuestionAnswering` +#### 8. `BertForQuestionAnswering` `BertForQuestionAnswering` is a fine-tuning model that includes `BertModel` with a token-level classifiers on top of the full sequence of last hidden states. @@ -419,6 +431,23 @@ Training with the previous hyper-parameters gave us the following results: {"f1": 88.52381567990474, "exact_match": 81.22043519394512} ``` +The data for Swag can be downloaded by cloning the following [repository](https://github.com/rowanz/swagaf) + +```shell +export SWAG_DIR=/path/to/SWAG + +python run_swag.py \ + --bert_model bert-base-uncased \ + --do_train \ + --do_eval \ + --data_dir $SWAG_DIR/data + --train_batch_size 10 \ + --learning_rate 2e-5 \ + --num_train_epochs 3.0 \ + --max_seq_length 80 \ + --output_dir /tmp/swag_output/ +``` + ## Fine-tuning BERT-large on GPUs The options we list above allow to fine-tune BERT-large rather easily on GPU(s) instead of the TPU used by the original implementation. From 4fa7892d640c2244ff9b888f890344caa14d4eac Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Thu, 6 Dec 2018 19:18:29 +0100 Subject: [PATCH 035/111] Wrong line number link to modeling file. --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index c77fe84d51a5..b9d57bcb3fbb 100644 --- a/README.md +++ b/README.md @@ -52,9 +52,9 @@ This package comprises the following classes that can be imported in Python and - [`BertForNextSentencePrediction`](./pytorch_pretrained_bert/modeling.py#L752) - BERT Transformer with the pre-trained next sentence prediction classifier on top (**fully pre-trained**), - [`BertForPreTraining`](./pytorch_pretrained_bert/modeling.py#L620) - BERT Transformer with masked language modeling head and next sentence prediction classifier on top (**fully pre-trained**), - [`BertForSequenceClassification`](./pytorch_pretrained_bert/modeling.py#L814) - BERT Transformer with a sequence classification head on top (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**), - - [`BertForMultipleChoice`](./pytorch_pretrained_bert/modeling.py#L893) - BERT Transformer with a multiple choice head on top (used for task like Swag) (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**), + - [`BertForMultipleChoice`](./pytorch_pretrained_bert/modeling.py#L880) - BERT Transformer with a multiple choice head on top (used for task like Swag) (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**), - [`BertForTokenClassification`](./pytorch_pretrained_bert/modeling.py#L949) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**), - - [`BertForQuestionAnswering`](./pytorch_pretrained_bert/modeling.py#L1102) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**). + - [`BertForQuestionAnswering`](./pytorch_pretrained_bert/modeling.py#L1015) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**). - Three tokenizers (in the [`tokenization.py`](./pytorch_pretrained_bert/tokenization.py) file): - `BasicTokenizer` - basic tokenization (punctuation splitting, lower casing, etc.), From d429c15f251ba86a55f726fa1ba98e78b42fd38c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Thu, 6 Dec 2018 19:19:21 +0100 Subject: [PATCH 036/111] Removing old code from copy-paste. --- examples/run_swag.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/examples/run_swag.py b/examples/run_swag.py index 8ebb506e4aad..201317766fe8 100644 --- a/examples/run_swag.py +++ b/examples/run_swag.py @@ -379,14 +379,6 @@ def main(): raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) os.makedirs(args.output_dir, exist_ok=True) - # task_name = args.task_name.lower() - - # if task_name not in processors: - # raise ValueError("Task not found: %s" % (task_name)) - - # processor = processors[task_name]() - # label_list = processor.get_labels() - tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) train_examples = None From 150f3cd9fa9a360eaf1bbc9178a5b894d899e74b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Thu, 6 Dec 2018 19:22:07 +0100 Subject: [PATCH 037/111] Few typos in README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b9d57bcb3fbb..d443ba7a07f7 100644 --- a/README.md +++ b/README.md @@ -69,7 +69,7 @@ This package comprises the following classes that can be imported in Python and The repository further comprises: -- Three examples on how to use Bert (in the [`examples` folder](./examples)): +- Four examples on how to use Bert (in the [`examples` folder](./examples)): - [`extract_features.py`](./examples/extract_features.py) - Show how to extract hidden states from an instance of `BertModel`, - [`run_classifier.py`](./examples/run_classifier.py) - Show how to fine-tune an instance of `BertForSequenceClassification` on GLUE's MRPC task, - [`run_squad.py`](./examples/run_squad.py) - Show how to fine-tune an instance of `BertForQuestionAnswering` on SQuAD v1.0 task. @@ -284,7 +284,7 @@ An example on how to use this class is given in the [`run_classifier.py`](./exam `BertForMultipleChoice` is a fine-tuning model that includes `BertModel` and a linear layer on top of the `BertModel`. -The linear layer outputs a single value for each choice of a multiple choice problem, then all the output corresponding to an instance are passed through a softmax to get the model choice. +The linear layer outputs a single value for each choice of a multiple choice problem, then all the outputs corresponding to an instance are passed through a softmax to get the model choice. This implementation is largely inspired by the work of OpenAI in [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) and the answer of Jacob Devlin in the following [issue](https://github.com/google-research/bert/issues/38). From c9f67e037cee0658b2f8a4118392dcf7994c93a9 Mon Sep 17 00:00:00 2001 From: Davide Fiocco Date: Fri, 7 Dec 2018 20:40:56 +0100 Subject: [PATCH 038/111] Adding --do_lower_case for all uncased BERTs I had missed those, it should make sense to use them --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 85adf9c7594e..897d884ec8af 100644 --- a/README.md +++ b/README.md @@ -404,6 +404,7 @@ python run_squad.py \ --bert_model bert-base-uncased \ --do_train \ --do_predict \ + --do_lower_case \ --train_file $SQUAD_DIR/train-v1.1.json \ --predict_file $SQUAD_DIR/dev-v1.1.json \ --train_batch_size 12 \ @@ -438,6 +439,7 @@ python ./run_squad.py \ --bert_model bert-large-uncased \ --do_train \ --do_predict \ + --do_lower_case \ --train_file $SQUAD_TRAIN \ --predict_file $SQUAD_EVAL \ --learning_rate 3e-5 \ @@ -458,6 +460,7 @@ python ./run_squad.py \ --bert_model bert-large-uncased \ --do_train \ --do_predict \ + --do_lower_case \ --train_file $SQUAD_TRAIN \ --predict_file $SQUAD_EVAL \ --learning_rate 3e-5 \ From 68f77303b294385239c0b356948c93d68ea09715 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Sun, 9 Dec 2018 16:17:11 -0500 Subject: [PATCH 039/111] fixing Adam weights skip in TF convert script --- pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py b/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py index 20fdd8c0d6e8..79b5f41adcf4 100755 --- a/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py +++ b/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py @@ -50,7 +50,7 @@ def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytor name = name.split('/') # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model - if name[-1] in ["adam_v", "adam_m"]: + if any(n in ["adam_v", "adam_m"] for n in name): print("Skipping {}".format("/".join(name))) continue pointer = model From 1db916b5be1ee281fe08780e07fa24e2d9471c92 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Sun, 9 Dec 2018 16:57:51 -0500 Subject: [PATCH 040/111] compatibility PT 1.0 and 0.4.1 --- tests/optimization_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/optimization_test.py b/tests/optimization_test.py index 1c010750ae1f..184637359156 100644 --- a/tests/optimization_test.py +++ b/tests/optimization_test.py @@ -32,7 +32,7 @@ def assertListAlmostEqual(self, list1, list2, tol): def test_adam(self): w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True) target = torch.tensor([0.4, 0.2, -0.5]) - criterion = torch.nn.MSELoss(reduction='elementwise_mean') + criterion = torch.nn.MSELoss() # No warmup, constant schedule, no gradient clipping optimizer = BertAdam(params=[w], lr=2e-1, weight_decay_rate=0.0, From 174cdbccde6601884cf0a25d2902c6ba31130de4 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Sun, 9 Dec 2018 17:04:23 -0500 Subject: [PATCH 041/111] adding save checkpoint and loading in examples --- examples/run_classifier.py | 6 +++++- examples/run_squad.py | 2 +- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index a5e7d2c30df5..3d13ee463fd9 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -359,7 +359,7 @@ def main(): default=None, type=str, required=True, - help="The output directory where the model checkpoints will be written.") + help="The output directory where the model predictions and checkpoints will be written.") ## Other parameters parser.add_argument("--max_seq_length", @@ -626,6 +626,10 @@ def main(): 'global_step': global_step, 'loss': tr_loss/nb_tr_steps} + model_to_save = model.module if hasattr(model, 'module') else model + raise NotImplementedError # TODO add save of the configuration file and vocabulary file also ? + output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") + torch.save(model_to_save, output_model_file) output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") diff --git a/examples/run_squad.py b/examples/run_squad.py index e47730043e9d..cd0a9a302837 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -706,7 +706,7 @@ def main(): help="Bert pre-trained model selected in the list: bert-base-uncased, " "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.") parser.add_argument("--output_dir", default=None, type=str, required=True, - help="The output directory where the model checkpoints will be written.") + help="The output directory where the model checkpoints and predictions will be written.") ## Other parameters parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json") From 81e1e2489f71e9f0250034492b0d52616a450c77 Mon Sep 17 00:00:00 2001 From: Li Li Date: Mon, 10 Dec 2018 02:08:38 -0800 Subject: [PATCH 042/111] Fix optimizer to work with horovod --- pytorch_pretrained_bert/optimization.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pytorch_pretrained_bert/optimization.py b/pytorch_pretrained_bert/optimization.py index 4266a8f83ba6..4314c84144cb 100644 --- a/pytorch_pretrained_bert/optimization.py +++ b/pytorch_pretrained_bert/optimization.py @@ -17,6 +17,7 @@ import math import torch from torch.optim import Optimizer +from torch.optim.optimizer import required from torch.nn.utils import clip_grad_norm_ def warmup_cosine(x, warmup=0.002): @@ -55,10 +56,10 @@ class BertAdam(Optimizer): weight_decay_rate: Weight decay. Default: 0.01 max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0 """ - def __init__(self, params, lr, warmup=-1, t_total=-1, schedule='warmup_linear', + def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear', b1=0.9, b2=0.999, e=1e-6, weight_decay_rate=0.01, max_grad_norm=1.0): - if not lr >= 0.0: + if lr is not required and lr < 0.0: raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr)) if schedule not in SCHEDULES: raise ValueError("Invalid schedule parameter: {}".format(schedule)) From 0876b77f7fbda110d5e64c03880e34123f2cea88 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Mon, 10 Dec 2018 15:34:19 +0100 Subject: [PATCH 043/111] Change to the README file to add SWAG results. --- README.md | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d443ba7a07f7..23cd315c298c 100644 --- a/README.md +++ b/README.md @@ -441,13 +441,25 @@ python run_swag.py \ --do_train \ --do_eval \ --data_dir $SWAG_DIR/data - --train_batch_size 10 \ + --train_batch_size 4 \ --learning_rate 2e-5 \ --num_train_epochs 3.0 \ --max_seq_length 80 \ --output_dir /tmp/swag_output/ ``` +Training with the previous hyper-parameters gave us the following results: +``` +eval_accuracy = 0.7776167149855043 +eval_loss = 1.006812262735175 +global_step = 55161 +loss = 0.282251750624779 +``` + +The difference with the `81.6%` accuracy announced in the Bert article +is probably due to the different `training_batch_size` (here 4 and 16 +in the article). + ## Fine-tuning BERT-large on GPUs The options we list above allow to fine-tune BERT-large rather easily on GPU(s) instead of the TPU used by the original implementation. From df34f22854a5174f5ad941c72255098e1b47e1bd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Mon, 10 Dec 2018 17:45:23 +0100 Subject: [PATCH 044/111] Removing the dependency to pandas and using the csv module to load data. --- examples/run_swag.py | 30 ++++++++++++++++-------------- 1 file changed, 16 insertions(+), 14 deletions(-) diff --git a/examples/run_swag.py b/examples/run_swag.py index 201317766fe8..88297bf80107 100644 --- a/examples/run_swag.py +++ b/examples/run_swag.py @@ -14,13 +14,12 @@ # limitations under the License. """BERT finetuning runner.""" -import pandas as pd - import logging import os import argparse import random from tqdm import tqdm, trange +import csv import numpy as np import torch @@ -100,25 +99,28 @@ def __init__(self, def read_swag_examples(input_file, is_training): - input_df = pd.read_csv(input_file) + with open(input_file, 'r') as f: + reader = csv.reader(f) + lines = list(reader) - if is_training and 'label' not in input_df.columns: + if is_training and lines[0][-1] != 'label': raise ValueError( - "For training, the input file must contain a label column.") + "For training, the input file must contain a label column." + ) examples = [ SwagExample( - swag_id = row['fold-ind'], - context_sentence = row['sent1'], - start_ending = row['sent2'], # in the swag dataset, the + swag_id = line[2], + context_sentence = line[4], + start_ending = line[5], # in the swag dataset, the # common beginning of each # choice is stored in "sent2". - ending_0 = row['ending0'], - ending_1 = row['ending1'], - ending_2 = row['ending2'], - ending_3 = row['ending3'], - label = row['label'] if is_training else None - ) for _, row in input_df.iterrows() + ending_0 = line[7], + ending_1 = line[8], + ending_2 = line[9], + ending_3 = line[10], + label = int(line[11]) if is_training else None + ) for line in lines[1:] # we skip the line with the column names ] return examples From a3a3180c86f63ee7af9d5a2a6ce1c05a8a3385b4 Mon Sep 17 00:00:00 2001 From: Thomas Wolf Date: Tue, 11 Dec 2018 11:29:45 +0100 Subject: [PATCH 045/111] Bump up requirements to Python 3.6 --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 897d884ec8af..720ddc347813 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ This implementation is provided with [Google's pre-trained models](https://githu ## Installation -This repo was tested on Python 3.5+ and PyTorch 0.4.1 +This repo was tested on Python 3.6+ and PyTorch 0.4.1 ### With pip From 270fa2f20b6dd9736a08f24e6050f24b2a96b010 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Tue, 11 Dec 2018 11:50:38 +0100 Subject: [PATCH 046/111] add pretrained loading from state_dict --- pytorch_pretrained_bert/modeling.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index 3af5854072fe..3d04f0842cc6 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -445,9 +445,9 @@ def init_bert_weights(self, module): module.bias.data.zero_() @classmethod - def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwargs): + def from_pretrained(cls, pretrained_model_name, state_dict=None, cache_dir=None, *inputs, **kwargs): """ - Instantiate a PreTrainedBertModel from a pre-trained model file. + Instantiate a PreTrainedBertModel from a pre-trained model file or a pytorch state dict. Download and cache the pre-trained model file if needed. Params: @@ -461,6 +461,8 @@ def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwarg - a path or url to a pretrained model archive containing: . `bert_config.json` a configuration file for the model . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance + cache_dir: an optional path to a folder in which the pre-trained models will be cached. + state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models *inputs, **kwargs: additional input for the specific Bert class (ex: num_labels for BertForSequenceClassification) """ @@ -502,8 +504,9 @@ def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwarg logger.info("Model config {}".format(config)) # Instantiate model. model = cls(config, *inputs, **kwargs) - weights_path = os.path.join(serialization_dir, WEIGHTS_NAME) - state_dict = torch.load(weights_path) + if state_dict is None: + weights_path = os.path.join(serialization_dir, WEIGHTS_NAME) + state_dict = torch.load(weights_path) missing_keys = [] unexpected_keys = [] From b13abfa9feb648836e47ba6e47fa18d28dd300ea Mon Sep 17 00:00:00 2001 From: thomwolf Date: Tue, 11 Dec 2018 11:58:07 +0100 Subject: [PATCH 047/111] add saving and loading model in examples --- examples/run_classifier.py | 17 +++++++++++------ examples/run_squad.py | 9 +++++++++ 2 files changed, 20 insertions(+), 6 deletions(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index 3d13ee463fd9..b535415a7442 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -487,8 +487,8 @@ def main(): len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) # Prepare model - model = BertForSequenceClassification.from_pretrained(args.bert_model, - cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank)) + cache_dir = PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank) # for distributed learning + model = BertForSequenceClassification.from_pretrained(args.bert_model, cache_dir=cache_dir) if args.fp16: model.half() model.to(device) @@ -579,6 +579,15 @@ def main(): model.zero_grad() global_step += 1 + # Save a trained model + model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self + output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") + torch.save(model_to_save.state_dict(), output_model_file) + + # Load a trained model that you have fine-tuned + model_state_dict = torch.load(output_model_file) + model = BertForSequenceClassification.from_pretrained(args.bert_model, state_dict=model_state_dict) + if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = processor.get_dev_examples(args.data_dir) eval_features = convert_examples_to_features( @@ -626,10 +635,6 @@ def main(): 'global_step': global_step, 'loss': tr_loss/nb_tr_steps} - model_to_save = model.module if hasattr(model, 'module') else model - raise NotImplementedError # TODO add save of the configuration file and vocabulary file also ? - output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") - torch.save(model_to_save, output_model_file) output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") diff --git a/examples/run_squad.py b/examples/run_squad.py index cd0a9a302837..cd10e5d5f1de 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -933,6 +933,15 @@ def main(): model.zero_grad() global_step += 1 + # Save a trained model + model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self + output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") + torch.save(model_to_save.state_dict(), output_model_file) + + # Load a trained model that you have fine-tuned + model_state_dict = torch.load(output_model_file) + model = BertForQuestionAnswering.from_pretrained(args.bert_model, state_dict=model_state_dict) + if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = read_squad_examples( input_file=args.predict_file, is_training=False) From ed3b62cd3bc5529b6388e405d4ada78a88903800 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Tue, 11 Dec 2018 12:12:08 +0100 Subject: [PATCH 048/111] added version in __init__.py --- pytorch_pretrained_bert/__init__.py | 1 + requirements.txt | 5 ++--- setup.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pytorch_pretrained_bert/__init__.py b/pytorch_pretrained_bert/__init__.py index fc9b15a12d31..f8d04f5d7fbd 100644 --- a/pytorch_pretrained_bert/__init__.py +++ b/pytorch_pretrained_bert/__init__.py @@ -1,3 +1,4 @@ +__version__ = 0.4.0 from .tokenization import BertTokenizer, BasicTokenizer, WordpieceTokenizer from .modeling import (BertConfig, BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction, diff --git a/requirements.txt b/requirements.txt index e9a3640a9b3a..f37f11cc540b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,5 @@ -# This installs Pytorch for CUDA 8 only. If you are using a newer version, -# please visit http://pytorch.org/ and install the relevant version. -torch>=0.4.1,<0.5.0 +# PyTorch +torch>=0.4.1 # progress bars in model download and training scripts tqdm # Accessing files from S3 directly. diff --git a/setup.py b/setup.py index fc793b53e695..21ca97294d3a 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name="pytorch_pretrained_bert", - version="0.3.0", + version="0.4.0", author="Thomas Wolf, Victor Sanh, Tim Rault, Google AI Language Team Authors", author_email="thomas@huggingface.co", description="PyTorch version of Google AI BERT model with script to load Google pre-trained models", From 770f805ae521b0890438092b09475f99b37643de Mon Sep 17 00:00:00 2001 From: thomwolf Date: Tue, 11 Dec 2018 12:20:22 +0100 Subject: [PATCH 049/111] include version number + comment in setup.py --- pytorch_pretrained_bert/__init__.py | 2 +- setup.py | 35 +++++++++++++++++++++++++++++ 2 files changed, 36 insertions(+), 1 deletion(-) diff --git a/pytorch_pretrained_bert/__init__.py b/pytorch_pretrained_bert/__init__.py index f8d04f5d7fbd..ebc4f7edccb1 100644 --- a/pytorch_pretrained_bert/__init__.py +++ b/pytorch_pretrained_bert/__init__.py @@ -1,4 +1,4 @@ -__version__ = 0.4.0 +__version__ = "0.4.0" from .tokenization import BertTokenizer, BasicTokenizer, WordpieceTokenizer from .modeling import (BertConfig, BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction, diff --git a/setup.py b/setup.py index 21ca97294d3a..a1e1f68db619 100644 --- a/setup.py +++ b/setup.py @@ -1,3 +1,38 @@ +""" +Simple check list from AllenNLP repo: https://github.com/allenai/allennlp/blob/master/setup.py + +To create the package for pypi. + +1. Change the version in __init__.py and setup.py. + +2. Commit these changes with the message: "Release: VERSION" + +3. Add a tag in git to mark the release: "git tag VERSION -m'Adds tag VERSION for pypi' " + Push the tag to git: git push --tags origin master + +4. Build both the sources and the wheel. Do not change anything in setup.py between + creating the wheel and the source distribution (obviously). + + For the wheel, run: "python setup.py bdist_wheel" in the top level allennlp directory. + (this will build a wheel for the python version you use to build it - make sure you use python 3.x). + + For the sources, run: "python setup.py sdist" + You should now have a /dist directory with both .whl and .tar.gz source versions of allennlp. + +5. Check that everything looks correct by uploading the package to the pypi test server: + + twine upload dist/* -r pypitest + (pypi suggest using twine as other methods upload files via plaintext.) + + Check that you can install it in a virtualenv by running: + pip install -i https://testpypi.python.org/pypi allennlp + +6. Upload the final version to actual pypi: + twine upload dist/* -r pypi + +7. Copy the release notes from RELEASE.md to the tag in github once everything is looking hunky-dory. + +""" from setuptools import find_packages, setup setup( From bc659f86adfb26d7cf86e67fa4600b89e63ac07c Mon Sep 17 00:00:00 2001 From: hzhwcmhf Date: Tue, 11 Dec 2018 20:18:56 +0800 Subject: [PATCH 050/111] fix compatibility with python 3.5.2; convert path to str --- pytorch_pretrained_bert/file_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pytorch_pretrained_bert/file_utils.py b/pytorch_pretrained_bert/file_utils.py index f734b7e22b11..1b34407b82de 100644 --- a/pytorch_pretrained_bert/file_utils.py +++ b/pytorch_pretrained_bert/file_utils.py @@ -23,8 +23,8 @@ logger = logging.getLogger(__name__) # pylint: disable=invalid-name -PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', - Path.home() / '.pytorch_pretrained_bert')) +PYTORCH_PRETRAINED_BERT_CACHE = str(Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', + Path.home() / '.pytorch_pretrained_bert'))) def url_to_filename(url: str, etag: str = None) -> str: From 485adde74244f9b614263420d1f823660e0f96fe Mon Sep 17 00:00:00 2001 From: hzhwcmhf Date: Tue, 11 Dec 2018 22:49:19 +0800 Subject: [PATCH 051/111] add pathlib support for file_utils.py on python 3.5 --- pytorch_pretrained_bert/file_utils.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/pytorch_pretrained_bert/file_utils.py b/pytorch_pretrained_bert/file_utils.py index 1b34407b82de..139418f1a544 100644 --- a/pytorch_pretrained_bert/file_utils.py +++ b/pytorch_pretrained_bert/file_utils.py @@ -23,8 +23,8 @@ logger = logging.getLogger(__name__) # pylint: disable=invalid-name -PYTORCH_PRETRAINED_BERT_CACHE = str(Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', - Path.home() / '.pytorch_pretrained_bert'))) +PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', + Path.home() / '.pytorch_pretrained_bert')) def url_to_filename(url: str, etag: str = None) -> str: @@ -45,13 +45,15 @@ def url_to_filename(url: str, etag: str = None) -> str: return filename -def filename_to_url(filename: str, cache_dir: str = None) -> Tuple[str, str]: +def filename_to_url(filename: str, cache_dir: Union[str, Path] = None) -> Tuple[str, str]: """ Return the url and etag (which may be ``None``) stored for `filename`. Raise ``FileNotFoundError`` if `filename` or its stored metadata do not exist. """ if cache_dir is None: cache_dir = PYTORCH_PRETRAINED_BERT_CACHE + if isinstance(cache_dir, Path): + cache_dir = str(cache_dir) cache_path = os.path.join(cache_dir, filename) if not os.path.exists(cache_path): @@ -69,7 +71,7 @@ def filename_to_url(filename: str, cache_dir: str = None) -> Tuple[str, str]: return url, etag -def cached_path(url_or_filename: Union[str, Path], cache_dir: str = None) -> str: +def cached_path(url_or_filename: Union[str, Path], cache_dir: Union[str, Path] = None) -> str: """ Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file and cache it, and @@ -80,6 +82,8 @@ def cached_path(url_or_filename: Union[str, Path], cache_dir: str = None) -> str cache_dir = PYTORCH_PRETRAINED_BERT_CACHE if isinstance(url_or_filename, Path): url_or_filename = str(url_or_filename) + if isinstance(cache_dir, Path): + cache_dir = str(cache_dir) parsed = urlparse(url_or_filename) @@ -158,13 +162,15 @@ def http_get(url: str, temp_file: IO) -> None: progress.close() -def get_from_cache(url: str, cache_dir: str = None) -> str: +def get_from_cache(url: str, cache_dir: Union[str, Path] = None) -> str: """ Given a URL, look for the corresponding dataset in the local cache. If it's not there, download it. Then return the path to the cached file. """ if cache_dir is None: cache_dir = PYTORCH_PRETRAINED_BERT_CACHE + if isinstance(cache_dir, Path): + cache_dir = str(cache_dir) os.makedirs(cache_dir, exist_ok=True) From c8ea286048517d9072397d77f4de21b8483a4531 Mon Sep 17 00:00:00 2001 From: Deyu Fu Date: Wed, 5 Dec 2018 15:07:40 -0800 Subject: [PATCH 052/111] change to apex for better fp16 and multi-gpu support --- README.md | 2 +- examples/run_classifier.py | 125 +++++++++------------- examples/run_squad.py | 136 ++++++++++-------------- pytorch_pretrained_bert/modeling.py | 36 +++++-- pytorch_pretrained_bert/optimization.py | 10 +- tests/optimization_test.py | 2 +- 6 files changed, 142 insertions(+), 169 deletions(-) diff --git a/README.md b/README.md index 720ddc347813..686627cd118d 100644 --- a/README.md +++ b/README.md @@ -326,7 +326,7 @@ The optimizer accepts the following arguments: - `b1` : Adams b1. Default : `0.9` - `b2` : Adams b2. Default : `0.999` - `e` : Adams epsilon. Default : `1e-6` -- `weight_decay_rate:` Weight decay. Default : `0.01` +- `weight_decay:` Weight decay. Default : `0.01` - `max_grad_norm` : Maximum norm for the gradients (`-1` means no clipping). Default : `1.0` ## Examples diff --git a/examples/run_classifier.py b/examples/run_classifier.py index 475ab54c9687..a531ea572554 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -1,5 +1,6 @@ # coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -35,6 +36,13 @@ from pytorch_pretrained_bert.optimization import BertAdam from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE +try: + from apex.optimizers import FP16_Optimizer + from apex.optimizers import FusedAdam + from apex.parallel import DistributedDataParallel as DDP +except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this.") + logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt = '%m/%d/%Y %H:%M:%S', level = logging.INFO) @@ -295,34 +303,10 @@ def accuracy(out, labels): outputs = np.argmax(out, axis=1) return np.sum(outputs == labels) -def copy_optimizer_params_to_model(named_params_model, named_params_optimizer): - """ Utility function for optimize_on_cpu and 16-bits training. - Copy the parameters optimized on CPU/RAM back to the model on GPU - """ - for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model): - if name_opti != name_model: - logger.error("name_opti != name_model: {} {}".format(name_opti, name_model)) - raise ValueError - param_model.data.copy_(param_opti.data) - -def set_optimizer_params_grad(named_params_optimizer, named_params_model, test_nan=False): - """ Utility function for optimize_on_cpu and 16-bits training. - Copy the gradient of the GPU parameters to the CPU/RAMM copy of the model - """ - is_nan = False - for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model): - if name_opti != name_model: - logger.error("name_opti != name_model: {} {}".format(name_opti, name_model)) - raise ValueError - if param_model.grad is not None: - if test_nan and torch.isnan(param_model.grad).sum() > 0: - is_nan = True - if param_opti.grad is None: - param_opti.grad = torch.nn.Parameter(param_opti.data.new().resize_(*param_opti.data.size())) - param_opti.grad.data.copy_(param_model.grad.data) - else: - param_opti.grad = None - return is_nan +def warmup_linear(x, warmup=0.002): + if x < warmup: + return x/warmup + return 1.0 - x def main(): parser = argparse.ArgumentParser() @@ -403,17 +387,15 @@ def main(): type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") - parser.add_argument('--optimize_on_cpu', - default=False, - action='store_true', - help="Whether to perform optimization and keep the optimizer averages on CPU") parser.add_argument('--fp16', default=False, action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument('--loss_scale', - type=float, default=128, - help='Loss scaling, positive power of 2 values can improve fp16 convergence.') + type=float, default=0, + help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" + "0 (default value): dynamic loss scaling.\n" + "Positive power of 2: static loss scaling value.\n") args = parser.parse_args() @@ -433,13 +415,11 @@ def main(): device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: + torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') - if args.fp16: - logger.info("16-bits training currently not supported in distributed training") - args.fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496) logger.info("device %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1)) if args.gradient_accumulation_steps < 1: @@ -487,32 +467,35 @@ def main(): model.half() model.to(device) if args.local_rank != -1: - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], - output_device=args.local_rank) + model = DDP(model) elif n_gpu > 1: model = torch.nn.DataParallel(model) # Prepare optimizer - if args.fp16: - param_optimizer = [(n, param.clone().detach().to('cpu').float().requires_grad_()) \ - for n, param in model.named_parameters()] - elif args.optimize_on_cpu: - param_optimizer = [(n, param.clone().detach().to('cpu').requires_grad_()) \ - for n, param in model.named_parameters()] - else: - param_optimizer = list(model.named_parameters()) - no_decay = ['bias', 'gamma', 'beta'] + param_optimizer = list(model.named_parameters()) + no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [ - {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01}, - {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0} + {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, + {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] t_total = num_train_steps if args.local_rank != -1: t_total = t_total // torch.distributed.get_world_size() - optimizer = BertAdam(optimizer_grouped_parameters, - lr=args.learning_rate, - warmup=args.warmup_proportion, - t_total=t_total) + if args.fp16: + optimizer = FusedAdam(optimizer_grouped_parameters, + lr=args.learning_rate, + bias_correction=False, + max_grad_norm=1.0) + if args.loss_scale == 0: + optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) + else: + optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale) + + else: + optimizer = BertAdam(optimizer_grouped_parameters, + lr=args.learning_rate, + warmup=args.warmup_proportion, + t_total=t_total) global_step = 0 if args.do_train: @@ -543,34 +526,24 @@ def main(): loss = model(input_ids, segment_ids, input_mask, label_ids) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. - if args.fp16 and args.loss_scale != 1.0: - # rescale loss for fp16 training - # see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html - loss = loss * args.loss_scale if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps - loss.backward() + + if args.fp16: + optimizer.backward(loss) + else: + loss.backward() + tr_loss += loss.item() nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 if (step + 1) % args.gradient_accumulation_steps == 0: - if args.fp16 or args.optimize_on_cpu: - if args.fp16 and args.loss_scale != 1.0: - # scale down gradients for fp16 training - for param in model.parameters(): - if param.grad is not None: - param.grad.data = param.grad.data / args.loss_scale - is_nan = set_optimizer_params_grad(param_optimizer, model.named_parameters(), test_nan=True) - if is_nan: - logger.info("FP16 TRAINING: Nan in gradients, reducing loss scaling") - args.loss_scale = args.loss_scale / 2 - model.zero_grad() - continue - optimizer.step() - copy_optimizer_params_to_model(model.named_parameters(), param_optimizer) - else: - optimizer.step() - model.zero_grad() + # modify learning rate with special warm up BERT uses + lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion) + for param_group in optimizer.param_groups: + param_group['lr'] = lr_this_step + optimizer.step() + optimizer.zero_grad() global_step += 1 if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): diff --git a/examples/run_squad.py b/examples/run_squad.py index e47730043e9d..b96fcece37ce 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -1,5 +1,6 @@ # coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -38,7 +39,14 @@ from pytorch_pretrained_bert.optimization import BertAdam from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE -logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', +try: + from apex.optimizers import FP16_Optimizer + from apex.optimizers import FusedAdam + from apex.parallel import DistributedDataParallel as DDP +except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this.") + +logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt = '%m/%d/%Y %H:%M:%S', level = logging.INFO) logger = logging.getLogger(__name__) @@ -669,34 +677,10 @@ def _compute_softmax(scores): probs.append(score / total_sum) return probs -def copy_optimizer_params_to_model(named_params_model, named_params_optimizer): - """ Utility function for optimize_on_cpu and 16-bits training. - Copy the parameters optimized on CPU/RAM back to the model on GPU - """ - for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model): - if name_opti != name_model: - logger.error("name_opti != name_model: {} {}".format(name_opti, name_model)) - raise ValueError - param_model.data.copy_(param_opti.data) - -def set_optimizer_params_grad(named_params_optimizer, named_params_model, test_nan=False): - """ Utility function for optimize_on_cpu and 16-bits training. - Copy the gradient of the GPU parameters to the CPU/RAMM copy of the model - """ - is_nan = False - for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model): - if name_opti != name_model: - logger.error("name_opti != name_model: {} {}".format(name_opti, name_model)) - raise ValueError - if param_model.grad is not None: - if test_nan and torch.isnan(param_model.grad).sum() > 0: - is_nan = True - if param_opti.grad is None: - param_opti.grad = torch.nn.Parameter(param_opti.data.new().resize_(*param_opti.data.size())) - param_opti.grad.data.copy_(param_model.grad.data) - else: - param_opti.grad = None - return is_nan +def warmup_linear(x, warmup=0.002): + if x < warmup: + return x/warmup + return 1.0 - x def main(): parser = argparse.ArgumentParser() @@ -743,8 +727,8 @@ def main(): default=False, action='store_true', help="Whether not to use CUDA when available") - parser.add_argument('--seed', - type=int, + parser.add_argument('--seed', + type=int, default=42, help="random seed for initialization") parser.add_argument('--gradient_accumulation_steps', @@ -759,17 +743,15 @@ def main(): type=int, default=-1, help="local_rank for distributed training on gpus") - parser.add_argument('--optimize_on_cpu', - default=False, - action='store_true', - help="Whether to perform optimization and keep the optimizer averages on CPU") parser.add_argument('--fp16', default=False, action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument('--loss_scale', - type=float, default=128, - help='Loss scaling, positive power of 2 values can improve fp16 convergence.') + type=float, default=0, + help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" + "0 (default value): dynamic loss scaling.\n" + "Positive power of 2: static loss scaling value.\n") args = parser.parse_args() @@ -777,13 +759,11 @@ def main(): device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: + torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') - if args.fp16: - logger.info("16-bits training currently not supported in distributed training") - args.fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496) logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits trainiing: {}".format( device, n_gpu, bool(args.local_rank != -1), args.fp16)) @@ -828,36 +808,45 @@ def main(): # Prepare model model = BertForQuestionAnswering.from_pretrained(args.bert_model, cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank)) + if args.fp16: model.half() model.to(device) if args.local_rank != -1: - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], - output_device=args.local_rank) + model = DDP(model) elif n_gpu > 1: model = torch.nn.DataParallel(model) # Prepare optimizer - if args.fp16: - param_optimizer = [(n, param.clone().detach().to('cpu').float().requires_grad_()) \ - for n, param in model.named_parameters()] - elif args.optimize_on_cpu: - param_optimizer = [(n, param.clone().detach().to('cpu').requires_grad_()) \ - for n, param in model.named_parameters()] - else: - param_optimizer = list(model.named_parameters()) - no_decay = ['bias', 'gamma', 'beta'] + param_optimizer = list(model.named_parameters()) + + # hack to remove pooler, which is not used + # thus it produce None grad that break apex + param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]] + + no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [ - {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01}, - {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0} + {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, + {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] + t_total = num_train_steps if args.local_rank != -1: t_total = t_total // torch.distributed.get_world_size() - optimizer = BertAdam(optimizer_grouped_parameters, - lr=args.learning_rate, - warmup=args.warmup_proportion, - t_total=t_total) + if args.fp16: + optimizer = FusedAdam(optimizer_grouped_parameters, + lr=args.learning_rate, + bias_correction=False, + max_grad_norm=1.0) + if args.loss_scale == 0: + optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) + else: + optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale) + else: + optimizer = BertAdam(optimizer_grouped_parameters, + lr=args.learning_rate, + warmup=args.warmup_proportion, + t_total=t_total) global_step = 0 if args.do_train: @@ -906,31 +895,20 @@ def main(): loss = model(input_ids, segment_ids, input_mask, start_positions, end_positions) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. - if args.fp16 and args.loss_scale != 1.0: - # rescale loss for fp16 training - # see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html - loss = loss * args.loss_scale if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps - loss.backward() + + if args.fp16: + optimizer.backward(loss) + else: + loss.backward() if (step + 1) % args.gradient_accumulation_steps == 0: - if args.fp16 or args.optimize_on_cpu: - if args.fp16 and args.loss_scale != 1.0: - # scale down gradients for fp16 training - for param in model.parameters(): - if param.grad is not None: - param.grad.data = param.grad.data / args.loss_scale - is_nan = set_optimizer_params_grad(param_optimizer, model.named_parameters(), test_nan=True) - if is_nan: - logger.info("FP16 TRAINING: Nan in gradients, reducing loss scaling") - args.loss_scale = args.loss_scale / 2 - model.zero_grad() - continue - optimizer.step() - copy_optimizer_params_to_model(model.named_parameters(), param_optimizer) - else: - optimizer.step() - model.zero_grad() + # modify learning rate with special warm up BERT uses + lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion) + for param_group in optimizer.param_groups: + param_group['lr'] = lr_this_step + optimizer.step() + optimizer.zero_grad() global_step += 1 if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0): diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index 3af5854072fe..7b2b05e2e97e 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -1,5 +1,6 @@ # coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -30,10 +31,14 @@ import torch from torch import nn from torch.nn import CrossEntropyLoss +try: + from apex.normalization.fused_layer_norm import FusedLayerNorm +except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this.") from .file_utils import cached_path -logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', +logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt = '%m/%d/%Y %H:%M:%S', level = logging.INFO) logger = logging.getLogger(__name__) @@ -180,7 +185,7 @@ def __init__(self, config): # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file - self.LayerNorm = BertLayerNorm(config) + self.LayerNorm = FusedLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, input_ids, token_type_ids=None): @@ -255,7 +260,7 @@ class BertSelfOutput(nn.Module): def __init__(self, config): super(BertSelfOutput, self).__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.LayerNorm = BertLayerNorm(config) + self.LayerNorm = FusedLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): @@ -294,7 +299,7 @@ class BertOutput(nn.Module): def __init__(self, config): super(BertOutput, self).__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) - self.LayerNorm = BertLayerNorm(config) + self.LayerNorm = FusedLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): @@ -322,7 +327,7 @@ class BertEncoder(nn.Module): def __init__(self, config): super(BertEncoder, self).__init__() layer = BertLayer(config) - self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)]) + self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)]) def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True): all_encoder_layers = [] @@ -356,7 +361,7 @@ def __init__(self, config): self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.transform_act_fn = ACT2FN[config.hidden_act] \ if isinstance(config.hidden_act, str) else config.hidden_act - self.LayerNorm = BertLayerNorm(config) + self.LayerNorm = FusedLayerNorm(config.hidden_size, eps=1e-12) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) @@ -438,6 +443,9 @@ def init_bert_weights(self, module): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + elif isinstance(module, FusedLayerNorm): + module.bias.data.normal_(mean=0.0, std=self.config.initializer_range) + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) elif isinstance(module, BertLayerNorm): module.beta.data.normal_(mean=0.0, std=self.config.initializer_range) module.gamma.data.normal_(mean=0.0, std=self.config.initializer_range) @@ -449,7 +457,7 @@ def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwarg """ Instantiate a PreTrainedBertModel from a pre-trained model file. Download and cache the pre-trained model file if needed. - + Params: pretrained_model_name: either: - a str with the name of a pre-trained model to load selected in the list of: @@ -505,6 +513,20 @@ def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwarg weights_path = os.path.join(serialization_dir, WEIGHTS_NAME) state_dict = torch.load(weights_path) + old_keys = [] + new_keys = [] + for key in state_dict.keys(): + new_key = None + if 'gamma' in key: + new_key = key.replace('gamma','weight') + if 'beta' in key: + new_key = key.replace('beta','bias') + if new_key: + old_keys.append(key) + new_keys.append(new_key) + for old_key, new_key in zip(old_keys, new_keys): + state_dict[new_key]=state_dict.pop(old_key) + missing_keys = [] unexpected_keys = [] error_msgs = [] diff --git a/pytorch_pretrained_bert/optimization.py b/pytorch_pretrained_bert/optimization.py index 4314c84144cb..f3d1de0d37b8 100644 --- a/pytorch_pretrained_bert/optimization.py +++ b/pytorch_pretrained_bert/optimization.py @@ -53,11 +53,11 @@ class BertAdam(Optimizer): b1: Adams b1. Default: 0.9 b2: Adams b2. Default: 0.999 e: Adams epsilon. Default: 1e-6 - weight_decay_rate: Weight decay. Default: 0.01 + weight_decay: Weight decay. Default: 0.01 max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0 """ def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear', - b1=0.9, b2=0.999, e=1e-6, weight_decay_rate=0.01, + b1=0.9, b2=0.999, e=1e-6, weight_decay=0.01, max_grad_norm=1.0): if lr is not required and lr < 0.0: raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr)) @@ -72,7 +72,7 @@ def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_ if not e >= 0.0: raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e)) defaults = dict(lr=lr, schedule=schedule, warmup=warmup, t_total=t_total, - b1=b1, b2=b2, e=e, weight_decay_rate=weight_decay_rate, + b1=b1, b2=b2, e=e, weight_decay=weight_decay, max_grad_norm=max_grad_norm) super(BertAdam, self).__init__(params, defaults) @@ -140,8 +140,8 @@ def step(self, closure=None): # Instead we want to decay the weights in a manner that doesn't interact # with the m/v parameters. This is equivalent to adding the square # of the weights to the loss with plain (non-momentum) SGD. - if group['weight_decay_rate'] > 0.0: - update += group['weight_decay_rate'] * p.data + if group['weight_decay'] > 0.0: + update += group['weight_decay'] * p.data if group['t_total'] != -1: schedule_fct = SCHEDULES[group['schedule']] diff --git a/tests/optimization_test.py b/tests/optimization_test.py index 1c010750ae1f..ad13c28d0c5e 100644 --- a/tests/optimization_test.py +++ b/tests/optimization_test.py @@ -35,7 +35,7 @@ def test_adam(self): criterion = torch.nn.MSELoss(reduction='elementwise_mean') # No warmup, constant schedule, no gradient clipping optimizer = BertAdam(params=[w], lr=2e-1, - weight_decay_rate=0.0, + weight_decay=0.0, max_grad_norm=-1) for _ in range(100): loss = criterion(w, target) From dcb50eaa4b80d3ab75d373c36780c80fb47cfd97 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Wed, 12 Dec 2018 18:17:46 +0100 Subject: [PATCH 053/111] Swag example readme section update with gradient accumulation run. --- README.md | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 23cd315c298c..0ccf96c42c6a 100644 --- a/README.md +++ b/README.md @@ -441,25 +441,22 @@ python run_swag.py \ --do_train \ --do_eval \ --data_dir $SWAG_DIR/data - --train_batch_size 4 \ + --train_batch_size 16 \ --learning_rate 2e-5 \ --num_train_epochs 3.0 \ --max_seq_length 80 \ --output_dir /tmp/swag_output/ + --gradient_accumulation_steps 4 ``` Training with the previous hyper-parameters gave us the following results: ``` -eval_accuracy = 0.7776167149855043 -eval_loss = 1.006812262735175 -global_step = 55161 -loss = 0.282251750624779 +eval_accuracy = 0.8062081375587323 +eval_loss = 0.5966546792367169 +global_step = 13788 +loss = 0.06423990014260186 ``` -The difference with the `81.6%` accuracy announced in the Bert article -is probably due to the different `training_batch_size` (here 4 and 16 -in the article). - ## Fine-tuning BERT-large on GPUs The options we list above allow to fine-tune BERT-large rather easily on GPU(s) instead of the TPU used by the original implementation. From 3b0a14b7614d5a1741dbd5288f6ab1e38d4a3103 Mon Sep 17 00:00:00 2001 From: Deyu Fu Date: Wed, 12 Dec 2018 15:05:45 -0800 Subject: [PATCH 054/111] add fallback path for apex used in modeling.py --- pytorch_pretrained_bert/modeling.py | 51 +++++++++++++---------------- 1 file changed, 23 insertions(+), 28 deletions(-) diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index 7b2b05e2e97e..72c7d47f86a0 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -31,10 +31,6 @@ import torch from torch import nn from torch.nn import CrossEntropyLoss -try: - from apex.normalization.fused_layer_norm import FusedLayerNorm -except ImportError: - raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this.") from .file_utils import cached_path @@ -157,22 +153,24 @@ def to_json_string(self): """Serializes this instance to a JSON string.""" return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" - -class BertLayerNorm(nn.Module): - def __init__(self, config, variance_epsilon=1e-12): - """Construct a layernorm module in the TF style (epsilon inside the square root). - """ - super(BertLayerNorm, self).__init__() - self.gamma = nn.Parameter(torch.ones(config.hidden_size)) - self.beta = nn.Parameter(torch.zeros(config.hidden_size)) - self.variance_epsilon = variance_epsilon - - def forward(self, x): - u = x.mean(-1, keepdim=True) - s = (x - u).pow(2).mean(-1, keepdim=True) - x = (x - u) / torch.sqrt(s + self.variance_epsilon) - return self.gamma * x + self.beta - +try: + from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm +except ImportError: + print("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex.") + class BertLayerNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-12): + """Construct a layernorm module in the TF style (epsilon inside the square root). + """ + super(BertLayerNorm, self).__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.bias = nn.Parameter(torch.zeros(hidden_size)) + self.variance_epsilon = eps + + def forward(self, x): + u = x.mean(-1, keepdim=True) + s = (x - u).pow(2).mean(-1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.variance_epsilon) + return self.weight * x + self.bias class BertEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings. @@ -185,7 +183,7 @@ def __init__(self, config): # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file - self.LayerNorm = FusedLayerNorm(config.hidden_size, eps=1e-12) + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, input_ids, token_type_ids=None): @@ -260,7 +258,7 @@ class BertSelfOutput(nn.Module): def __init__(self, config): super(BertSelfOutput, self).__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.LayerNorm = FusedLayerNorm(config.hidden_size, eps=1e-12) + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): @@ -299,7 +297,7 @@ class BertOutput(nn.Module): def __init__(self, config): super(BertOutput, self).__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) - self.LayerNorm = FusedLayerNorm(config.hidden_size, eps=1e-12) + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): @@ -361,7 +359,7 @@ def __init__(self, config): self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.transform_act_fn = ACT2FN[config.hidden_act] \ if isinstance(config.hidden_act, str) else config.hidden_act - self.LayerNorm = FusedLayerNorm(config.hidden_size, eps=1e-12) + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) @@ -443,12 +441,9 @@ def init_bert_weights(self, module): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) - elif isinstance(module, FusedLayerNorm): + elif isinstance(module, BertLayerNorm): module.bias.data.normal_(mean=0.0, std=self.config.initializer_range) module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) - elif isinstance(module, BertLayerNorm): - module.beta.data.normal_(mean=0.0, std=self.config.initializer_range) - module.gamma.data.normal_(mean=0.0, std=self.config.initializer_range) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() From 13bf0d4659f633a118ca75f4f8b3291e0e0ffc05 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Sun, 9 Dec 2018 16:17:11 -0500 Subject: [PATCH 055/111] fixing Adam weights skip in TF convert script --- pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py b/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py index 20fdd8c0d6e8..79b5f41adcf4 100755 --- a/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py +++ b/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py @@ -50,7 +50,7 @@ def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytor name = name.split('/') # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model - if name[-1] in ["adam_v", "adam_m"]: + if any(n in ["adam_v", "adam_m"] for n in name): print("Skipping {}".format("/".join(name))) continue pointer = model From 85fff78c2ddea175cc04ff8399e690de0cead686 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Sun, 9 Dec 2018 16:57:51 -0500 Subject: [PATCH 056/111] compatibility PT 1.0 and 0.4.1 --- tests/optimization_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/optimization_test.py b/tests/optimization_test.py index ad13c28d0c5e..848b9d1cf5c2 100644 --- a/tests/optimization_test.py +++ b/tests/optimization_test.py @@ -32,7 +32,7 @@ def assertListAlmostEqual(self, list1, list2, tol): def test_adam(self): w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True) target = torch.tensor([0.4, 0.2, -0.5]) - criterion = torch.nn.MSELoss(reduction='elementwise_mean') + criterion = torch.nn.MSELoss() # No warmup, constant schedule, no gradient clipping optimizer = BertAdam(params=[w], lr=2e-1, weight_decay=0.0, From b3caec5a5662f591a1f148bf34ba4f853be514e2 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Sun, 9 Dec 2018 17:04:23 -0500 Subject: [PATCH 057/111] adding save checkpoint and loading in examples --- examples/run_classifier.py | 6 +++++- examples/run_squad.py | 2 +- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index a531ea572554..f18c5489bae0 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -329,7 +329,7 @@ def main(): default=None, type=str, required=True, - help="The output directory where the model checkpoints will be written.") + help="The output directory where the model predictions and checkpoints will be written.") ## Other parameters parser.add_argument("--max_seq_length", @@ -593,6 +593,10 @@ def main(): 'global_step': global_step, 'loss': tr_loss/nb_tr_steps} + model_to_save = model.module if hasattr(model, 'module') else model + raise NotImplementedError # TODO add save of the configuration file and vocabulary file also ? + output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") + torch.save(model_to_save, output_model_file) output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") diff --git a/examples/run_squad.py b/examples/run_squad.py index b96fcece37ce..b0668b38d8c4 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -690,7 +690,7 @@ def main(): help="Bert pre-trained model selected in the list: bert-base-uncased, " "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.") parser.add_argument("--output_dir", default=None, type=str, required=True, - help="The output directory where the model checkpoints will be written.") + help="The output directory where the model checkpoints and predictions will be written.") ## Other parameters parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json") From 93f335ef86b2a14ffc41daba612d022a1c73e045 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Tue, 11 Dec 2018 11:50:38 +0100 Subject: [PATCH 058/111] add pretrained loading from state_dict --- pytorch_pretrained_bert/modeling.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index 1aeff4dd04c7..bfc5585ea8e7 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -448,9 +448,9 @@ def init_bert_weights(self, module): module.bias.data.zero_() @classmethod - def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwargs): + def from_pretrained(cls, pretrained_model_name, state_dict=None, cache_dir=None, *inputs, **kwargs): """ - Instantiate a PreTrainedBertModel from a pre-trained model file. + Instantiate a PreTrainedBertModel from a pre-trained model file or a pytorch state dict. Download and cache the pre-trained model file if needed. Params: @@ -464,6 +464,8 @@ def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwarg - a path or url to a pretrained model archive containing: . `bert_config.json` a configuration file for the model . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance + cache_dir: an optional path to a folder in which the pre-trained models will be cached. + state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models *inputs, **kwargs: additional input for the specific Bert class (ex: num_labels for BertForSequenceClassification) """ @@ -505,8 +507,9 @@ def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwarg logger.info("Model config {}".format(config)) # Instantiate model. model = cls(config, *inputs, **kwargs) - weights_path = os.path.join(serialization_dir, WEIGHTS_NAME) - state_dict = torch.load(weights_path) + if state_dict is None: + weights_path = os.path.join(serialization_dir, WEIGHTS_NAME) + state_dict = torch.load(weights_path) old_keys = [] new_keys = [] From d3fcec1a3e21cf62e47a0b3910a991d4025ad23b Mon Sep 17 00:00:00 2001 From: thomwolf Date: Tue, 11 Dec 2018 11:58:07 +0100 Subject: [PATCH 059/111] add saving and loading model in examples --- examples/run_classifier.py | 13 +++++++++---- examples/run_squad.py | 9 +++++++++ 2 files changed, 18 insertions(+), 4 deletions(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index f18c5489bae0..7c4eb7da47fc 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -546,6 +546,15 @@ def main(): optimizer.zero_grad() global_step += 1 + # Save a trained model + model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self + output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") + torch.save(model_to_save.state_dict(), output_model_file) + + # Load a trained model that you have fine-tuned + model_state_dict = torch.load(output_model_file) + model = BertForSequenceClassification.from_pretrained(args.bert_model, state_dict=model_state_dict) + if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = processor.get_dev_examples(args.data_dir) eval_features = convert_examples_to_features( @@ -593,10 +602,6 @@ def main(): 'global_step': global_step, 'loss': tr_loss/nb_tr_steps} - model_to_save = model.module if hasattr(model, 'module') else model - raise NotImplementedError # TODO add save of the configuration file and vocabulary file also ? - output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") - torch.save(model_to_save, output_model_file) output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") diff --git a/examples/run_squad.py b/examples/run_squad.py index b0668b38d8c4..81956ad394c8 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -911,6 +911,15 @@ def main(): optimizer.zero_grad() global_step += 1 + # Save a trained model + model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self + output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") + torch.save(model_to_save.state_dict(), output_model_file) + + # Load a trained model that you have fine-tuned + model_state_dict = torch.load(output_model_file) + model = BertForQuestionAnswering.from_pretrained(args.bert_model, state_dict=model_state_dict) + if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = read_squad_examples( input_file=args.predict_file, is_training=False) From ce5217763848fa172f87ded595d77af872e0d125 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Tue, 11 Dec 2018 12:12:08 +0100 Subject: [PATCH 060/111] added version in __init__.py --- pytorch_pretrained_bert/__init__.py | 1 + requirements.txt | 5 ++--- setup.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pytorch_pretrained_bert/__init__.py b/pytorch_pretrained_bert/__init__.py index e1ecabf31dbd..416fe25e6971 100644 --- a/pytorch_pretrained_bert/__init__.py +++ b/pytorch_pretrained_bert/__init__.py @@ -1,3 +1,4 @@ +__version__ = 0.4.0 from .tokenization import BertTokenizer, BasicTokenizer, WordpieceTokenizer from .modeling import (BertConfig, BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction, diff --git a/requirements.txt b/requirements.txt index e9a3640a9b3a..f37f11cc540b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,5 @@ -# This installs Pytorch for CUDA 8 only. If you are using a newer version, -# please visit http://pytorch.org/ and install the relevant version. -torch>=0.4.1,<0.5.0 +# PyTorch +torch>=0.4.1 # progress bars in model download and training scripts tqdm # Accessing files from S3 directly. diff --git a/setup.py b/setup.py index fc793b53e695..21ca97294d3a 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name="pytorch_pretrained_bert", - version="0.3.0", + version="0.4.0", author="Thomas Wolf, Victor Sanh, Tim Rault, Google AI Language Team Authors", author_email="thomas@huggingface.co", description="PyTorch version of Google AI BERT model with script to load Google pre-trained models", From 1cbb32a5428b6a3ad1fbc0de6b0709a63cfda903 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Tue, 11 Dec 2018 12:20:22 +0100 Subject: [PATCH 061/111] include version number + comment in setup.py --- pytorch_pretrained_bert/__init__.py | 2 +- setup.py | 35 +++++++++++++++++++++++++++++ 2 files changed, 36 insertions(+), 1 deletion(-) diff --git a/pytorch_pretrained_bert/__init__.py b/pytorch_pretrained_bert/__init__.py index 416fe25e6971..0ef826374815 100644 --- a/pytorch_pretrained_bert/__init__.py +++ b/pytorch_pretrained_bert/__init__.py @@ -1,4 +1,4 @@ -__version__ = 0.4.0 +__version__ = "0.4.0" from .tokenization import BertTokenizer, BasicTokenizer, WordpieceTokenizer from .modeling import (BertConfig, BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction, diff --git a/setup.py b/setup.py index 21ca97294d3a..a1e1f68db619 100644 --- a/setup.py +++ b/setup.py @@ -1,3 +1,38 @@ +""" +Simple check list from AllenNLP repo: https://github.com/allenai/allennlp/blob/master/setup.py + +To create the package for pypi. + +1. Change the version in __init__.py and setup.py. + +2. Commit these changes with the message: "Release: VERSION" + +3. Add a tag in git to mark the release: "git tag VERSION -m'Adds tag VERSION for pypi' " + Push the tag to git: git push --tags origin master + +4. Build both the sources and the wheel. Do not change anything in setup.py between + creating the wheel and the source distribution (obviously). + + For the wheel, run: "python setup.py bdist_wheel" in the top level allennlp directory. + (this will build a wheel for the python version you use to build it - make sure you use python 3.x). + + For the sources, run: "python setup.py sdist" + You should now have a /dist directory with both .whl and .tar.gz source versions of allennlp. + +5. Check that everything looks correct by uploading the package to the pypi test server: + + twine upload dist/* -r pypitest + (pypi suggest using twine as other methods upload files via plaintext.) + + Check that you can install it in a virtualenv by running: + pip install -i https://testpypi.python.org/pypi allennlp + +6. Upload the final version to actual pypi: + twine upload dist/* -r pypi + +7. Copy the release notes from RELEASE.md to the tag in github once everything is looking hunky-dory. + +""" from setuptools import find_packages, setup setup( From 4946c2c5003a1b71632a5c89c9372e30c820fea4 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 13 Dec 2018 13:02:07 +0100 Subject: [PATCH 062/111] run_swag example in readme --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 253245561b28..bdc1d853ef67 100644 --- a/README.md +++ b/README.md @@ -440,6 +440,7 @@ export SWAG_DIR=/path/to/SWAG python run_swag.py \ --bert_model bert-base-uncased \ --do_train \ + --do_lower_case \ --do_eval \ --data_dir $SWAG_DIR/data --train_batch_size 16 \ From 52c53f39d076249f379c397d34fe27f67d382dc9 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 13 Dec 2018 13:02:17 +0100 Subject: [PATCH 063/111] clean up apex integration --- pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py | 4 ++-- pytorch_pretrained_bert/modeling.py | 4 ++-- tests/optimization_test.py | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py b/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py index 79b5f41adcf4..120624bc1b49 100755 --- a/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py +++ b/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py @@ -59,9 +59,9 @@ def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytor l = re.split(r'_(\d+)', m_name) else: l = [m_name] - if l[0] == 'kernel': + if l[0] == 'kernel' or l[0] == 'gamma': pointer = getattr(pointer, 'weight') - elif l[0] == 'output_bias': + elif l[0] == 'output_bias' or l[0] == 'beta': pointer = getattr(pointer, 'bias') elif l[0] == 'output_weights': pointer = getattr(pointer, 'weight') diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index 0699f6719994..c6940c74eb2d 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -516,9 +516,9 @@ def from_pretrained(cls, pretrained_model_name, state_dict=None, cache_dir=None, for key in state_dict.keys(): new_key = None if 'gamma' in key: - new_key = key.replace('gamma','weight') + new_key = key.replace('gamma', 'weight') if 'beta' in key: - new_key = key.replace('beta','bias') + new_key = key.replace('beta', 'bias') if new_key: old_keys.append(key) new_keys.append(new_key) diff --git a/tests/optimization_test.py b/tests/optimization_test.py index 848b9d1cf5c2..184637359156 100644 --- a/tests/optimization_test.py +++ b/tests/optimization_test.py @@ -35,7 +35,7 @@ def test_adam(self): criterion = torch.nn.MSELoss() # No warmup, constant schedule, no gradient clipping optimizer = BertAdam(params=[w], lr=2e-1, - weight_decay=0.0, + weight_decay_rate=0.0, max_grad_norm=-1) for _ in range(100): loss = criterion(w, target) From 0cf88ff084f963261000be436cd5e3ae3dd4adb7 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 13 Dec 2018 13:28:00 +0100 Subject: [PATCH 064/111] make examples work without apex --- examples/run_classifier.py | 18 +++++++++++------- examples/run_squad.py | 18 +++++++++++------- 2 files changed, 22 insertions(+), 14 deletions(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index 7c4eb7da47fc..aca099daf243 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -36,13 +36,6 @@ from pytorch_pretrained_bert.optimization import BertAdam from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE -try: - from apex.optimizers import FP16_Optimizer - from apex.optimizers import FusedAdam - from apex.parallel import DistributedDataParallel as DDP -except ImportError: - raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this.") - logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt = '%m/%d/%Y %H:%M:%S', level = logging.INFO) @@ -467,6 +460,11 @@ def main(): model.half() model.to(device) if args.local_rank != -1: + try: + from apex.parallel import DistributedDataParallel as DDP + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") + model = DDP(model) elif n_gpu > 1: model = torch.nn.DataParallel(model) @@ -482,6 +480,12 @@ def main(): if args.local_rank != -1: t_total = t_total // torch.distributed.get_world_size() if args.fp16: + try: + from apex.optimizers import FP16_Optimizer + from apex.optimizers import FusedAdam + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") + optimizer = FusedAdam(optimizer_grouped_parameters, lr=args.learning_rate, bias_correction=False, diff --git a/examples/run_squad.py b/examples/run_squad.py index 81956ad394c8..147cd60f29cd 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -39,13 +39,6 @@ from pytorch_pretrained_bert.optimization import BertAdam from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE -try: - from apex.optimizers import FP16_Optimizer - from apex.optimizers import FusedAdam - from apex.parallel import DistributedDataParallel as DDP -except ImportError: - raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this.") - logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt = '%m/%d/%Y %H:%M:%S', level = logging.INFO) @@ -813,6 +806,11 @@ def main(): model.half() model.to(device) if args.local_rank != -1: + try: + from apex.parallel import DistributedDataParallel as DDP + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") + model = DDP(model) elif n_gpu > 1: model = torch.nn.DataParallel(model) @@ -834,6 +832,12 @@ def main(): if args.local_rank != -1: t_total = t_total // torch.distributed.get_world_size() if args.fp16: + try: + from apex.optimizers import FP16_Optimizer + from apex.optimizers import FusedAdam + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") + optimizer = FusedAdam(optimizer_grouped_parameters, lr=args.learning_rate, bias_correction=False, From 0f544625f4aaf04c45e4674884cf577857ae88a4 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 13 Dec 2018 13:35:59 +0100 Subject: [PATCH 065/111] fix swag example for work with apex --- README.md | 4 +- examples/run_swag.py | 131 ++++++++++++++++--------------------- tests/optimization_test.py | 2 +- 3 files changed, 59 insertions(+), 78 deletions(-) diff --git a/README.md b/README.md index bdc1d853ef67..96393bc082ec 100644 --- a/README.md +++ b/README.md @@ -442,12 +442,12 @@ python run_swag.py \ --do_train \ --do_lower_case \ --do_eval \ - --data_dir $SWAG_DIR/data + --data_dir $SWAG_DIR/data \ --train_batch_size 16 \ --learning_rate 2e-5 \ --num_train_epochs 3.0 \ --max_seq_length 80 \ - --output_dir /tmp/swag_output/ + --output_dir /tmp/swag_output/ \ --gradient_accumulation_steps 4 ``` diff --git a/examples/run_swag.py b/examples/run_swag.py index 88297bf80107..d1ebb7354248 100644 --- a/examples/run_swag.py +++ b/examples/run_swag.py @@ -1,5 +1,6 @@ # coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -232,34 +233,10 @@ def select_field(features, field): for feature in features ] -def copy_optimizer_params_to_model(named_params_model, named_params_optimizer): - """ Utility function for optimize_on_cpu and 16-bits training. - Copy the parameters optimized on CPU/RAM back to the model on GPU - """ - for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model): - if name_opti != name_model: - logger.error("name_opti != name_model: {} {}".format(name_opti, name_model)) - raise ValueError - param_model.data.copy_(param_opti.data) - -def set_optimizer_params_grad(named_params_optimizer, named_params_model, test_nan=False): - """ Utility function for optimize_on_cpu and 16-bits training. - Copy the gradient of the GPU parameters to the CPU/RAMM copy of the model - """ - is_nan = False - for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model): - if name_opti != name_model: - logger.error("name_opti != name_model: {} {}".format(name_opti, name_model)) - raise ValueError - if param_model.grad is not None: - if test_nan and torch.isnan(param_model.grad).sum() > 0: - is_nan = True - if param_opti.grad is None: - param_opti.grad = torch.nn.Parameter(param_opti.data.new().resize_(*param_opti.data.size())) - param_opti.grad.data.copy_(param_model.grad.data) - else: - param_opti.grad = None - return is_nan +def warmup_linear(x, warmup=0.002): + if x < warmup: + return x/warmup + return 1.0 - x def main(): parser = argparse.ArgumentParser() @@ -335,17 +312,15 @@ def main(): type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") - parser.add_argument('--optimize_on_cpu', - default=False, - action='store_true', - help="Whether to perform optimization and keep the optimizer averages on CPU") parser.add_argument('--fp16', default=False, action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument('--loss_scale', - type=float, default=128, - help='Loss scaling, positive power of 2 values can improve fp16 convergence.') + type=float, default=0, + help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" + "0 (default value): dynamic loss scaling.\n" + "Positive power of 2: static loss scaling value.\n") args = parser.parse_args() @@ -353,13 +328,11 @@ def main(): device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: + torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') - if args.fp16: - logger.info("16-bits training currently not supported in distributed training") - args.fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496) logger.info("device %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1)) if args.gradient_accumulation_steps < 1: @@ -399,32 +372,50 @@ def main(): model.half() model.to(device) if args.local_rank != -1: - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], - output_device=args.local_rank) + try: + from apex.parallel import DistributedDataParallel as DDP + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") + + model = DDP(model) elif n_gpu > 1: model = torch.nn.DataParallel(model) # Prepare optimizer - if args.fp16: - param_optimizer = [(n, param.clone().detach().to('cpu').float().requires_grad_()) \ - for n, param in model.named_parameters()] - elif args.optimize_on_cpu: - param_optimizer = [(n, param.clone().detach().to('cpu').requires_grad_()) \ - for n, param in model.named_parameters()] - else: - param_optimizer = list(model.named_parameters()) - no_decay = ['bias', 'gamma', 'beta'] + param_optimizer = list(model.named_parameters()) + + # hack to remove pooler, which is not used + # thus it produce None grad that break apex + param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]] + + no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [ - {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01}, - {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0} + {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, + {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] t_total = num_train_steps if args.local_rank != -1: t_total = t_total // torch.distributed.get_world_size() - optimizer = BertAdam(optimizer_grouped_parameters, - lr=args.learning_rate, - warmup=args.warmup_proportion, - t_total=t_total) + if args.fp16: + try: + from apex.optimizers import FP16_Optimizer + from apex.optimizers import FusedAdam + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") + + optimizer = FusedAdam(optimizer_grouped_parameters, + lr=args.learning_rate, + bias_correction=False, + max_grad_norm=1.0) + if args.loss_scale == 0: + optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) + else: + optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale) + else: + optimizer = BertAdam(optimizer_grouped_parameters, + lr=args.learning_rate, + warmup=args.warmup_proportion, + t_total=t_total) global_step = 0 if args.do_train: @@ -461,28 +452,18 @@ def main(): loss = loss * args.loss_scale if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps - loss.backward() - tr_loss += loss.item() - nb_tr_examples += input_ids.size(0) - nb_tr_steps += 1 + + if args.fp16: + optimizer.backward(loss) + else: + loss.backward() if (step + 1) % args.gradient_accumulation_steps == 0: - if args.fp16 or args.optimize_on_cpu: - if args.fp16 and args.loss_scale != 1.0: - # scale down gradients for fp16 training - for param in model.parameters(): - if param.grad is not None: - param.grad.data = param.grad.data / args.loss_scale - is_nan = set_optimizer_params_grad(param_optimizer, model.named_parameters(), test_nan=True) - if is_nan: - logger.info("FP16 TRAINING: Nan in gradients, reducing loss scaling") - args.loss_scale = args.loss_scale / 2 - model.zero_grad() - continue - optimizer.step() - copy_optimizer_params_to_model(model.named_parameters(), param_optimizer) - else: - optimizer.step() - model.zero_grad() + # modify learning rate with special warm up BERT uses + lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion) + for param_group in optimizer.param_groups: + param_group['lr'] = lr_this_step + optimizer.step() + optimizer.zero_grad() global_step += 1 if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): diff --git a/tests/optimization_test.py b/tests/optimization_test.py index 184637359156..848b9d1cf5c2 100644 --- a/tests/optimization_test.py +++ b/tests/optimization_test.py @@ -35,7 +35,7 @@ def test_adam(self): criterion = torch.nn.MSELoss() # No warmup, constant schedule, no gradient clipping optimizer = BertAdam(params=[w], lr=2e-1, - weight_decay_rate=0.0, + weight_decay=0.0, max_grad_norm=-1) for _ in range(100): loss = criterion(w, target) From 087798b7fa6dc82f34a7462fddd313d38b251bda Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 13 Dec 2018 14:48:12 +0100 Subject: [PATCH 066/111] fix reloading model for evaluation in examples --- README.md | 56 ++++++++++++++++++++++++++++---------- examples/run_classifier.py | 3 ++ examples/run_squad.py | 3 ++ examples/run_swag.py | 20 ++++++++++++-- 4 files changed, 65 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 96393bc082ec..c4f6b99f1c4f 100644 --- a/README.md +++ b/README.md @@ -46,13 +46,13 @@ python -m pytest -sv tests/ This package comprises the following classes that can be imported in Python and are detailed in the [Doc](#doc) section of this readme: -- Seven PyTorch models (`torch.nn.Module`) for Bert with pre-trained weights (in the [`modeling.py`](./pytorch_pretrained_bert/modeling.py) file): +- Eight PyTorch models (`torch.nn.Module`) for Bert with pre-trained weights (in the [`modeling.py`](./pytorch_pretrained_bert/modeling.py) file): - [`BertModel`](./pytorch_pretrained_bert/modeling.py#L537) - raw BERT Transformer model (**fully pre-trained**), - [`BertForMaskedLM`](./pytorch_pretrained_bert/modeling.py#L691) - BERT Transformer with the pre-trained masked language modeling head on top (**fully pre-trained**), - [`BertForNextSentencePrediction`](./pytorch_pretrained_bert/modeling.py#L752) - BERT Transformer with the pre-trained next sentence prediction classifier on top (**fully pre-trained**), - [`BertForPreTraining`](./pytorch_pretrained_bert/modeling.py#L620) - BERT Transformer with masked language modeling head and next sentence prediction classifier on top (**fully pre-trained**), - [`BertForSequenceClassification`](./pytorch_pretrained_bert/modeling.py#L814) - BERT Transformer with a sequence classification head on top (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**), - - [`BertForMultipleChoice`](./pytorch_pretrained_bert/modeling.py#L880) - BERT Transformer with a multiple choice head on top (used for task like Swag) (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**), + - [`BertForMultipleChoice`](./pytorch_pretrained_bert/modeling.py#L880) - BERT Transformer with a multiple choice head on top (used for task like Swag) (BERT Transformer is **pre-trained**, the multiple choice classification head **is only initialized and has to be trained**), - [`BertForTokenClassification`](./pytorch_pretrained_bert/modeling.py#L949) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**), - [`BertForQuestionAnswering`](./pytorch_pretrained_bert/modeling.py#L1015) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**). @@ -156,7 +156,7 @@ Here is a detailed documentation of the classes in the package and how to use th | Sub-section | Description | |-|-| | [Loading Google AI's pre-trained weigths](#Loading-Google-AIs-pre-trained-weigths-and-PyTorch-dump) | How to load Google AI's pre-trained weight or a PyTorch saved instance | -| [PyTorch models](#PyTorch-models) | API of the seven PyTorch model classes: `BertModel`, `BertForMaskedLM`, `BertForNextSentencePrediction`, `BertForPreTraining`, `BertForSequenceClassification` or `BertForQuestionAnswering` | +| [PyTorch models](#PyTorch-models) | API of the eight PyTorch model classes: `BertModel`, `BertForMaskedLM`, `BertForNextSentencePrediction`, `BertForPreTraining`, `BertForSequenceClassification`, `BertForMultipleChoice` or `BertForQuestionAnswering` | | [Tokenizer: `BertTokenizer`](#Tokenizer-BertTokenizer) | API of the `BertTokenizer` class| | [Optimizer: `BertAdam`](#Optimizer-BertAdam) | API of the `BertAdam` class | @@ -170,7 +170,7 @@ model = BERT_CLASS.from_pretrain(PRE_TRAINED_MODEL_NAME_OR_PATH, cache_dir=None) where -- `BERT_CLASS` is either the `BertTokenizer` class (to load the vocabulary) or one of the seven PyTorch model classes (to load the pre-trained weights): `BertModel`, `BertForMaskedLM`, `BertForNextSentencePrediction`, `BertForPreTraining`, `BertForSequenceClassification`, `BertForTokenClassification` or `BertForQuestionAnswering`, and +- `BERT_CLASS` is either the `BertTokenizer` class (to load the vocabulary) or one of the eight PyTorch model classes (to load the pre-trained weights): `BertModel`, `BertForMaskedLM`, `BertForNextSentencePrediction`, `BertForPreTraining`, `BertForSequenceClassification`, `BertForTokenClassification`, `BertForMultipleChoice` or `BertForQuestionAnswering`, and - `PRE_TRAINED_MODEL_NAME_OR_PATH` is either: - the shortcut name of a Google AI's pre-trained model selected in the list: @@ -353,14 +353,13 @@ The optimizer accepts the following arguments: BERT-base and BERT-large are respectively 110M and 340M parameters models and it can be difficult to fine-tune them on a single GPU with the recommended batch size for good performance (in most case a batch size of 32). -To help with fine-tuning these models, we have included five techniques that you can activate in the fine-tuning scripts [`run_classifier.py`](./examples/run_classifier.py) and [`run_squad.py`](./examples/run_squad.py): gradient-accumulation, multi-gpu training, distributed training, optimize on CPU and 16-bits training . For more details on how to use these techniques you can read [the tips on training large batches in PyTorch](https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255) that I published earlier this month. +To help with fine-tuning these models, we have included several techniques that you can activate in the fine-tuning scripts [`run_classifier.py`](./examples/run_classifier.py) and [`run_squad.py`](./examples/run_squad.py): gradient-accumulation, multi-gpu training, distributed training and 16-bits training . For more details on how to use these techniques you can read [the tips on training large batches in PyTorch](https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255) that I published earlier this month. Here is how to use these techniques in our scripts: - **Gradient Accumulation**: Gradient accumulation can be used by supplying a integer greater than 1 to the `--gradient_accumulation_steps` argument. The batch at each step will be divided by this integer and gradient will be accumulated over `gradient_accumulation_steps` steps. - **Multi-GPU**: Multi-GPU is automatically activated when several GPUs are detected and the batches are splitted over the GPUs. - **Distributed training**: Distributed training can be activated by supplying an integer greater or equal to 0 to the `--local_rank` argument (see below). -- **Optimize on CPU**: The Adam optimizer stores 2 moving average of the weights of the model. If you keep them on GPU 1 (typical behavior), your first GPU will have to store 3-times the size of the model. This is not optimal for large models like `BERT-large` and means your batch size is a lot lower than it could be. This option will perform the optimization and store the averages on the CPU/RAM to free more room on the GPU(s). As the most computational intensive operation is usually the backward pass, this doesn't have a significant impact on the training time. Activate this option with `--optimize_on_cpu` on the [`run_squad.py`](./examples/run_squad.py) script. - **16-bits training**: 16-bits training, also called mixed-precision training, can reduce the memory requirement of your model on the GPU by using half-precision training, basically allowing to double the batch size. If you have a recent GPU (starting from NVIDIA Volta architecture) you should see no decrease in speed. A good introduction to Mixed precision training can be found [here](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) and a full documentation is [here](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html). In our scripts, this option can be activated by setting the `--fp16` flag and you can play with loss scaling using the `--loss_scaling` flag (see the previously linked documentation for details on loss scaling). If the loss scaling is too high (`Nan` in the gradients) it will be automatically scaled down until the value is acceptable. The default loss scaling is 128 which behaved nicely in our tests. Note: To use *Distributed Training*, you will need to run one training script on each of your machines. This can be done for example by running the following command on each server (see [the above mentioned blog post]((https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255)) for more details): @@ -371,16 +370,21 @@ Where `$THIS_MACHINE_INDEX` is an sequential index assigned to each of your mach ### Fine-tuning with BERT: running the examples -We showcase the same examples as [the original implementation](https://github.com/google-research/bert/): fine-tuning a sequence-level classifier on the MRPC classification corpus and a token-level classifier on the question answering dataset SQuAD. +We showcase several fine-tuning examples based on (and extended from) [the original implementation](https://github.com/google-research/bert/): -Before running these examples you should download the +- a sequence-level classifier on the MRPC classification corpus, +- a token-level classifier on the question answering dataset SQuAD, and +- a sequence-level multiple-choice classifier on the SWAG classification corpus. + +#### MRPC + +This example code fine-tunes BERT on the Microsoft Research Paraphrase +Corpus (MRPC) corpus and runs in less than 10 minutes on a single K-80 and in 27 seconds (!) on single tesla V100 16GB with apex installed. + +Before running this example you should download the [GLUE data](https://gluebenchmark.com/tasks) by running [this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e) -and unpack it to some directory `$GLUE_DIR`. Please also download the `BERT-Base` -checkpoint, unzip it to some directory `$BERT_BASE_DIR`, and convert it to its PyTorch version as explained in the previous section. - -This example code fine-tunes `BERT-Base` on the Microsoft Research Paraphrase -Corpus (MRPC) corpus and runs in less than 10 minutes on a single K-80. +and unpack it to some directory `$GLUE_DIR`. ```shell export GLUE_DIR=/path/to/glue @@ -401,7 +405,29 @@ python run_classifier.py \ Our test ran on a few seeds with [the original implementation hyper-parameters](https://github.com/google-research/bert#sentence-and-sentence-pair-classification-tasks) gave evaluation results between 84% and 88%. -The second example fine-tunes `BERT-Base` on the SQuAD question answering task. +**Fast run with apex and 16 bit precision: fine-tuning on MRPC in 27 seconds!** +First install apex as indicated [here](https://github.com/NVIDIA/apex). +Then run +```shell +export GLUE_DIR=/path/to/glue + +python run_classifier.py \ + --task_name MRPC \ + --do_train \ + --do_eval \ + --do_lower_case \ + --data_dir $GLUE_DIR/MRPC/ \ + --bert_model bert-base-uncased \ + --max_seq_length 128 \ + --train_batch_size 32 \ + --learning_rate 2e-5 \ + --num_train_epochs 3.0 \ + --output_dir /tmp/mrpc_output/ +``` + +#### SQuAD + +This example code fine-tunes BERT on the SQuAD dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large) on single tesla V100 16GB. The data for SQuAD can be downloaded with the following links and should be saved in a `$SQUAD_DIR` directory. @@ -451,7 +477,7 @@ python run_swag.py \ --gradient_accumulation_steps 4 ``` -Training with the previous hyper-parameters gave us the following results: +Training with the previous hyper-parameters on a single GPU gave us the following results: ``` eval_accuracy = 0.8062081375587323 eval_loss = 0.5966546792367169 diff --git a/examples/run_classifier.py b/examples/run_classifier.py index aca099daf243..ccb25bfcd8c8 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -558,6 +558,9 @@ def main(): # Load a trained model that you have fine-tuned model_state_dict = torch.load(output_model_file) model = BertForSequenceClassification.from_pretrained(args.bert_model, state_dict=model_state_dict) + if args.fp16: + model.half() + model.to(device) if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = processor.get_dev_examples(args.data_dir) diff --git a/examples/run_squad.py b/examples/run_squad.py index 147cd60f29cd..fb8cd8ddc68c 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -923,6 +923,9 @@ def main(): # Load a trained model that you have fine-tuned model_state_dict = torch.load(output_model_file) model = BertForQuestionAnswering.from_pretrained(args.bert_model, state_dict=model_state_dict) + if args.fp16: + model.half() + model.to(device) if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = read_squad_examples( diff --git a/examples/run_swag.py b/examples/run_swag.py index d1ebb7354248..8d2ab40f8836 100644 --- a/examples/run_swag.py +++ b/examples/run_swag.py @@ -366,8 +366,7 @@ def main(): # Prepare model model = BertForMultipleChoice.from_pretrained(args.bert_model, cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank), - num_choices = 4 - ) + num_choices=4) if args.fp16: model.half() model.to(device) @@ -452,6 +451,9 @@ def main(): loss = loss * args.loss_scale if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps + tr_loss += loss.item() + nb_tr_examples += input_ids.size(0) + nb_tr_steps += 1 if args.fp16: optimizer.backward(loss) @@ -466,6 +468,20 @@ def main(): optimizer.zero_grad() global_step += 1 + # Save a trained model + model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self + output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") + torch.save(model_to_save.state_dict(), output_model_file) + + # Load a trained model that you have fine-tuned + model_state_dict = torch.load(output_model_file) + model = BertForMultipleChoice.from_pretrained(args.bert_model, + state_dict=model_state_dict, + num_choices=4) + if args.fp16: + model.half() + model.to(device) + if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = read_swag_examples(os.path.join(args.data_dir, 'val.csv'), is_training = True) eval_features = convert_examples_to_features( From e1eab59aac9fdf5ebc062b1afe1d218d11d3a3d2 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Thu, 13 Dec 2018 14:54:02 +0100 Subject: [PATCH 067/111] no fp16 on evaluation --- examples/run_classifier.py | 2 -- examples/run_squad.py | 2 -- examples/run_swag.py | 2 -- 3 files changed, 6 deletions(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index ccb25bfcd8c8..e1dcd363446a 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -558,8 +558,6 @@ def main(): # Load a trained model that you have fine-tuned model_state_dict = torch.load(output_model_file) model = BertForSequenceClassification.from_pretrained(args.bert_model, state_dict=model_state_dict) - if args.fp16: - model.half() model.to(device) if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): diff --git a/examples/run_squad.py b/examples/run_squad.py index fb8cd8ddc68c..d6e96f4ac9c0 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -923,8 +923,6 @@ def main(): # Load a trained model that you have fine-tuned model_state_dict = torch.load(output_model_file) model = BertForQuestionAnswering.from_pretrained(args.bert_model, state_dict=model_state_dict) - if args.fp16: - model.half() model.to(device) if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0): diff --git a/examples/run_swag.py b/examples/run_swag.py index 8d2ab40f8836..bedfff0b132c 100644 --- a/examples/run_swag.py +++ b/examples/run_swag.py @@ -478,8 +478,6 @@ def main(): model = BertForMultipleChoice.from_pretrained(args.bert_model, state_dict=model_state_dict, num_choices=4) - if args.fp16: - model.half() model.to(device) if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): From ae88eb88a4baffdd23fa38acf7493aedd23fa6b5 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 14 Dec 2018 13:48:58 +0100 Subject: [PATCH 068/111] set encoding to 'utf-8' in calls to open --- examples/extract_features.py | 2 +- examples/run_classifier.py | 5 +++-- examples/run_squad.py | 4 ++-- examples/run_swag.py | 5 +++-- pytorch_pretrained_bert/file_utils.py | 2 +- pytorch_pretrained_bert/modeling.py | 4 ++-- setup.py | 2 +- 7 files changed, 13 insertions(+), 11 deletions(-) diff --git a/examples/extract_features.py b/examples/extract_features.py index dbab934c0813..4f8812121ea1 100644 --- a/examples/extract_features.py +++ b/examples/extract_features.py @@ -168,7 +168,7 @@ def read_examples(input_file): """Read a list of `InputExample`s from an input file.""" examples = [] unique_id = 0 - with open(input_file, "r") as reader: + with open(input_file, "r", encoding='utf-8') as reader: while True: line = reader.readline() if not line: diff --git a/examples/run_classifier.py b/examples/run_classifier.py index e1dcd363446a..adf81f4e28b6 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -91,7 +91,7 @@ def get_labels(self): @classmethod def _read_tsv(cls, input_file, quotechar=None): """Reads a tab separated value file.""" - with open(input_file, "r") as f: + with open(input_file, "r", encoding='utf-8') as f: reader = csv.reader(f, delimiter="\t", quotechar=quotechar) lines = [] for line in reader: @@ -413,7 +413,8 @@ def main(): n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') - logger.info("device %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1)) + logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format( + device, n_gpu, bool(args.local_rank != -1), args.fp16)) if args.gradient_accumulation_steps < 1: raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format( diff --git a/examples/run_squad.py b/examples/run_squad.py index d6e96f4ac9c0..6a97dd300b0e 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -108,7 +108,7 @@ def __init__(self, def read_squad_examples(input_file, is_training): """Read a SQuAD json file into a list of SquadExample.""" - with open(input_file, "r") as reader: + with open(input_file, "r", encoding='utf-8') as reader: input_data = json.load(reader)["data"] def is_whitespace(c): @@ -757,7 +757,7 @@ def main(): n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') - logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits trainiing: {}".format( + logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format( device, n_gpu, bool(args.local_rank != -1), args.fp16)) if args.gradient_accumulation_steps < 1: diff --git a/examples/run_swag.py b/examples/run_swag.py index bedfff0b132c..caddbee8ab7a 100644 --- a/examples/run_swag.py +++ b/examples/run_swag.py @@ -100,7 +100,7 @@ def __init__(self, def read_swag_examples(input_file, is_training): - with open(input_file, 'r') as f: + with open(input_file, 'r', encoding='utf-8') as f: reader = csv.reader(f) lines = list(reader) @@ -333,7 +333,8 @@ def main(): n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') - logger.info("device %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1)) + logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format( + device, n_gpu, bool(args.local_rank != -1), args.fp16)) if args.gradient_accumulation_steps < 1: raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format( diff --git a/pytorch_pretrained_bert/file_utils.py b/pytorch_pretrained_bert/file_utils.py index 139418f1a544..43fa8ca87e20 100644 --- a/pytorch_pretrained_bert/file_utils.py +++ b/pytorch_pretrained_bert/file_utils.py @@ -227,7 +227,7 @@ def read_set_from_file(filename: str) -> Set[str]: Expected file format is one item per line. ''' collection = set() - with open(filename, 'r') as file_: + with open(filename, 'r', encoding='utf-8') as file_: for line in file_: collection.add(line.rstrip()) return collection diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index c6940c74eb2d..28f22287d258 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -106,7 +106,7 @@ def __init__(self, initializing all weight matrices. """ if isinstance(vocab_size_or_config_json_file, str): - with open(vocab_size_or_config_json_file, "r") as reader: + with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader: json_config = json.loads(reader.read()) for key, value in json_config.items(): self.__dict__[key] = value @@ -137,7 +137,7 @@ def from_dict(cls, json_object): @classmethod def from_json_file(cls, json_file): """Constructs a `BertConfig` from a json file of parameters.""" - with open(json_file, "r") as reader: + with open(json_file, "r", encoding='utf-8') as reader: text = reader.read() return cls.from_dict(json.loads(text)) diff --git a/setup.py b/setup.py index a1e1f68db619..dbfeb2c6948e 100644 --- a/setup.py +++ b/setup.py @@ -41,7 +41,7 @@ author="Thomas Wolf, Victor Sanh, Tim Rault, Google AI Language Team Authors", author_email="thomas@huggingface.co", description="PyTorch version of Google AI BERT model with script to load Google pre-trained models", - long_description=open("README.md", "r").read(), + long_description=open("README.md", "r", encoding='utf-8').read(), long_description_content_type="text/markdown", keywords='BERT NLP deep learning google', license='Apache', From 4a4b0e57831aca4294ee1b2924375ed587bd345b Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 14 Dec 2018 14:46:25 +0100 Subject: [PATCH 069/111] remove logging. basicConfig from library code --- pytorch_pretrained_bert/modeling.py | 3 --- pytorch_pretrained_bert/tokenization.py | 3 --- 2 files changed, 6 deletions(-) diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index 28f22287d258..acdc741f6da4 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -34,9 +34,6 @@ from .file_utils import cached_path -logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', - datefmt = '%m/%d/%Y %H:%M:%S', - level = logging.INFO) logger = logging.getLogger(__name__) PRETRAINED_MODEL_ARCHIVE_MAP = { diff --git a/pytorch_pretrained_bert/tokenization.py b/pytorch_pretrained_bert/tokenization.py index c7ef20ddefcb..5954b78f6833 100644 --- a/pytorch_pretrained_bert/tokenization.py +++ b/pytorch_pretrained_bert/tokenization.py @@ -25,9 +25,6 @@ from .file_utils import cached_path -logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', - datefmt = '%m/%d/%Y %H:%M:%S', - level = logging.INFO) logger = logging.getLogger(__name__) PRETRAINED_VOCAB_ARCHIVE_MAP = { From 37378898a204c91e4aa47f163dd635a06c437628 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 14 Dec 2018 15:02:32 +0100 Subject: [PATCH 070/111] adding DockerFile --- docker/Dockerfile | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 docker/Dockerfile diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 000000000000..e47eb548f9a1 --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,7 @@ +FROM pytorch/pytorch:latest + +RUN git clone https://github.com/NVIDIA/apex.git && cd apex && python setup.py install --cuda_ext --cpp_ext + +RUN pip install pytorch-pretrained-bert + +WORKDIR /workspace \ No newline at end of file From d821358884e45e92164a7bc773e4bc47eed1b591 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Fri, 14 Dec 2018 15:15:17 +0100 Subject: [PATCH 071/111] update readme --- README.md | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index c4f6b99f1c4f..87afa88b5223 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ This implementation is provided with [Google's pre-trained models](https://githu ## Installation -This repo was tested on Python 3.6+ and PyTorch 0.4.1 +This repo was tested on Python 3.5+ and PyTorch 0.4.1/1.0.0 ### With pip @@ -372,9 +372,9 @@ Where `$THIS_MACHINE_INDEX` is an sequential index assigned to each of your mach We showcase several fine-tuning examples based on (and extended from) [the original implementation](https://github.com/google-research/bert/): -- a sequence-level classifier on the MRPC classification corpus, -- a token-level classifier on the question answering dataset SQuAD, and -- a sequence-level multiple-choice classifier on the SWAG classification corpus. +- a *sequence-level classifier* on the MRPC classification corpus, +- a *token-level classifier* on the question answering dataset SQuAD, and +- a *sequence-level multiple-choice classifier* on the SWAG classification corpus. #### MRPC @@ -427,7 +427,7 @@ python run_classifier.py \ #### SQuAD -This example code fine-tunes BERT on the SQuAD dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large) on single tesla V100 16GB. +This example code fine-tunes BERT on the SQuAD dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large) on a single tesla V100 16GB. The data for SQuAD can be downloaded with the following links and should be saved in a `$SQUAD_DIR` directory. @@ -458,7 +458,9 @@ Training with the previous hyper-parameters gave us the following results: {"f1": 88.52381567990474, "exact_match": 81.22043519394512} ``` -The data for Swag can be downloaded by cloning the following [repository](https://github.com/rowanz/swagaf) +#### SWAG + +The data for SWAG can be downloaded by cloning the following [repository](https://github.com/rowanz/swagaf) ```shell export SWAG_DIR=/path/to/SWAG From 8809eb6c93c55c86ab69df7adbfdeb4d2e753f0e Mon Sep 17 00:00:00 2001 From: Thomas Wolf Date: Fri, 14 Dec 2018 16:59:39 +0100 Subject: [PATCH 072/111] update readme with information on NVIDIA's apex --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 87afa88b5223..5962dfca66d8 100644 --- a/README.md +++ b/README.md @@ -360,7 +360,9 @@ Here is how to use these techniques in our scripts: - **Gradient Accumulation**: Gradient accumulation can be used by supplying a integer greater than 1 to the `--gradient_accumulation_steps` argument. The batch at each step will be divided by this integer and gradient will be accumulated over `gradient_accumulation_steps` steps. - **Multi-GPU**: Multi-GPU is automatically activated when several GPUs are detected and the batches are splitted over the GPUs. - **Distributed training**: Distributed training can be activated by supplying an integer greater or equal to 0 to the `--local_rank` argument (see below). -- **16-bits training**: 16-bits training, also called mixed-precision training, can reduce the memory requirement of your model on the GPU by using half-precision training, basically allowing to double the batch size. If you have a recent GPU (starting from NVIDIA Volta architecture) you should see no decrease in speed. A good introduction to Mixed precision training can be found [here](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) and a full documentation is [here](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html). In our scripts, this option can be activated by setting the `--fp16` flag and you can play with loss scaling using the `--loss_scaling` flag (see the previously linked documentation for details on loss scaling). If the loss scaling is too high (`Nan` in the gradients) it will be automatically scaled down until the value is acceptable. The default loss scaling is 128 which behaved nicely in our tests. +- **16-bits training**: 16-bits training, also called mixed-precision training, can reduce the memory requirement of your model on the GPU by using half-precision training, basically allowing to double the batch size. If you have a recent GPU (starting from NVIDIA Volta architecture) you should see no decrease in speed. A good introduction to Mixed precision training can be found [here](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) and a full documentation is [here](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html). In our scripts, this option can be activated by setting the `--fp16` flag and you can play with loss scaling using the `--loss_scale` flag (see the previously linked documentation for details on loss scaling). The loss scale can be zero in which case the scale is dynamically adjuted or a positive power of two in which case the scaling is static. + +To use 16-bits training and distributed training, you need to install NVIDIA's apex extension [as detailed here](https://github.com/nvidia/apex). You will find more information reguarding the internals of `apex` and how to use `apex` in [the doc and the associated repository](https://github.com/nvidia/apex). The results of the tests perfomed on pytorch-BERT by the NVIDIA team (and my trials at reproducing them) can be consulted in [the relevant PR of the present repository](https://github.com/huggingface/pytorch-pretrained-BERT/pull/116). Note: To use *Distributed Training*, you will need to run one training script on each of your machines. This can be done for example by running the following command on each server (see [the above mentioned blog post]((https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255)) for more details): ```bash From 8b1b93947f6e18c9dd2ef4067c286b09c27a6e28 Mon Sep 17 00:00:00 2001 From: Daniel Khashabi Date: Fri, 14 Dec 2018 14:10:36 -0500 Subject: [PATCH 073/111] Minor fix. --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index 5962dfca66d8..2dce5fcbb97d 100644 --- a/README.md +++ b/README.md @@ -517,8 +517,7 @@ python ./run_squad.py \ --doc_stride 128 \ --output_dir $OUTPUT_DIR \ --train_batch_size 24 \ - --gradient_accumulation_steps 2 \ - --optimize_on_cpu + --gradient_accumulation_steps 2 ``` If you have a recent GPU (starting from NVIDIA Volta series), you should try **16-bit fine-tuning** (FP16). From 786cc41299510ef8ee9973519d202b6ae683a293 Mon Sep 17 00:00:00 2001 From: Thomas Wolf Date: Mon, 17 Dec 2018 09:22:18 +0100 Subject: [PATCH 074/111] Typos in readme --- README.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 2dce5fcbb97d..d0fd120967dd 100644 --- a/README.md +++ b/README.md @@ -165,7 +165,7 @@ Here is a detailed documentation of the classes in the package and how to use th To load one of Google AI's pre-trained models or a PyTorch saved model (an instance of `BertForPreTraining` saved with `torch.save()`), the PyTorch model classes and the tokenizer can be instantiated as ```python -model = BERT_CLASS.from_pretrain(PRE_TRAINED_MODEL_NAME_OR_PATH, cache_dir=None) +model = BERT_CLASS.from_pretrained(PRE_TRAINED_MODEL_NAME_OR_PATH, cache_dir=None) ``` where @@ -189,14 +189,15 @@ where - `pytorch_model.bin` a PyTorch dump of a pre-trained instance `BertForPreTraining` (saved with the usual `torch.save()`) If `PRE_TRAINED_MODEL_NAME_OR_PATH` is a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the links [here](pytorch_pretrained_bert/modeling.py)) and stored in a cache folder to avoid future download (the cache folder can be found at `~/.pytorch_pretrained_bert/`). -- `cache_dir` can be an optional path to a specific directory to download and cache the pre-trained model weights. This option is useful in particular when you are using distributed training: to avoid concurrent access to the same weights you can set for example `cache_dir='./pretrained_model_{}'.format(args.local_rank)` (see the section on distributed training for more information) +- `cache_dir` can be an optional path to a specific directory to download and cache the pre-trained model weights. This option is useful in particular when you are using distributed training: to avoid concurrent access to the same weights you can set for example `cache_dir='./pretrained_model_{}'.format(args.local_rank)` (see the section on distributed training for more information). `Uncased` means that the text has been lowercased before WordPiece tokenization, e.g., `John Smith` becomes `john smith`. The Uncased model also strips out any accent markers. `Cased` means that the true case and accent markers are preserved. Typically, the Uncased model is better unless you know that case information is important for your task (e.g., Named Entity Recognition or Part-of-Speech tagging). For information about the Multilingual and Chinese model, see the [Multilingual README](https://github.com/google-research/bert/blob/master/multilingual.md) or the original TensorFlow repository. -**When using an `uncased model`, make sure to pass `--do_lower_case` to the training scripts. (Or pass `do_lower_case=True` directly to FullTokenizer if you're using your own script.)** +**When using an `uncased model`, make sure to pass `--do_lower_case` to the example training scripts (or pass `do_lower_case=True` to FullTokenizer if you're using your own script and loading the tokenizer your-self.).** Example: ```python +tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True) model = BertForSequenceClassification.from_pretrained('bert-base-uncased') ``` From a58361f197ec0d43ef28ce20fefd6dcb0c9c2ef7 Mon Sep 17 00:00:00 2001 From: deepset Date: Tue, 18 Dec 2018 10:32:25 +0100 Subject: [PATCH 075/111] Add example for fine tuning BERT language model (#1) Adds an example for loading a pre-trained BERT model and fine tune it as a language model (masked tokens & nextSentence) on your target corpus. --- examples/run_lm_finetuning.py | 674 ++++++++++++++++++++++++++++++++++ 1 file changed, 674 insertions(+) create mode 100644 examples/run_lm_finetuning.py diff --git a/examples/run_lm_finetuning.py b/examples/run_lm_finetuning.py new file mode 100644 index 000000000000..3e8bc36f48bf --- /dev/null +++ b/examples/run_lm_finetuning.py @@ -0,0 +1,674 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""BERT finetuning runner.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import logging +import argparse +from tqdm import tqdm, trange + +import numpy as np +import torch +from torch.utils.data import DataLoader, RandomSampler +from torch.utils.data.distributed import DistributedSampler + +from pytorch_pretrained_bert.tokenization import BertTokenizer +from pytorch_pretrained_bert.modeling import BertForPreTraining +from pytorch_pretrained_bert.optimization import BertAdam + +from torch.utils.data import Dataset +import random + +logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', + datefmt='%m/%d/%Y %H:%M:%S', + level=logging.INFO) +logger = logging.getLogger(__name__) + + +class BERTDataset(Dataset): + def __init__(self, corpus_path, tokenizer, seq_len, encoding="utf-8", corpus_lines=None, on_memory=True): + self.vocab = tokenizer.vocab + self.tokenizer = tokenizer + self.seq_len = seq_len + self.on_memory = on_memory + self.corpus_lines = corpus_lines # number of non-empty lines in input corpus + self.corpus_path = corpus_path + self.encoding = encoding + self.current_doc = 0 # to avoid random sentence from same doc + + # for loading samples directly from file + self.sample_counter = 0 # used to keep track of full epochs on file + self.line_buffer = None # keep second sentence of a pair in memory and use as first sentence in next pair + + # for loading samples in memory + self.current_random_doc = 0 + self.num_docs = 0 + + # load samples into memory + if on_memory: + self.all_docs = [] + doc = [] + self.corpus_lines = 0 + with open(corpus_path, "r", encoding=encoding) as f: + for line in tqdm(f, desc="Loading Dataset", total=corpus_lines): + line = line.strip() + if line == "": + self.all_docs.append(doc) + doc = [] + else: + doc.append(line) + self.corpus_lines = self.corpus_lines + 1 + # if last row in file is not empty + if self.all_docs[-1] != doc: + self.all_docs.append(doc) + + self.num_docs = len(self.all_docs) + + # load samples later lazily from disk + else: + if self.corpus_lines is None: + with open(corpus_path, "r", encoding=encoding) as f: + self.corpus_lines = 0 + for line in tqdm(f, desc="Loading Dataset", total=corpus_lines): + if line.strip() == "": + self.num_docs += 1 + else: + self.corpus_lines += 1 + + # if doc does not end with empty line + if line.strip() != "": + self.num_docs += 1 + + self.file = open(corpus_path, "r", encoding=encoding) + self.random_file = open(corpus_path, "r", encoding=encoding) + + def __len__(self): + # last line of doc won't be used, because there's no "nextSentence". Additionally, we start counting at 0. + return self.corpus_lines - self.num_docs - 1 + + def __getitem__(self, item): + cur_id = self.sample_counter + self.sample_counter += 1 + if not self.on_memory: + # after one epoch we start again from beginning of file + if cur_id != 0 and (cur_id % len(self) == 0): + self.file.close() + self.file = open(self.corpus_path, "r", encoding=self.encoding) + + t1, t2, is_next_label = self.random_sent(item) + + # tokenize + tokens_a = self.tokenizer.tokenize(t1) + tokens_b = self.tokenizer.tokenize(t2) + + # combine to one sample + cur_example = InputExample(guid=cur_id, tokens_a=tokens_a, tokens_b=tokens_b, is_next=is_next_label) + + # transform sample to features + cur_features = convert_example_to_features(cur_example, self.seq_len, self.tokenizer) + + cur_tensors = {"input_ids": torch.tensor(cur_features.input_ids), + "input_mask": torch.tensor(cur_features.input_mask), + "segment_ids": torch.tensor(cur_features.segment_ids), + "lm_label_ids": torch.tensor(cur_features.lm_label_ids), + "is_next": torch.tensor(cur_features.is_next)} + + return cur_tensors + + def random_sent(self, index): + """ + Get one sample from corpus consisting of two sentences. With prob. 50% these are two subsequent sentences + from one doc. With 50% the second sentence will be a random one from another doc. + :param index: int, index of sample. + :return: (str, str, int), sentence 1, sentence 2, isNextSentence Label + """ + t1, t2 = self.get_corpus_line(index) + if random.random() > 0.5: + label = 0 + else: + t2 = self.get_random_line() + label = 1 + + assert len(t1) > 0 + assert len(t2) > 0 + return t1, t2, label + + def get_corpus_line(self, item): + """ + Get one sample from corpus consisting of a pair of two subsequent lines from the same doc. + :param item: int, index of sample. + :return: (str, str), two subsequent sentences from corpus + """ + t1 = "" + t2 = "" + assert item < self.corpus_lines + if self.on_memory: + # get the right doc + doc_id = 0 + doc_start = 0 + doc_end = len(self.all_docs[doc_id]) - 2 + while item > doc_end: + doc_id += 1 + doc_start = doc_end + 1 + doc_end += len(self.all_docs[doc_id]) - 1 + # get the right line within doc + line_in_doc = item - doc_start + t1 = self.all_docs[doc_id][line_in_doc] + t2 = self.all_docs[doc_id][line_in_doc + 1] + # used later to avoid random nextSentence from same doc + self.current_doc = doc_id + return t1, t2 + else: + if self.line_buffer is None: + # read first non-empty line of file + while t1 == "" : + t1 = self.file.__next__().strip() + t2 = self.file.__next__().strip() + else: + # use t2 from previous iteration as new t1 + t1 = self.line_buffer + t2 = self.file.__next__().strip() + # skip empty rows that are used for separating documents and keep track of current doc id + while t2 == "" or t1 == "": + t1 = self.file.__next__().strip() + t2 = self.file.__next__().strip() + self.current_doc = self.current_doc+1 + self.line_buffer = t2 + + assert t1 != "" + assert t2 != "" + return t1, t2 + + def get_random_line(self): + """ + Get random line from another document for nextSentence task. + :return: str, content of one line + """ + # Similar to original tf repo: This outer loop should rarely go for more than one iteration for large + # corpora. However, just to be careful, we try to make sure that + # the random document is not the same as the document we're processing. + for _ in range(10): + if self.on_memory: + rand_doc_idx = random.randint(0, len(self.all_docs)-1) + rand_doc = self.all_docs[rand_doc_idx] + line = rand_doc[random.randrange(len(rand_doc))] + else: + rand_index = random.randint(1, self.corpus_lines if self.corpus_lines < 1000 else 1000) + #pick random line + for _ in range(rand_index): + line = self.get_next_line() + #check if our picked random line is really from another doc like we want it to be + if self.current_random_doc != self.current_doc: + break + return line + + def get_next_line(self): + """ Gets next line of random_file and starts over when reaching end of file""" + try: + line = self.random_file.__next__().strip() + #keep track of which document we are currently looking at to later avoid having the same doc as t1 + if line == "": + self.current_random_doc = self.current_random_doc + 1 + line = self.random_file.__next__().strip() + except StopIteration: + self.random_file.close() + self.random_file = open(self.corpus_path, "r", encoding=self.encoding) + line = self.random_file.__next__().strip() + return line + + +class InputExample(object): + """A single training/test example for the language model.""" + + def __init__(self, guid, tokens_a, tokens_b=None, is_next=None, lm_labels=None): + """Constructs a InputExample. + + Args: + guid: Unique id for the example. + tokens_a: string. The untokenized text of the first sequence. For single + sequence tasks, only this sequence must be specified. + tokens_b: (Optional) string. The untokenized text of the second sequence. + Only must be specified for sequence pair tasks. + label: (Optional) string. The label of the example. This should be + specified for train and dev examples, but not for test examples. + """ + self.guid = guid + self.tokens_a = tokens_a + self.tokens_b = tokens_b + self.is_next = is_next # nextSentence + self.lm_labels = lm_labels # masked words for language model + + +class InputFeatures(object): + """A single set of features of data.""" + + def __init__(self, input_ids, input_mask, segment_ids, is_next, lm_label_ids): + self.input_ids = input_ids + self.input_mask = input_mask + self.segment_ids = segment_ids + self.is_next = is_next + self.lm_label_ids = lm_label_ids + + +def random_word(tokens, tokenizer): + """ + Masking some random tokens for Language Model task with probabilities as in the original BERT paper. + :param tokens: list of str, tokenized sentence. + :param tokenizer: Tokenizer, object used for tokenization (we need it's vocab here) + :return: (list of str, list of int), masked tokens and related labels for LM prediction + """ + output_label = [] + + for i, token in enumerate(tokens): + prob = random.random() + # mask token with 15% probability + if prob < 0.15: + prob /= 0.15 + + # 80% randomly change token to mask token + if prob < 0.8: + tokens[i] = "[MASK]" + + # 10% randomly change token to random token + elif prob < 0.9: + tokens[i] = random.choice(list(tokenizer.vocab.items()))[0] + + # -> rest 10% randomly keep current token + + # append current token to output (we will predict these later) + try: + output_label.append(tokenizer.vocab[token]) + except KeyError: + # For unknown words (should not occur with BPE vocab) + output_label.append(tokenizer.vocab["[UNK]"]) + else: + # no masking token (will be ignored by loss function later) + output_label.append(-1) + + return tokens, output_label + + +def convert_example_to_features(example, max_seq_length, tokenizer): + """ + Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample with + IDs, LM labels, input_mask, CLS and SEP tokens etc. + :param example: InputExample, containing sentence input as strings and is_next label + :param max_seq_length: int, maximum length of sequence. + :param tokenizer: Tokenizer + :return: InputFeatures, containing all inputs and labels of one sample as IDs (as used for model training) + """ + tokens_a = example.tokens_a + tokens_b = example.tokens_b + # Modifies `tokens_a` and `tokens_b` in place so that the total + # length is less than the specified length. + # Account for [CLS], [SEP], [SEP] with "- 3" + _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) + + t1_random, t1_label = random_word(tokens_a, tokenizer) + t2_random, t2_label = random_word(tokens_b, tokenizer) + # concatenate lm labels and account for CLS, SEP, SEP + lm_label_ids = ([-1] + t1_label + [-1] + t2_label + [-1]) + + # The convention in BERT is: + # (a) For sequence pairs: + # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] + # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 + # (b) For single sequences: + # tokens: [CLS] the dog is hairy . [SEP] + # type_ids: 0 0 0 0 0 0 0 + # + # Where "type_ids" are used to indicate whether this is the first + # sequence or the second sequence. The embedding vectors for `type=0` and + # `type=1` were learned during pre-training and are added to the wordpiece + # embedding vector (and position vector). This is not *strictly* necessary + # since the [SEP] token unambigiously separates the sequences, but it makes + # it easier for the model to learn the concept of sequences. + # + # For classification tasks, the first vector (corresponding to [CLS]) is + # used as as the "sentence vector". Note that this only makes sense because + # the entire model is fine-tuned. + tokens = [] + segment_ids = [] + tokens.append("[CLS]") + segment_ids.append(0) + for token in tokens_a: + tokens.append(token) + segment_ids.append(0) + tokens.append("[SEP]") + segment_ids.append(0) + + assert len(tokens_b) > 0 + for token in tokens_b: + tokens.append(token) + segment_ids.append(1) + tokens.append("[SEP]") + segment_ids.append(1) + + input_ids = tokenizer.convert_tokens_to_ids(tokens) + + # The mask has 1 for real tokens and 0 for padding tokens. Only real + # tokens are attended to. + input_mask = [1] * len(input_ids) + + # Zero-pad up to the sequence length. + while len(input_ids) < max_seq_length: + input_ids.append(0) + input_mask.append(0) + segment_ids.append(0) + lm_label_ids.append(-1) + + assert len(input_ids) == max_seq_length + assert len(input_mask) == max_seq_length + assert len(segment_ids) == max_seq_length + assert len(lm_label_ids) == max_seq_length + + if example.guid < 5: + logger.info("*** Example ***") + logger.info("guid: %s" % (example.guid)) + logger.info("tokens: %s" % " ".join( + [str(x) for x in tokens])) + logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) + logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) + logger.info( + "segment_ids: %s" % " ".join([str(x) for x in segment_ids])) + logger.info("LM label: %s " % (lm_label_ids)) + logger.info("Is next sentence label: %s " % (example.is_next)) + + features = InputFeatures(input_ids=input_ids, + input_mask=input_mask, + segment_ids=segment_ids, + lm_label_ids=lm_label_ids, + is_next=example.is_next) + return features + + +def main(): + parser = argparse.ArgumentParser() + + ## Required parameters + parser.add_argument("--train_file", + default=None, + type=str, + required=True, + help="The input train corpus.") + parser.add_argument("--bert_model", default=None, type=str, required=True, + help="Bert pre-trained model selected in the list: bert-base-uncased, " + "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.") + parser.add_argument("--output_dir", + default=None, + type=str, + required=True, + help="The output directory where the model checkpoints will be written.") + + ## Other parameters + parser.add_argument("--max_seq_length", + default=128, + type=int, + help="The maximum total input sequence length after WordPiece tokenization. \n" + "Sequences longer than this will be truncated, and sequences shorter \n" + "than this will be padded.") + parser.add_argument("--do_train", + default=False, + action='store_true', + help="Whether to run training.") + parser.add_argument("--train_batch_size", + default=32, + type=int, + help="Total batch size for training.") + parser.add_argument("--eval_batch_size", + default=8, + type=int, + help="Total batch size for eval.") + parser.add_argument("--learning_rate", + default=3e-5, + type=float, + help="The initial learning rate for Adam.") + parser.add_argument("--num_train_epochs", + default=3.0, + type=float, + help="Total number of training epochs to perform.") + parser.add_argument("--warmup_proportion", + default=0.1, + type=float, + help="Proportion of training to perform linear learning rate warmup for. " + "E.g., 0.1 = 10%% of training.") + parser.add_argument("--no_cuda", + default=False, + action='store_true', + help="Whether not to use CUDA when available") + parser.add_argument("--on_memory", + default=False, + action='store_true', + help="Whether to load train samples into memory or use disk") + parser.add_argument("--local_rank", + type=int, + default=-1, + help="local_rank for distributed training on gpus") + parser.add_argument('--seed', + type=int, + default=42, + help="random seed for initialization") + parser.add_argument('--gradient_accumulation_steps', + type=int, + default=1, + help="Number of updates steps to accumualte before performing a backward/update pass.") + parser.add_argument('--optimize_on_cpu', + default=False, + action='store_true', + help="Whether to perform optimization and keep the optimizer averages on CPU") + parser.add_argument('--fp16', + default=False, + action='store_true', + help="Whether to use 16-bit float precision instead of 32-bit") + parser.add_argument('--loss_scale', + type=float, default=128, + help='Loss scaling, positive power of 2 values can improve fp16 convergence.') + + args = parser.parse_args() + + if args.local_rank == -1 or args.no_cuda: + device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") + n_gpu = torch.cuda.device_count() + else: + device = torch.device("cuda", args.local_rank) + n_gpu = 1 + # Initializes the distributed backend which will take care of sychronizing nodes/GPUs + torch.distributed.init_process_group(backend='nccl') + if args.fp16: + logger.info("16-bits training currently not supported in distributed training") + args.fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496) + logger.info("device %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1)) + + if args.gradient_accumulation_steps < 1: + raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format( + args.gradient_accumulation_steps)) + + args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps) + + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if n_gpu > 0: + torch.cuda.manual_seed_all(args.seed) + + if not args.do_train and not args.do_eval: + raise ValueError("At least one of `do_train` or `do_eval` must be True.") + + if os.path.exists(args.output_dir) and os.listdir(args.output_dir): + raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) + os.makedirs(args.output_dir, exist_ok=True) + + tokenizer = BertTokenizer.from_pretrained(args.bert_model) + + #train_examples = None + num_train_steps = None + if args.do_train: + print("Loading Train Dataset", args.train_file) + train_dataset = BERTDataset(args.train_file, tokenizer, seq_len=args.max_seq_length, + corpus_lines=None, on_memory=args.on_memory) + num_train_steps = int( + len(train_dataset) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) + + # Prepare model + model = BertForPreTraining.from_pretrained(args.bert_model) + if args.fp16: + model.half() + model.to(device) + if args.local_rank != -1: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], + output_device=args.local_rank) + elif n_gpu > 1: + model = torch.nn.DataParallel(model) + + # Prepare optimizer + if args.fp16: + param_optimizer = [(n, param.clone().detach().to('cpu').float().requires_grad_()) \ + for n, param in model.named_parameters()] + elif args.optimize_on_cpu: + param_optimizer = [(n, param.clone().detach().to('cpu').requires_grad_()) \ + for n, param in model.named_parameters()] + else: + param_optimizer = list(model.named_parameters()) + no_decay = ['bias', 'gamma', 'beta'] + optimizer_grouped_parameters = [ + {'params': [p for n, p in param_optimizer if n not in no_decay], 'weight_decay_rate': 0.01}, + {'params': [p for n, p in param_optimizer if n in no_decay], 'weight_decay_rate': 0.0} + ] + optimizer = BertAdam(optimizer_grouped_parameters, + lr=args.learning_rate, + warmup=args.warmup_proportion, + t_total=num_train_steps) + + global_step = 0 + if args.do_train: + logger.info("***** Running training *****") + logger.info(" Num examples = %d", len(train_dataset)) + logger.info(" Batch size = %d", args.train_batch_size) + logger.info(" Num steps = %d", num_train_steps) + + if args.local_rank == -1: + train_sampler = RandomSampler(train_dataset) + else: + #TODO: check if this works with current data generator from disk that relies on file.__next__ + # (it doesn't return item back by index) + train_sampler = DistributedSampler(train_dataset) + train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) + + model.train() + for _ in trange(int(args.num_train_epochs), desc="Epoch"): + tr_loss = 0 + nb_tr_examples, nb_tr_steps = 0, 0 + for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): + batch = tuple(t.to(device) for t in batch.values()) + input_ids, input_mask, segment_ids, lm_label_ids, is_next = batch + loss = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next) + if n_gpu > 1: + loss = loss.mean() # mean() to average on multi-gpu. + if args.fp16 and args.loss_scale != 1.0: + # rescale loss for fp16 training + # see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html + loss = loss * args.loss_scale + if args.gradient_accumulation_steps > 1: + loss = loss / args.gradient_accumulation_steps + loss.backward() + tr_loss += loss.item() + nb_tr_examples += input_ids.size(0) + nb_tr_steps += 1 + if (step + 1) % args.gradient_accumulation_steps == 0: + if args.fp16 or args.optimize_on_cpu: + if args.fp16 and args.loss_scale != 1.0: + # scale down gradients for fp16 training + for param in model.parameters(): + param.grad.data = param.grad.data / args.loss_scale + is_nan = set_optimizer_params_grad(param_optimizer, model.named_parameters(), test_nan=True) + if is_nan: + logger.info("FP16 TRAINING: Nan in gradients, reducing loss scaling") + args.loss_scale = args.loss_scale / 2 + model.zero_grad() + continue + optimizer.step() + copy_optimizer_params_to_model(model.named_parameters(), param_optimizer) + else: + optimizer.step() + model.zero_grad() + global_step += 1 + + logger.info("** ** * Saving fine - tuned model ** ** * ") + output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") + if n_gpu > 1: + torch.save(model.module.bert.state_dict(), output_model_file) + else: + torch.save(model.bert.state_dict(), output_model_file) + + +def _truncate_seq_pair(tokens_a, tokens_b, max_length): + """Truncates a sequence pair in place to the maximum length.""" + + # This is a simple heuristic which will always truncate the longer sequence + # one token at a time. This makes more sense than truncating an equal percent + # of tokens from each, since if one sequence is very short then each token + # that's truncated likely contains more information than a longer sequence. + while True: + total_length = len(tokens_a) + len(tokens_b) + if total_length <= max_length: + break + if len(tokens_a) > len(tokens_b): + tokens_a.pop() + else: + tokens_b.pop() + + +def accuracy(out, labels): + outputs = np.argmax(out, axis=1) + return np.sum(outputs == labels) + + +def copy_optimizer_params_to_model(named_params_model, named_params_optimizer): + """ Utility function for optimize_on_cpu and 16-bits training. + Copy the parameters optimized on CPU/RAM back to the model on GPU + """ + for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model): + if name_opti != name_model: + logger.error("name_opti != name_model: {} {}".format(name_opti, name_model)) + raise ValueError + param_model.data.copy_(param_opti.data) + + +def set_optimizer_params_grad(named_params_optimizer, named_params_model, test_nan=False): + """ Utility function for optimize_on_cpu and 16-bits training. + Copy the gradient of the GPU parameters to the CPU/RAMM copy of the model + """ + is_nan = False + for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model): + if name_opti != name_model: + logger.error("name_opti != name_model: {} {}".format(name_opti, name_model)) + raise ValueError + if param_model.grad is not None: + if test_nan and torch.isnan(param_model.grad).sum() > 0: + is_nan = True + if param_opti.grad is None: + param_opti.grad = torch.nn.Parameter(param_opti.data.new().resize_(*param_opti.data.size())) + param_opti.grad.data.copy_(param_model.grad.data) + else: + param_opti.grad = None + return is_nan + + +if __name__ == "__main__": + main() \ No newline at end of file From 78cf7b4ab4de783942383b008be7eb7f65dc541d Mon Sep 17 00:00:00 2001 From: Patrick Lewis Date: Tue, 18 Dec 2018 14:41:30 +0000 Subject: [PATCH 076/111] added code to raise value error for bert tokenizer for covert_tokens_to_indices --- pytorch_pretrained_bert/tokenization.py | 44 ++++++++++++++++++------- tests/tokenization_test.py | 22 +++++++++++-- 2 files changed, 53 insertions(+), 13 deletions(-) diff --git a/pytorch_pretrained_bert/tokenization.py b/pytorch_pretrained_bert/tokenization.py index 5954b78f6833..838401565b6a 100644 --- a/pytorch_pretrained_bert/tokenization.py +++ b/pytorch_pretrained_bert/tokenization.py @@ -36,6 +36,15 @@ 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt", 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt", } +PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = { + 'bert-base-uncased': 512, + 'bert-large-uncased': 512, + 'bert-base-cased': 512, + 'bert-large-cased': 512, + 'bert-base-multilingual-uncased': 512, + 'bert-base-multilingual-cased': 512, + 'bert-base-chinese': 512, +} VOCAB_NAME = 'vocab.txt' @@ -65,7 +74,8 @@ def whitespace_tokenize(text): class BertTokenizer(object): """Runs end-to-end tokenization: punctuation splitting + wordpiece""" - def __init__(self, vocab_file, do_lower_case=True): + + def __init__(self, vocab_file, do_lower_case=True, max_len=None): if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained " @@ -75,6 +85,7 @@ def __init__(self, vocab_file, do_lower_case=True): [(ids, tok) for tok, ids in self.vocab.items()]) self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) + self.max_len = max_len if max_len is not None else int(1e12) def tokenize(self, text): split_tokens = [] @@ -88,6 +99,12 @@ def convert_tokens_to_ids(self, tokens): ids = [] for token in tokens: ids.append(self.vocab[token]) + if len(ids) > self.max_len: + raise ValueError( + "Token indices sequence length is longer than the specified maximum " + " sequence length for this BERT model ({} > {}). Running this" + " sequence through BERT will result in indexing errors".format(len(ids), self.max_len) + ) return ids def convert_ids_to_tokens(self, ids): @@ -126,6 +143,11 @@ def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwarg else: logger.info("loading vocabulary file {} from cache at {}".format( vocab_file, resolved_vocab_file)) + if pretrained_model_name in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP: + # if we're using a pretrained model, ensure the tokenizer wont index sequences longer + # than the number of positional embeddings + max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name] + kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len) # Instantiate tokenizer. tokenizer = cls(resolved_vocab_file, *inputs, **kwargs) return tokenizer @@ -193,7 +215,7 @@ def _run_split_on_punc(self, text): i += 1 return ["".join(x) for x in output] - + def _tokenize_chinese_chars(self, text): """Adds whitespace around any CJK character.""" output = [] @@ -218,17 +240,17 @@ def _is_chinese_char(self, cp): # space-separated words, so they are not treated specially and handled # like the all of the other languages. if ((cp >= 0x4E00 and cp <= 0x9FFF) or # - (cp >= 0x3400 and cp <= 0x4DBF) or # - (cp >= 0x20000 and cp <= 0x2A6DF) or # - (cp >= 0x2A700 and cp <= 0x2B73F) or # - (cp >= 0x2B740 and cp <= 0x2B81F) or # - (cp >= 0x2B820 and cp <= 0x2CEAF) or - (cp >= 0xF900 and cp <= 0xFAFF) or # - (cp >= 0x2F800 and cp <= 0x2FA1F)): # + (cp >= 0x3400 and cp <= 0x4DBF) or # + (cp >= 0x20000 and cp <= 0x2A6DF) or # + (cp >= 0x2A700 and cp <= 0x2B73F) or # + (cp >= 0x2B740 and cp <= 0x2B81F) or # + (cp >= 0x2B820 and cp <= 0x2CEAF) or + (cp >= 0xF900 and cp <= 0xFAFF) or # + (cp >= 0x2F800 and cp <= 0x2FA1F)): # return True - + return False - + def _clean_text(self, text): """Performs invalid character removal and whitespace cleanup on text.""" output = [] diff --git a/tests/tokenization_test.py b/tests/tokenization_test.py index f541a620e832..e1474e938bbc 100644 --- a/tests/tokenization_test.py +++ b/tests/tokenization_test.py @@ -44,12 +44,30 @@ def test_full_tokenizer(self): self.assertListEqual( tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9]) + def test_full_tokenizer_raises_error_for_long_sequences(self): + vocab_tokens = [ + "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", + "##ing", "," + ] + with open("/tmp/bert_tokenizer_test.txt", "w") as vocab_writer: + vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) + vocab_file = vocab_writer.name + + tokenizer = BertTokenizer(vocab_file, max_len=10) + os.remove(vocab_file) + tokens = tokenizer.tokenize(u"the cat sat on the mat in the summer time") + indices = tokenizer.convert_tokens_to_ids(tokens) + self.assertListEqual(indices, [0 for _ in range(10)]) + + tokens = tokenizer.tokenize(u"the cat sat on the mat in the summer time .") + self.assertRaises(ValueError, tokenizer.convert_tokens_to_ids, tokens) + def test_chinese(self): tokenizer = BasicTokenizer() - + self.assertListEqual( tokenizer.tokenize(u"ah\u535A\u63A8zz"), - [u"ah", u"\u535A", u"\u63A8", u"zz"]) + [u"ah", u"\u535A", u"\u63A8", u"zz"]) def test_basic_tokenizer_lower(self): tokenizer = BasicTokenizer(do_lower_case=True) From d57763f582c18c22768f8bd821e3d9c2c36f95f0 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Tue, 18 Dec 2018 19:23:22 -0500 Subject: [PATCH 077/111] Fix typos --- README.md | 6 +++--- pytorch_pretrained_bert/tokenization.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index d0fd120967dd..a487b03fdb77 100644 --- a/README.md +++ b/README.md @@ -65,7 +65,7 @@ This package comprises the following classes that can be imported in Python and - `BertAdam` - Bert version of Adam algorithm with weight decay fix, warmup and linear decay of the learning rate. - A configuration class (in the [`modeling.py`](./pytorch_pretrained_bert/modeling.py) file): - - `BertConfig` - Configuration class to store the configuration of a `BertModel` with utilisities to read and write from JSON configuration files. + - `BertConfig` - Configuration class to store the configuration of a `BertModel` with utilities to read and write from JSON configuration files. The repository further comprises: @@ -361,9 +361,9 @@ Here is how to use these techniques in our scripts: - **Gradient Accumulation**: Gradient accumulation can be used by supplying a integer greater than 1 to the `--gradient_accumulation_steps` argument. The batch at each step will be divided by this integer and gradient will be accumulated over `gradient_accumulation_steps` steps. - **Multi-GPU**: Multi-GPU is automatically activated when several GPUs are detected and the batches are splitted over the GPUs. - **Distributed training**: Distributed training can be activated by supplying an integer greater or equal to 0 to the `--local_rank` argument (see below). -- **16-bits training**: 16-bits training, also called mixed-precision training, can reduce the memory requirement of your model on the GPU by using half-precision training, basically allowing to double the batch size. If you have a recent GPU (starting from NVIDIA Volta architecture) you should see no decrease in speed. A good introduction to Mixed precision training can be found [here](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) and a full documentation is [here](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html). In our scripts, this option can be activated by setting the `--fp16` flag and you can play with loss scaling using the `--loss_scale` flag (see the previously linked documentation for details on loss scaling). The loss scale can be zero in which case the scale is dynamically adjuted or a positive power of two in which case the scaling is static. +- **16-bits training**: 16-bits training, also called mixed-precision training, can reduce the memory requirement of your model on the GPU by using half-precision training, basically allowing to double the batch size. If you have a recent GPU (starting from NVIDIA Volta architecture) you should see no decrease in speed. A good introduction to Mixed precision training can be found [here](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) and a full documentation is [here](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html). In our scripts, this option can be activated by setting the `--fp16` flag and you can play with loss scaling using the `--loss_scale` flag (see the previously linked documentation for details on loss scaling). The loss scale can be zero in which case the scale is dynamically adjusted or a positive power of two in which case the scaling is static. -To use 16-bits training and distributed training, you need to install NVIDIA's apex extension [as detailed here](https://github.com/nvidia/apex). You will find more information reguarding the internals of `apex` and how to use `apex` in [the doc and the associated repository](https://github.com/nvidia/apex). The results of the tests perfomed on pytorch-BERT by the NVIDIA team (and my trials at reproducing them) can be consulted in [the relevant PR of the present repository](https://github.com/huggingface/pytorch-pretrained-BERT/pull/116). +To use 16-bits training and distributed training, you need to install NVIDIA's apex extension [as detailed here](https://github.com/nvidia/apex). You will find more information regarding the internals of `apex` and how to use `apex` in [the doc and the associated repository](https://github.com/nvidia/apex). The results of the tests performed on pytorch-BERT by the NVIDIA team (and my trials at reproducing them) can be consulted in [the relevant PR of the present repository](https://github.com/huggingface/pytorch-pretrained-BERT/pull/116). Note: To use *Distributed Training*, you will need to run one training script on each of your machines. This can be done for example by running the following command on each server (see [the above mentioned blog post]((https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255)) for more details): ```bash diff --git a/pytorch_pretrained_bert/tokenization.py b/pytorch_pretrained_bert/tokenization.py index 5954b78f6833..23a6eba2f162 100644 --- a/pytorch_pretrained_bert/tokenization.py +++ b/pytorch_pretrained_bert/tokenization.py @@ -263,7 +263,7 @@ def tokenize(self, text): Args: text: A single token or whitespace separated tokens. This should have - already been passed through `BasicTokenizer. + already been passed through `BasicTokenizer`. Returns: A list of wordpiece tokens. From b3d86162b0eb119fc2ffb1fd2bd54d9896e60728 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Patrick=20Sodr=C3=A9?= Date: Wed, 19 Dec 2018 01:41:18 +0000 Subject: [PATCH 078/111] Add license to source distribution --- MANIFEST.in | 1 + 1 file changed, 1 insertion(+) create mode 100644 MANIFEST.in diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 000000000000..1aba38f67a22 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1 @@ +include LICENSE From 87c1244c7db8d57d6e1eed9a6578c0262379afea Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Patrick=20Sodr=C3=A9?= Date: Wed, 19 Dec 2018 01:57:04 +0000 Subject: [PATCH 079/111] Convert scripts into entry_points The recommended approach to create launch scripts is to use entry_points and console_scripts. xref: https://packaging.python.org/guides/distributing-packages-using-setuptools/#scripts --- pytorch_pretrained_bert/__main__.py | 5 ++++- setup.py | 6 +++++- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/pytorch_pretrained_bert/__main__.py b/pytorch_pretrained_bert/__main__.py index 73f1909b43a2..79ad84293232 100644 --- a/pytorch_pretrained_bert/__main__.py +++ b/pytorch_pretrained_bert/__main__.py @@ -1,5 +1,5 @@ # coding: utf8 -if __name__ == '__main__': +def main(): import sys try: from .convert_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch @@ -17,3 +17,6 @@ TF_CONFIG = sys.argv.pop() TF_CHECKPOINT = sys.argv.pop() convert_tf_checkpoint_to_pytorch(TF_CHECKPOINT, TF_CONFIG, PYTORCH_DUMP_OUTPUT) + +if __name__ == '__main__': + main() diff --git a/setup.py b/setup.py index dbfeb2c6948e..e9b5c077c491 100644 --- a/setup.py +++ b/setup.py @@ -53,7 +53,11 @@ 'boto3', 'requests', 'tqdm'], - scripts=["bin/pytorch_pretrained_bert"], + entry_points={ + 'console_scripts': [ + "pytorch_pretrained_bert=pytorch_pretrained_bert.__main__:main" + ] + }, python_requires='>=3.5.0', tests_require=['pytest'], classifiers=[ From ecf3ea197e48545eb10612ac22a25a72d90d19dd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Patrick=20Sodr=C3=A9?= Date: Wed, 19 Dec 2018 01:58:53 +0000 Subject: [PATCH 080/111] Remove original script --- bin/pytorch_pretrained_bert | 2 -- 1 file changed, 2 deletions(-) delete mode 100644 bin/pytorch_pretrained_bert diff --git a/bin/pytorch_pretrained_bert b/bin/pytorch_pretrained_bert deleted file mode 100644 index eee2b4c250c9..000000000000 --- a/bin/pytorch_pretrained_bert +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/sh -python -m pytorch_pretrained_bert "$@" \ No newline at end of file From 67f4dd56a39c7e34ff41df7fa7f1e0a87642dc56 Mon Sep 17 00:00:00 2001 From: tholor Date: Wed, 19 Dec 2018 09:22:37 +0100 Subject: [PATCH 081/111] update readme for run_lm_finetuning --- README.md | 32 ++++++++++++++++++++++++++++---- 1 file changed, 28 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index d0fd120967dd..89d3ac4519d7 100644 --- a/README.md +++ b/README.md @@ -69,12 +69,13 @@ This package comprises the following classes that can be imported in Python and The repository further comprises: -- Four examples on how to use Bert (in the [`examples` folder](./examples)): +- Five examples on how to use Bert (in the [`examples` folder](./examples)): - [`extract_features.py`](./examples/extract_features.py) - Show how to extract hidden states from an instance of `BertModel`, - [`run_classifier.py`](./examples/run_classifier.py) - Show how to fine-tune an instance of `BertForSequenceClassification` on GLUE's MRPC task, - [`run_squad.py`](./examples/run_squad.py) - Show how to fine-tune an instance of `BertForQuestionAnswering` on SQuAD v1.0 task. - [`run_swag.py`](./examples/run_swag.py) - Show how to fine-tune an instance of `BertForMultipleChoice` on Swag task. - + - [`run_lm_finetuning`](./examples/run_lm_finetuning.py) - Show how to fine-tune an instance of `BertForPretraining' on a target text corpus. + These examples are detailed in the [Examples](#examples) section of this readme. - Three notebooks that were used to check that the TensorFlow and PyTorch models behave identically (in the [`notebooks` folder](./notebooks)): @@ -246,6 +247,9 @@ An example on how to use this class is given in the [`extract_features.py`](./ex - the masked language modeling logits, and - the next sentence classification logits. + +An example on how to use this class is given in the [`run_lm_finetuning.py`](./examples/run_lm_finetuning.py) script which can be used to fine-tune the BERT language model on your specific different text corpus. This should improve model performance, if the language style is different from the original BERT training corpus (Wiki + BookCorpus). + #### 3. `BertForMaskedLM` @@ -347,7 +351,7 @@ The optimizer accepts the following arguments: | Sub-section | Description | |-|-| | [Training large models: introduction, tools and examples](#Training-large-models-introduction,-tools-and-examples) | How to use gradient-accumulation, multi-gpu training, distributed training, optimize on CPU and 16-bits training to train Bert models | -| [Fine-tuning with BERT: running the examples](#Fine-tuning-with-BERT-running-the-examples) | Running the examples in [`./examples`](./examples/): `extract_classif.py`, `run_classifier.py` and `run_squad.py` | +| [Fine-tuning with BERT: running the examples](#Fine-tuning-with-BERT-running-the-examples) | Running the examples in [`./examples`](./examples/): `extract_classif.py`, `run_classifier.py`, `run_squad.py` and `run_lm_finetuning.py` | | [Fine-tuning BERT-large on GPUs](#Fine-tuning-BERT-large-on-GPUs) | How to fine tune `BERT large`| ### Training large models: introduction, tools and examples @@ -378,7 +382,8 @@ We showcase several fine-tuning examples based on (and extended from) [the origi - a *sequence-level classifier* on the MRPC classification corpus, - a *token-level classifier* on the question answering dataset SQuAD, and - a *sequence-level multiple-choice classifier* on the SWAG classification corpus. - +- a *BERT language model* on another target corpus + #### MRPC This example code fine-tunes BERT on the Microsoft Research Paraphrase @@ -490,6 +495,25 @@ global_step = 13788 loss = 0.06423990014260186 ``` +#### LM Fine-tuning + +The data should be a text file in the same format as [sample_text.txt](./samples/sample_text.txt) (one sentence per line, docs separated by empty line). + +Training one epoch on a 500k sentence corpus takes about 1:20h on 4 x NVIDIA Tesla P100 with `train_batch_size=200` and `max_seq_length=128`: + + +```shell +python run_lm_finetuning.py \ + --bert_model bert-base-cased + --do_train + --train_file samples/sample_text.txt + --output_dir models + --num_train_epochs 5.0 + --learning_rate 3e-5 + --train_batch_size 32 + --max_seq_length 128 +``` + ## Fine-tuning BERT-large on GPUs The options we list above allow to fine-tune BERT-large rather easily on GPU(s) instead of the TPU used by the original implementation. From 7176674849680cfc38dda617677634222f0debaa Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Thu, 20 Dec 2018 13:11:17 +0100 Subject: [PATCH 082/111] Fixing various class documentations. --- pytorch_pretrained_bert/modeling.py | 14 ++++---------- 1 file changed, 4 insertions(+), 10 deletions(-) diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index acdc741f6da4..8eb856e66a27 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -456,7 +456,9 @@ def from_pretrained(cls, pretrained_model_name, state_dict=None, cache_dir=None, . `bert-base-uncased` . `bert-large-uncased` . `bert-base-cased` - . `bert-base-multilingual` + . `bert-large-cased` + . `bert-base-multilingual-uncased` + . `bert-base-multilingual-cased` . `bert-base-chinese` - a path or url to a pretrained model archive containing: . `bert_config.json` a configuration file for the model @@ -1035,15 +1037,7 @@ class BertForQuestionAnswering(PreTrainedBertModel): the sequence output that computes start_logits and end_logits Params: - `config`: either - - a BertConfig class instance with the configuration to build a new model, or - - a str with the name of a pre-trained model to load selected in the list of: - . `bert-base-uncased` - . `bert-large-uncased` - . `bert-base-cased` - . `bert-base-multilingual` - . `bert-base-chinese` - The pre-trained model will be downloaded and cached if needed. + `config`: a BertConfig class instance with the configuration to build a new model. Inputs: `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] From e5fc98c542af436dd079b0c0f2b2dcac1a89594f Mon Sep 17 00:00:00 2001 From: tholor Date: Thu, 20 Dec 2018 18:30:52 +0100 Subject: [PATCH 083/111] add exemplary training data. update to nvidia apex. refactor 'item -> line in doc' mapping. add warning for unknown word. --- README.md | 4 +- examples/run_lm_finetuning.py | 162 +++++++++++++++------------------- 2 files changed, 71 insertions(+), 95 deletions(-) diff --git a/README.md b/README.md index 89d3ac4519d7..b22b66ae0cf9 100644 --- a/README.md +++ b/README.md @@ -498,8 +498,8 @@ loss = 0.06423990014260186 #### LM Fine-tuning The data should be a text file in the same format as [sample_text.txt](./samples/sample_text.txt) (one sentence per line, docs separated by empty line). - -Training one epoch on a 500k sentence corpus takes about 1:20h on 4 x NVIDIA Tesla P100 with `train_batch_size=200` and `max_seq_length=128`: +You can download an [exemplary training corpus](https://ext-bert-sample.obs.eu-de.otc.t-systems.com/small_wiki_sentence_corpus.txt) generated from wikipedia articles and splitted into ~500k sentences with spaCy. +Training one epoch on this corpus takes about 1:20h on 4 x NVIDIA Tesla P100 with `train_batch_size=200` and `max_seq_length=128`: ```shell diff --git a/examples/run_lm_finetuning.py b/examples/run_lm_finetuning.py index 3e8bc36f48bf..9ca9830eff37 100644 --- a/examples/run_lm_finetuning.py +++ b/examples/run_lm_finetuning.py @@ -1,5 +1,6 @@ # coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -41,6 +42,12 @@ logger = logging.getLogger(__name__) +def warmup_linear(x, warmup=0.002): + if x < warmup: + return x/warmup + return 1.0 - x + + class BERTDataset(Dataset): def __init__(self, corpus_path, tokenizer, seq_len, encoding="utf-8", corpus_lines=None, on_memory=True): self.vocab = tokenizer.vocab @@ -59,6 +66,7 @@ def __init__(self, corpus_path, tokenizer, seq_len, encoding="utf-8", corpus_lin # for loading samples in memory self.current_random_doc = 0 self.num_docs = 0 + self.sample_to_doc = [] # map sample index to doc and line # load samples into memory if on_memory: @@ -71,12 +79,20 @@ def __init__(self, corpus_path, tokenizer, seq_len, encoding="utf-8", corpus_lin if line == "": self.all_docs.append(doc) doc = [] + #remove last added sample because there won't be a subsequent line anymore in the doc + self.sample_to_doc.pop() else: + #store as one sample + sample = {"doc_id": len(self.all_docs), + "line": len(doc)} + self.sample_to_doc.append(sample) doc.append(line) - self.corpus_lines = self.corpus_lines + 1 + self.corpus_lines = self.corpus_lines + 1 + # if last row in file is not empty if self.all_docs[-1] != doc: self.all_docs.append(doc) + self.sample_to_doc.pop() self.num_docs = len(self.all_docs) @@ -159,20 +175,11 @@ def get_corpus_line(self, item): t2 = "" assert item < self.corpus_lines if self.on_memory: - # get the right doc - doc_id = 0 - doc_start = 0 - doc_end = len(self.all_docs[doc_id]) - 2 - while item > doc_end: - doc_id += 1 - doc_start = doc_end + 1 - doc_end += len(self.all_docs[doc_id]) - 1 - # get the right line within doc - line_in_doc = item - doc_start - t1 = self.all_docs[doc_id][line_in_doc] - t2 = self.all_docs[doc_id][line_in_doc + 1] + sample = self.sample_to_doc[item] + t1 = self.all_docs[sample["doc_id"]][sample["line"]] + t2 = self.all_docs[sample["doc_id"]][sample["line"]+1] # used later to avoid random nextSentence from same doc - self.current_doc = doc_id + self.current_doc = sample["doc_id"] return t1, t2 else: if self.line_buffer is None: @@ -297,6 +304,7 @@ def random_word(tokens, tokenizer): except KeyError: # For unknown words (should not occur with BPE vocab) output_label.append(tokenizer.vocab["[UNK]"]) + logger.warning("Cannot find token '{}' in vocab. Using [UNK] insetad".format(token)) else: # no masking token (will be ignored by loss function later) output_label.append(-1) @@ -468,17 +476,15 @@ def main(): type=int, default=1, help="Number of updates steps to accumualte before performing a backward/update pass.") - parser.add_argument('--optimize_on_cpu', - default=False, - action='store_true', - help="Whether to perform optimization and keep the optimizer averages on CPU") parser.add_argument('--fp16', default=False, action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument('--loss_scale', - type=float, default=128, - help='Loss scaling, positive power of 2 values can improve fp16 convergence.') + type = float, default = 0, + help = "Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" + "0 (default value): dynamic loss scaling.\n" + "Positive power of 2: static loss scaling value.\n") args = parser.parse_args() @@ -486,14 +492,13 @@ def main(): device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: + torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') - if args.fp16: - logger.info("16-bits training currently not supported in distributed training") - args.fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496) - logger.info("device %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1)) + logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format( + device, n_gpu, bool(args.local_rank != -1), args.fp16)) if args.gradient_accumulation_steps < 1: raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format( @@ -531,29 +536,42 @@ def main(): model.half() model.to(device) if args.local_rank != -1: - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], - output_device=args.local_rank) + try: + from apex.parallel import DistributedDataParallel as DDP + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") + model = DDP(model) elif n_gpu > 1: model = torch.nn.DataParallel(model) # Prepare optimizer - if args.fp16: - param_optimizer = [(n, param.clone().detach().to('cpu').float().requires_grad_()) \ - for n, param in model.named_parameters()] - elif args.optimize_on_cpu: - param_optimizer = [(n, param.clone().detach().to('cpu').requires_grad_()) \ - for n, param in model.named_parameters()] - else: - param_optimizer = list(model.named_parameters()) - no_decay = ['bias', 'gamma', 'beta'] + param_optimizer = list(model.named_parameters()) + no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [ - {'params': [p for n, p in param_optimizer if n not in no_decay], 'weight_decay_rate': 0.01}, - {'params': [p for n, p in param_optimizer if n in no_decay], 'weight_decay_rate': 0.0} + {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, + {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] - optimizer = BertAdam(optimizer_grouped_parameters, - lr=args.learning_rate, - warmup=args.warmup_proportion, - t_total=num_train_steps) + if args.fp16: + try: + from apex.optimizers import FP16_Optimizer + from apex.optimizers import FusedAdam + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") + + optimizer = FusedAdam(optimizer_grouped_parameters, + lr=args.learning_rate, + bias_correction=False, + max_grad_norm=1.0) + if args.loss_scale == 0: + optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) + else: + optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale) + + else: + optimizer = BertAdam(optimizer_grouped_parameters, + lr=args.learning_rate, + warmup=args.warmup_proportion, + t_total=num_train_steps) global_step = 0 if args.do_train: @@ -580,33 +598,22 @@ def main(): loss = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. - if args.fp16 and args.loss_scale != 1.0: - # rescale loss for fp16 training - # see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html - loss = loss * args.loss_scale if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps - loss.backward() + if args.fp16: + optimizer.backward(loss) + else: + loss.backward() tr_loss += loss.item() nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 if (step + 1) % args.gradient_accumulation_steps == 0: - if args.fp16 or args.optimize_on_cpu: - if args.fp16 and args.loss_scale != 1.0: - # scale down gradients for fp16 training - for param in model.parameters(): - param.grad.data = param.grad.data / args.loss_scale - is_nan = set_optimizer_params_grad(param_optimizer, model.named_parameters(), test_nan=True) - if is_nan: - logger.info("FP16 TRAINING: Nan in gradients, reducing loss scaling") - args.loss_scale = args.loss_scale / 2 - model.zero_grad() - continue - optimizer.step() - copy_optimizer_params_to_model(model.named_parameters(), param_optimizer) - else: - optimizer.step() - model.zero_grad() + # modify learning rate with special warm up BERT uses + lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_steps, args.warmup_proportion) + for param_group in optimizer.param_groups: + param_group['lr'] = lr_this_step + optimizer.step() + optimizer.zero_grad() global_step += 1 logger.info("** ** * Saving fine - tuned model ** ** * ") @@ -639,36 +646,5 @@ def accuracy(out, labels): return np.sum(outputs == labels) -def copy_optimizer_params_to_model(named_params_model, named_params_optimizer): - """ Utility function for optimize_on_cpu and 16-bits training. - Copy the parameters optimized on CPU/RAM back to the model on GPU - """ - for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model): - if name_opti != name_model: - logger.error("name_opti != name_model: {} {}".format(name_opti, name_model)) - raise ValueError - param_model.data.copy_(param_opti.data) - - -def set_optimizer_params_grad(named_params_optimizer, named_params_model, test_nan=False): - """ Utility function for optimize_on_cpu and 16-bits training. - Copy the gradient of the GPU parameters to the CPU/RAMM copy of the model - """ - is_nan = False - for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model): - if name_opti != name_model: - logger.error("name_opti != name_model: {} {}".format(name_opti, name_model)) - raise ValueError - if param_model.grad is not None: - if test_nan and torch.isnan(param_model.grad).sum() > 0: - is_nan = True - if param_opti.grad is None: - param_opti.grad = torch.nn.Parameter(param_opti.data.new().resize_(*param_opti.data.size())) - param_opti.grad.data.copy_(param_model.grad.data) - else: - param_opti.grad = None - return is_nan - - if __name__ == "__main__": main() \ No newline at end of file From 8da280ebbeca5ebd7561fd05af78c65df9161f92 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Thu, 20 Dec 2018 16:33:39 -0500 Subject: [PATCH 084/111] Setup CI --- .circleci/config.yml | 11 +++++++++++ README.md | 2 ++ 2 files changed, 13 insertions(+) create mode 100644 .circleci/config.yml diff --git a/.circleci/config.yml b/.circleci/config.yml new file mode 100644 index 000000000000..2c8f906aba78 --- /dev/null +++ b/.circleci/config.yml @@ -0,0 +1,11 @@ +version: 2 +jobs: + build: + working_directory: ~/pytorch-pretrained-BERT + docker: + - image: circleci/python:3.7 + steps: + - checkout + - run: sudo pip install --progress-bar off . + - run: sudo pip install pytest + - run: python -m pytest -sv tests/ diff --git a/README.md b/README.md index a487b03fdb77..e0c64e36d7a4 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,7 @@ # PyTorch Pretrained Bert +[![CircleCI](https://circleci.com/gh/huggingface/pytorch-pretrained-BERT.svg?style=svg)](https://circleci.com/gh/huggingface/pytorch-pretrained-BERT) + This repository contains an op-for-op PyTorch reimplementation of [Google's TensorFlow repository for the BERT model](https://github.com/google-research/bert) that was released together with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. This implementation is provided with [Google's pre-trained models](https://github.com/google-research/bert), examples, notebooks and a command-line interface to load any pre-trained TensorFlow checkpoint for BERT is also provided. From 99709ee61d887ac1a4431a54a4f78f008b5b11d6 Mon Sep 17 00:00:00 2001 From: Jasdeep Singh <33911313+SinghJasdeep@users.noreply.github.com> Date: Thu, 20 Dec 2018 13:55:47 -0800 Subject: [PATCH 085/111] loading saved model when n_classes != 2 Required to for: Assertion `t >= 0 && t < n_classes` failed, if your default number of classes is not 2. --- examples/run_classifier.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index adf81f4e28b6..456b06b07fee 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -558,7 +558,7 @@ def main(): # Load a trained model that you have fine-tuned model_state_dict = torch.load(output_model_file) - model = BertForSequenceClassification.from_pretrained(args.bert_model, state_dict=model_state_dict) + model = BertForSequenceClassification.from_pretrained(args.bert_model, state_dict=model_state_dict, num_labels=num_labels) model.to(device) if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): From e626eecc25b92398b7cf1e06d4fad5ca1df72c18 Mon Sep 17 00:00:00 2001 From: wlhgtc Date: Sat, 22 Dec 2018 20:26:05 +0800 Subject: [PATCH 086/111] Update modeling.py --- pytorch_pretrained_bert/modeling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index acdc741f6da4..ad423e79ddf6 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -728,7 +728,7 @@ class BertForMaskedLM(PreTrainedBertModel): is only computed for the labels set in [0, ..., vocab_size] Outputs: - if `masked_lm_labels` is `None`: + if `masked_lm_labels` is not `None`: Outputs the masked language modeling loss. if `masked_lm_labels` is `None`: Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size]. From 186f75342eed9f7bd2505b1b41ef317ea89d657b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Wed, 2 Jan 2019 14:00:59 +0100 Subject: [PATCH 087/111] Adding new pretrained model to the help of the `bert_model` argument. --- examples/run_classifier.py | 3 ++- examples/run_squad.py | 3 ++- examples/run_swag.py | 3 ++- 3 files changed, 6 insertions(+), 3 deletions(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index adf81f4e28b6..e265ed73df35 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -312,7 +312,8 @@ def main(): help="The input data dir. Should contain the .tsv files (or other data files) for the task.") parser.add_argument("--bert_model", default=None, type=str, required=True, help="Bert pre-trained model selected in the list: bert-base-uncased, " - "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.") + "bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, " + "bert-base-multilingual-cased, bert-base-chinese.") parser.add_argument("--task_name", default=None, type=str, diff --git a/examples/run_squad.py b/examples/run_squad.py index 6a97dd300b0e..8be4143a58d2 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -681,7 +681,8 @@ def main(): ## Required parameters parser.add_argument("--bert_model", default=None, type=str, required=True, help="Bert pre-trained model selected in the list: bert-base-uncased, " - "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.") + "bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, " + "bert-base-multilingual-cased, bert-base-chinese.") parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model checkpoints and predictions will be written.") diff --git a/examples/run_swag.py b/examples/run_swag.py index caddbee8ab7a..c31696fec838 100644 --- a/examples/run_swag.py +++ b/examples/run_swag.py @@ -249,7 +249,8 @@ def main(): help="The input data dir. Should contain the .csv files (or other data files) for the task.") parser.add_argument("--bert_model", default=None, type=str, required=True, help="Bert pre-trained model selected in the list: bert-base-uncased, " - "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.") + "bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, " + "bert-base-multilingual-cased, bert-base-chinese.") parser.add_argument("--output_dir", default=None, type=str, From be3b9bcf4db4f7e942c7f71eb1d7de3a8d476ad0 Mon Sep 17 00:00:00 2001 From: Jade Abbott Date: Thu, 3 Jan 2019 09:02:33 +0200 Subject: [PATCH 088/111] Allow one to use the pretrained model in evaluation when do_train is not selected --- examples/run_classifier.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index adf81f4e28b6..9236c6a252d1 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -430,8 +430,8 @@ def main(): if not args.do_train and not args.do_eval: raise ValueError("At least one of `do_train` or `do_eval` must be True.") - - if os.path.exists(args.output_dir) and os.listdir(args.output_dir): + + if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train: raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) os.makedirs(args.output_dir, exist_ok=True) @@ -554,7 +554,8 @@ def main(): # Save a trained model model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") - torch.save(model_to_save.state_dict(), output_model_file) + if args.do_train: + torch.save(model_to_save.state_dict(), output_model_file) # Load a trained model that you have fine-tuned model_state_dict = torch.load(output_model_file) From b96149a19b225cc2eabd14c3227b8acc9b268b49 Mon Sep 17 00:00:00 2001 From: Jade Abbott Date: Thu, 3 Jan 2019 10:31:56 +0200 Subject: [PATCH 089/111] Training loss is not initialized if only do_eval is specified --- examples/run_classifier.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index 9236c6a252d1..c99cc0e12a7a 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -430,7 +430,7 @@ def main(): if not args.do_train and not args.do_eval: raise ValueError("At least one of `do_train` or `do_eval` must be True.") - + if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train: raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) os.makedirs(args.output_dir, exist_ok=True) @@ -503,6 +503,7 @@ def main(): t_total=t_total) global_step = 0 + tr_loss = 0 if args.do_train: train_features = convert_examples_to_features( train_examples, label_list, args.max_seq_length, tokenizer) @@ -581,7 +582,8 @@ def main(): model.eval() eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 - for input_ids, input_mask, segment_ids, label_ids in eval_dataloader: + + for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"): input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) @@ -603,11 +605,11 @@ def main(): eval_loss = eval_loss / nb_eval_steps eval_accuracy = eval_accuracy / nb_eval_examples - + loss = tr_loss/nb_tr_steps if args.do_train else None result = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'global_step': global_step, - 'loss': tr_loss/nb_tr_steps} + 'loss': loss} output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: From c64de50ea4b4cec7e87732abb621bf70c8fa8763 Mon Sep 17 00:00:00 2001 From: Jade Abbott Date: Thu, 3 Jan 2019 12:34:57 +0200 Subject: [PATCH 090/111] nb_tr_steps is not initialized --- examples/run_classifier.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index c99cc0e12a7a..8441c86937b0 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -503,6 +503,7 @@ def main(): t_total=t_total) global_step = 0 + nb_tr_steps = 0 tr_loss = 0 if args.do_train: train_features = convert_examples_to_features( @@ -565,6 +566,7 @@ def main(): if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = processor.get_dev_examples(args.data_dir) + # should tokenize this too. eval_features = convert_examples_to_features( eval_examples, label_list, args.max_seq_length, tokenizer) logger.info("***** Running evaluation *****") From 193e2df8ba95efd6e3326cb0907576a0c74f1d74 Mon Sep 17 00:00:00 2001 From: Jade Abbott Date: Thu, 3 Jan 2019 13:13:06 +0200 Subject: [PATCH 091/111] Remove rogue comment --- examples/run_classifier.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/run_classifier.py b/examples/run_classifier.py index 8441c86937b0..be212edc1be5 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -566,7 +566,6 @@ def main(): if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = processor.get_dev_examples(args.data_dir) - # should tokenize this too. eval_features = convert_examples_to_features( eval_examples, label_list, args.max_seq_length, tokenizer) logger.info("***** Running evaluation *****") From ca4e7aaa72551cdba39e49094f5a05962573c774 Mon Sep 17 00:00:00 2001 From: Sang-Kil Park Date: Sat, 5 Jan 2019 11:42:54 +0900 Subject: [PATCH 092/111] Fix error when `bert_model` param is path or url. Error occurs when `bert_model` param is path or url. Therefore, if it is path, specify the last path to prevent error. --- examples/run_squad.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/run_squad.py b/examples/run_squad.py index 6a97dd300b0e..bbc803867694 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -855,7 +855,7 @@ def main(): global_step = 0 if args.do_train: cached_train_features_file = args.train_file+'_{0}_{1}_{2}_{3}'.format( - args.bert_model, str(args.max_seq_length), str(args.doc_stride), str(args.max_query_length)) + list(filter(None, args.bert_model.split('/'))).pop(), str(args.max_seq_length), str(args.doc_stride), str(args.max_query_length)) train_features = None try: with open(cached_train_features_file, "rb") as reader: From d0d9b384f2578869066d2d028ee7339bbb661e75 Mon Sep 17 00:00:00 2001 From: Li Dong Date: Mon, 7 Jan 2019 15:51:33 +0800 Subject: [PATCH 093/111] LayerNorm initialization The LayerNorm gamma and beta should be initialized by .fill_(1.0) and .zero_(). reference links: https://github.com/tensorflow/tensorflow/blob/989e78c412a7e0f5361d4d7dfdfb230c8136e749/tensorflow/contrib/layers/python/layers/layers.py#L2298 https://github.com/tensorflow/tensorflow/blob/989e78c412a7e0f5361d4d7dfdfb230c8136e749/tensorflow/contrib/layers/python/layers/layers.py#L2308 --- pytorch_pretrained_bert/modeling.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pytorch_pretrained_bert/modeling.py b/pytorch_pretrained_bert/modeling.py index acdc741f6da4..83562ea57336 100644 --- a/pytorch_pretrained_bert/modeling.py +++ b/pytorch_pretrained_bert/modeling.py @@ -439,8 +439,8 @@ def init_bert_weights(self, module): # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) elif isinstance(module, BertLayerNorm): - module.bias.data.normal_(mean=0.0, std=self.config.initializer_range) - module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + module.bias.data.zero_() + module.weight.data.fill_(1.0) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() From c9fd3505678d581388fb44ba1d79ac41e8fb28a4 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Mon, 7 Jan 2019 13:01:54 +0100 Subject: [PATCH 094/111] remove default when action is store_true in arguments --- examples/extract_features.py | 3 +-- examples/run_classifier.py | 5 ----- examples/run_lm_finetuning.py | 4 ---- examples/run_squad.py | 9 +++------ examples/run_swag.py | 5 ----- 5 files changed, 4 insertions(+), 22 deletions(-) diff --git a/examples/extract_features.py b/examples/extract_features.py index 4f8812121ea1..9d05d7905d0f 100644 --- a/examples/extract_features.py +++ b/examples/extract_features.py @@ -199,7 +199,7 @@ def main(): "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.") ## Other parameters - parser.add_argument("--do_lower_case", default=False, action='store_true', help="Set this flag if you are using an uncased model.") + parser.add_argument("--do_lower_case", action='store_true', help="Set this flag if you are using an uncased model.") parser.add_argument("--layers", default="-1,-2,-3,-4", type=str) parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after WordPiece tokenization. Sequences longer " @@ -210,7 +210,6 @@ def main(): default=-1, help = "local_rank for distributed training on gpus") parser.add_argument("--no_cuda", - default=False, action='store_true', help="Whether not to use CUDA when available") diff --git a/examples/run_classifier.py b/examples/run_classifier.py index 0afd4434022f..31877a541457 100644 --- a/examples/run_classifier.py +++ b/examples/run_classifier.py @@ -333,15 +333,12 @@ def main(): "Sequences longer than this will be truncated, and sequences shorter \n" "than this will be padded.") parser.add_argument("--do_train", - default=False, action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", - default=False, action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--do_lower_case", - default=False, action='store_true', help="Set this flag if you are using an uncased model.") parser.add_argument("--train_batch_size", @@ -366,7 +363,6 @@ def main(): help="Proportion of training to perform linear learning rate warmup for. " "E.g., 0.1 = 10%% of training.") parser.add_argument("--no_cuda", - default=False, action='store_true', help="Whether not to use CUDA when available") parser.add_argument("--local_rank", @@ -382,7 +378,6 @@ def main(): default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument('--fp16', - default=False, action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument('--loss_scale', diff --git a/examples/run_lm_finetuning.py b/examples/run_lm_finetuning.py index 9ca9830eff37..2c64f67b5f6a 100644 --- a/examples/run_lm_finetuning.py +++ b/examples/run_lm_finetuning.py @@ -432,7 +432,6 @@ def main(): "Sequences longer than this will be truncated, and sequences shorter \n" "than this will be padded.") parser.add_argument("--do_train", - default=False, action='store_true', help="Whether to run training.") parser.add_argument("--train_batch_size", @@ -457,11 +456,9 @@ def main(): help="Proportion of training to perform linear learning rate warmup for. " "E.g., 0.1 = 10%% of training.") parser.add_argument("--no_cuda", - default=False, action='store_true', help="Whether not to use CUDA when available") parser.add_argument("--on_memory", - default=False, action='store_true', help="Whether to load train samples into memory or use disk") parser.add_argument("--local_rank", @@ -477,7 +474,6 @@ def main(): default=1, help="Number of updates steps to accumualte before performing a backward/update pass.") parser.add_argument('--fp16', - default=False, action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument('--loss_scale', diff --git a/examples/run_squad.py b/examples/run_squad.py index a4a568d9994b..88ea59093658 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -698,8 +698,8 @@ def main(): parser.add_argument("--max_query_length", default=64, type=int, help="The maximum number of tokens for the question. Questions longer than this will " "be truncated to this length.") - parser.add_argument("--do_train", default=False, action='store_true', help="Whether to run training.") - parser.add_argument("--do_predict", default=False, action='store_true', help="Whether to run eval on the dev set.") + parser.add_argument("--do_train", action='store_true', help="Whether to run training.") + parser.add_argument("--do_predict", action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.") parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") @@ -714,11 +714,10 @@ def main(): parser.add_argument("--max_answer_length", default=30, type=int, help="The maximum length of an answer that can be generated. This is needed because the start " "and end predictions are not conditioned on one another.") - parser.add_argument("--verbose_logging", default=False, action='store_true', + parser.add_argument("--verbose_logging", action='store_true', help="If true, all of the warnings related to data processing will be printed. " "A number of warnings are expected for a normal SQuAD evaluation.") parser.add_argument("--no_cuda", - default=False, action='store_true', help="Whether not to use CUDA when available") parser.add_argument('--seed', @@ -730,7 +729,6 @@ def main(): default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument("--do_lower_case", - default=True, action='store_true', help="Whether to lower case the input text. True for uncased models, False for cased models.") parser.add_argument("--local_rank", @@ -738,7 +736,6 @@ def main(): default=-1, help="local_rank for distributed training on gpus") parser.add_argument('--fp16', - default=False, action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument('--loss_scale', diff --git a/examples/run_swag.py b/examples/run_swag.py index c31696fec838..3fb87ae3e778 100644 --- a/examples/run_swag.py +++ b/examples/run_swag.py @@ -265,15 +265,12 @@ def main(): "Sequences longer than this will be truncated, and sequences shorter \n" "than this will be padded.") parser.add_argument("--do_train", - default=False, action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", - default=False, action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--do_lower_case", - default=False, action='store_true', help="Set this flag if you are using an uncased model.") parser.add_argument("--train_batch_size", @@ -298,7 +295,6 @@ def main(): help="Proportion of training to perform linear learning rate warmup for. " "E.g., 0.1 = 10%% of training.") parser.add_argument("--no_cuda", - default=False, action='store_true', help="Whether not to use CUDA when available") parser.add_argument("--local_rank", @@ -314,7 +310,6 @@ def main(): default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument('--fp16', - default=False, action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument('--loss_scale', From 2e4db64cab198dc241e18221ef088908f2587c61 Mon Sep 17 00:00:00 2001 From: thomwolf Date: Mon, 7 Jan 2019 13:06:42 +0100 Subject: [PATCH 095/111] add do_lower_case tokenizer loading optino in run_squad and ine_tuning examples --- examples/run_lm_finetuning.py | 2 +- examples/run_squad.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/run_lm_finetuning.py b/examples/run_lm_finetuning.py index 2c64f67b5f6a..39df2e99f832 100644 --- a/examples/run_lm_finetuning.py +++ b/examples/run_lm_finetuning.py @@ -515,7 +515,7 @@ def main(): raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) os.makedirs(args.output_dir, exist_ok=True) - tokenizer = BertTokenizer.from_pretrained(args.bert_model) + tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) #train_examples = None num_train_steps = None diff --git a/examples/run_squad.py b/examples/run_squad.py index 88ea59093658..245aee0ff26a 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -786,7 +786,7 @@ def main(): raise ValueError("Output directory () already exists and is not empty.") os.makedirs(args.output_dir, exist_ok=True) - tokenizer = BertTokenizer.from_pretrained(args.bert_model) + tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) train_examples = None num_train_steps = None From 751beb9e73c39065ea8b76fbfead9007a70a054f Mon Sep 17 00:00:00 2001 From: WrRan Date: Tue, 8 Jan 2019 10:54:51 +0800 Subject: [PATCH 096/111] never split some text --- pytorch_pretrained_bert/tokenization.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/pytorch_pretrained_bert/tokenization.py b/pytorch_pretrained_bert/tokenization.py index 9cfb3d8ce94d..9cb36a1b466c 100644 --- a/pytorch_pretrained_bert/tokenization.py +++ b/pytorch_pretrained_bert/tokenization.py @@ -75,7 +75,8 @@ def whitespace_tokenize(text): class BertTokenizer(object): """Runs end-to-end tokenization: punctuation splitting + wordpiece""" - def __init__(self, vocab_file, do_lower_case=True, max_len=None): + def __init__(self, vocab_file, do_lower_case=True, max_len=None, + never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")): if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained " @@ -83,7 +84,8 @@ def __init__(self, vocab_file, do_lower_case=True, max_len=None): self.vocab = load_vocab(vocab_file) self.ids_to_tokens = collections.OrderedDict( [(ids, tok) for tok, ids in self.vocab.items()]) - self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) + self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case, + never_split=never_split) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) self.max_len = max_len if max_len is not None else int(1e12) @@ -156,13 +158,16 @@ def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwarg class BasicTokenizer(object): """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" - def __init__(self, do_lower_case=True): + def __init__(self, + do_lower_case=True, + never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")): """Constructs a BasicTokenizer. Args: do_lower_case: Whether to lower case the input. """ self.do_lower_case = do_lower_case + self.never_split = never_split def tokenize(self, text): """Tokenizes a piece of text.""" @@ -198,6 +203,8 @@ def _run_strip_accents(self, text): def _run_split_on_punc(self, text): """Splits punctuation on a piece of text.""" + if text in self.never_split: + return [text] chars = list(text) i = 0 start_new_word = True From 3f60a60eede2129f01a31cea73f77b3338e5e894 Mon Sep 17 00:00:00 2001 From: WrRan Date: Tue, 8 Jan 2019 13:33:57 +0800 Subject: [PATCH 097/111] text in never_split should not lowercase --- pytorch_pretrained_bert/tokenization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pytorch_pretrained_bert/tokenization.py b/pytorch_pretrained_bert/tokenization.py index 9cb36a1b466c..595eb8fdaa92 100644 --- a/pytorch_pretrained_bert/tokenization.py +++ b/pytorch_pretrained_bert/tokenization.py @@ -182,7 +182,7 @@ def tokenize(self, text): orig_tokens = whitespace_tokenize(text) split_tokens = [] for token in orig_tokens: - if self.do_lower_case: + if self.do_lower_case and token not in self.never_split: token = token.lower() token = self._run_strip_accents(token) split_tokens.extend(self._run_split_on_punc(token)) From b3628f117e1462c5fb968294570997c47e612ede Mon Sep 17 00:00:00 2001 From: Unknown Date: Tue, 8 Jan 2019 15:13:13 -0800 Subject: [PATCH 098/111] Added Squad 2.0 --- examples/run_squad2.py | 1075 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1075 insertions(+) create mode 100644 examples/run_squad2.py diff --git a/examples/run_squad2.py b/examples/run_squad2.py new file mode 100644 index 000000000000..2f7de749055c --- /dev/null +++ b/examples/run_squad2.py @@ -0,0 +1,1075 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Run BERT on SQuAD 2.0""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import collections +import logging +import json +import math +import os +import random +import pickle +from tqdm import tqdm, trange + +import numpy as np +import torch +from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler +from torch.utils.data.distributed import DistributedSampler + +from pytorch_pretrained_bert.tokenization import whitespace_tokenize, BasicTokenizer, BertTokenizer +from pytorch_pretrained_bert.modeling import BertForQuestionAnswering +from pytorch_pretrained_bert.optimization import BertAdam +from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE + +logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', + datefmt = '%m/%d/%Y %H:%M:%S', + level = logging.INFO) +logger = logging.getLogger(__name__) + + +class SquadExample(object): + """ + A single training/test example for the Squad dataset. + For examples without an answer, the start and end position are -1. + """ + + def __init__(self, + qas_id, + question_text, + doc_tokens, + orig_answer_text=None, + start_position=None, + end_position=None, + is_impossible=None): + self.qas_id = qas_id + self.question_text = question_text + self.doc_tokens = doc_tokens + self.orig_answer_text = orig_answer_text + self.start_position = start_position + self.end_position = end_position + self.is_impossible = is_impossible + + def __str__(self): + return self.__repr__() + + def __repr__(self): + s = "" + s += "qas_id: %s" % (self.qas_id) + s += ", question_text: %s" % ( + self.question_text) + s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens)) + if self.start_position: + s += ", start_position: %d" % (self.start_position) + if self.start_position: + s += ", end_position: %d" % (self.end_position) + if self.start_position: + s += ", is_impossible: %r" % (self.is_impossible) + return s + + +class InputFeatures(object): + """A single set of features of data.""" + + def __init__(self, + unique_id, + example_index, + doc_span_index, + tokens, + token_to_orig_map, + token_is_max_context, + input_ids, + input_mask, + segment_ids, + start_position=None, + end_position=None, + is_impossible=None): + self.unique_id = unique_id + self.example_index = example_index + self.doc_span_index = doc_span_index + self.tokens = tokens + self.token_to_orig_map = token_to_orig_map + self.token_is_max_context = token_is_max_context + self.input_ids = input_ids + self.input_mask = input_mask + self.segment_ids = segment_ids + self.start_position = start_position + self.end_position = end_position + self.is_impossible = is_impossible + + +def read_squad_examples(input_file, is_training): + """Read a SQuAD json file into a list of SquadExample.""" + with open(input_file, "r", encoding='utf-8') as reader: + source = json.load(reader) + input_data = source["data"] + version = source["version"] + + def is_whitespace(c): + if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F: + return True + return False + + examples = [] + for entry in input_data: + for paragraph in entry["paragraphs"]: + paragraph_text = paragraph["context"] + doc_tokens = [] + char_to_word_offset = [] + prev_is_whitespace = True + for c in paragraph_text: + if is_whitespace(c): + prev_is_whitespace = True + else: + if prev_is_whitespace: + doc_tokens.append(c) + else: + doc_tokens[-1] += c + prev_is_whitespace = False + char_to_word_offset.append(len(doc_tokens) - 1) + + for qa in paragraph["qas"]: + qas_id = qa["id"] + question_text = qa["question"] + start_position = None + end_position = None + orig_answer_text = None + is_impossible = False + if is_training: + if version == "v2.0": + is_impossible = qa["is_impossible"] + if (len(qa["answers"]) != 1) and (not is_impossible): + raise ValueError( + "For training, each question should have exactly 1 answer.") + if not is_impossible: + answer = qa["answers"][0] + orig_answer_text = answer["text"] + answer_offset = answer["answer_start"] + answer_length = len(orig_answer_text) + start_position = char_to_word_offset[answer_offset] + end_position = char_to_word_offset[answer_offset + answer_length - 1] + # Only add answers where the text can be exactly recovered from the + # document. If this CAN'T happen it's likely due to weird Unicode + # stuff so we will just skip the example. + # + # Note that this means for training mode, every example is NOT + # guaranteed to be preserved. + actual_text = " ".join(doc_tokens[start_position:(end_position + 1)]) + cleaned_answer_text = " ".join( + whitespace_tokenize(orig_answer_text)) + if actual_text.find(cleaned_answer_text) == -1: + logger.warning("Could not find answer: '%s' vs. '%s'", + actual_text, cleaned_answer_text) + continue + else: + start_position = -1 + end_position = -1 + orig_answer_text = "" + + example = SquadExample( + qas_id=qas_id, + question_text=question_text, + doc_tokens=doc_tokens, + orig_answer_text=orig_answer_text, + start_position=start_position, + end_position=end_position, + is_impossible=is_impossible) + examples.append(example) + return examples + + +def convert_examples_to_features(examples, tokenizer, max_seq_length, + doc_stride, max_query_length, is_training): + """Loads a data file into a list of `InputBatch`s.""" + + unique_id = 1000000000 + + features = [] + for (example_index, example) in enumerate(examples): + query_tokens = tokenizer.tokenize(example.question_text) + + if len(query_tokens) > max_query_length: + query_tokens = query_tokens[0:max_query_length] + + tok_to_orig_index = [] + orig_to_tok_index = [] + all_doc_tokens = [] + for (i, token) in enumerate(example.doc_tokens): + orig_to_tok_index.append(len(all_doc_tokens)) + sub_tokens = tokenizer.tokenize(token) + for sub_token in sub_tokens: + tok_to_orig_index.append(i) + all_doc_tokens.append(sub_token) + + tok_start_position = None + tok_end_position = None + if is_training and example.is_impossible: + tok_start_position = -1 + tok_end_position = -1 + if is_training and not example.is_impossible: + tok_start_position = orig_to_tok_index[example.start_position] + if example.end_position < len(example.doc_tokens) - 1: + tok_end_position = orig_to_tok_index[example.end_position + 1] - 1 + else: + tok_end_position = len(all_doc_tokens) - 1 + (tok_start_position, tok_end_position) = _improve_answer_span( + all_doc_tokens, tok_start_position, tok_end_position, tokenizer, + example.orig_answer_text) + + # The -3 accounts for [CLS], [SEP] and [SEP] + max_tokens_for_doc = max_seq_length - len(query_tokens) - 3 + + # We can have documents that are longer than the maximum sequence length. + # To deal with this we do a sliding window approach, where we take chunks + # of the up to our max length with a stride of `doc_stride`. + _DocSpan = collections.namedtuple( # pylint: disable=invalid-name + "DocSpan", ["start", "length"]) + doc_spans = [] + start_offset = 0 + while start_offset < len(all_doc_tokens): + length = len(all_doc_tokens) - start_offset + if length > max_tokens_for_doc: + length = max_tokens_for_doc + doc_spans.append(_DocSpan(start=start_offset, length=length)) + if start_offset + length == len(all_doc_tokens): + break + start_offset += min(length, doc_stride) + + for (doc_span_index, doc_span) in enumerate(doc_spans): + tokens = [] + token_to_orig_map = {} + token_is_max_context = {} + segment_ids = [] + tokens.append("[CLS]") + segment_ids.append(0) + for token in query_tokens: + tokens.append(token) + segment_ids.append(0) + tokens.append("[SEP]") + segment_ids.append(0) + + for i in range(doc_span.length): + split_token_index = doc_span.start + i + token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index] + + is_max_context = _check_is_max_context(doc_spans, doc_span_index, + split_token_index) + token_is_max_context[len(tokens)] = is_max_context + tokens.append(all_doc_tokens[split_token_index]) + segment_ids.append(1) + tokens.append("[SEP]") + segment_ids.append(1) + + input_ids = tokenizer.convert_tokens_to_ids(tokens) + + # The mask has 1 for real tokens and 0 for padding tokens. Only real + # tokens are attended to. + input_mask = [1] * len(input_ids) + + # Zero-pad up to the sequence length. + while len(input_ids) < max_seq_length: + input_ids.append(0) + input_mask.append(0) + segment_ids.append(0) + + assert len(input_ids) == max_seq_length + assert len(input_mask) == max_seq_length + assert len(segment_ids) == max_seq_length + + start_position = None + end_position = None + if is_training and not example.is_impossible: + # For training, if our document chunk does not contain an annotation + # we throw it out, since there is nothing to predict. + doc_start = doc_span.start + doc_end = doc_span.start + doc_span.length - 1 + out_of_span = False + if (example.start_position < doc_start or + example.end_position < doc_start or + example.start_position > doc_end or example.end_position > doc_end): + out_of_span = True + if out_of_span: + start_position = 0 + end_position = 0 + else: + doc_offset = len(query_tokens) + 2 + start_position = tok_start_position - doc_start + doc_offset + end_position = tok_end_position - doc_start + doc_offset + + if is_training and example.is_impossible: + start_position = 0 + end_position = 0 + + if example_index < 20: + logger.info("*** Example ***") + logger.info("unique_id: %s" % (unique_id)) + logger.info("example_index: %s" % (example_index)) + logger.info("doc_span_index: %s" % (doc_span_index)) + logger.info("tokens: %s" % " ".join(tokens)) + logger.info("token_to_orig_map: %s" % " ".join([ + "%d:%d" % (x, y) for (x, y) in token_to_orig_map.items()])) + logger.info("token_is_max_context: %s" % " ".join([ + "%d:%s" % (x, y) for (x, y) in token_is_max_context.items() + ])) + logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) + logger.info( + "input_mask: %s" % " ".join([str(x) for x in input_mask])) + logger.info( + "segment_ids: %s" % " ".join([str(x) for x in segment_ids])) + if is_training and example.is_impossible: + logger.info("impossible example") + if is_training and not example.is_impossible: + answer_text = " ".join(tokens[start_position:(end_position + 1)]) + logger.info("start_position: %d" % (start_position)) + logger.info("end_position: %d" % (end_position)) + logger.info( + "answer: %s" % (answer_text)) + + features.append( + InputFeatures( + unique_id=unique_id, + example_index=example_index, + doc_span_index=doc_span_index, + tokens=tokens, + token_to_orig_map=token_to_orig_map, + token_is_max_context=token_is_max_context, + input_ids=input_ids, + input_mask=input_mask, + segment_ids=segment_ids, + start_position=start_position, + end_position=end_position, + is_impossible=example.is_impossible)) + unique_id += 1 + + return features + + +def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, + orig_answer_text): + """Returns tokenized answer spans that better match the annotated answer.""" + + # The SQuAD annotations are character based. We first project them to + # whitespace-tokenized words. But then after WordPiece tokenization, we can + # often find a "better match". For example: + # + # Question: What year was John Smith born? + # Context: The leader was John Smith (1895-1943). + # Answer: 1895 + # + # The original whitespace-tokenized answer will be "(1895-1943).". However + # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match + # the exact answer, 1895. + # + # However, this is not always possible. Consider the following: + # + # Question: What country is the top exporter of electornics? + # Context: The Japanese electronics industry is the lagest in the world. + # Answer: Japan + # + # In this case, the annotator chose "Japan" as a character sub-span of + # the word "Japanese". Since our WordPiece tokenizer does not split + # "Japanese", we just use "Japanese" as the annotation. This is fairly rare + # in SQuAD, but does happen. + tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text)) + + for new_start in range(input_start, input_end + 1): + for new_end in range(input_end, new_start - 1, -1): + text_span = " ".join(doc_tokens[new_start:(new_end + 1)]) + if text_span == tok_answer_text: + return (new_start, new_end) + + return (input_start, input_end) + + +def _check_is_max_context(doc_spans, cur_span_index, position): + """Check if this is the 'max context' doc span for the token.""" + + # Because of the sliding window approach taken to scoring documents, a single + # token can appear in multiple documents. E.g. + # Doc: the man went to the store and bought a gallon of milk + # Span A: the man went to the + # Span B: to the store and bought + # Span C: and bought a gallon of + # ... + # + # Now the word 'bought' will have two scores from spans B and C. We only + # want to consider the score with "maximum context", which we define as + # the *minimum* of its left and right context (the *sum* of left and + # right context will always be the same, of course). + # + # In the example the maximum context for 'bought' would be span C since + # it has 1 left context and 3 right context, while span B has 4 left context + # and 0 right context. + best_score = None + best_span_index = None + for (span_index, doc_span) in enumerate(doc_spans): + end = doc_span.start + doc_span.length - 1 + if position < doc_span.start: + continue + if position > end: + continue + num_left_context = position - doc_span.start + num_right_context = end - position + score = min(num_left_context, num_right_context) + 0.01 * doc_span.length + if best_score is None or score > best_score: + best_score = score + best_span_index = span_index + + return cur_span_index == best_span_index + + + +RawResult = collections.namedtuple("RawResult", + ["unique_id", "start_logits", "end_logits"]) + + +def write_predictions(all_examples, all_features, all_results, n_best_size, + max_answer_length, do_lower_case, output_prediction_file, + output_nbest_file, output_null_log_odds_file, verbose_logging, is_version2, null_score_diff_threshold): + """Write final predictions to the json file and log-odds of null if needed.""" + logger.info("Writing predictions to: %s" % (output_prediction_file)) + logger.info("Writing nbest to: %s" % (output_nbest_file)) + + example_index_to_features = collections.defaultdict(list) + for feature in all_features: + example_index_to_features[feature.example_index].append(feature) + + unique_id_to_result = {} + for result in all_results: + unique_id_to_result[result.unique_id] = result + + _PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name + "PrelimPrediction", + ["feature_index", "start_index", "end_index", "start_logit", "end_logit"]) + + all_predictions = collections.OrderedDict() + all_nbest_json = collections.OrderedDict() + scores_diff_json = collections.OrderedDict() + + for (example_index, example) in enumerate(all_examples): + features = example_index_to_features[example_index] + + prelim_predictions = [] + # keep track of the minimum score of null start+end of position 0 + score_null = 1000000 # large and positive + min_null_feature_index = 0 # the paragraph slice with min mull score + null_start_logit = 0 # the start logit at the slice with min null score + null_end_logit = 0 # the end logit at the slice with min null score + + for (feature_index, feature) in enumerate(features): + result = unique_id_to_result[feature.unique_id] + start_indexes = _get_best_indexes(result.start_logits, n_best_size) + end_indexes = _get_best_indexes(result.end_logits, n_best_size) + # if we could have irrelevant answers, get the min score of irrelevant + if is_version2: + feature_null_score = result.start_logits[0] + result.end_logits[0] + if feature_null_score < score_null: + score_null = feature_null_score + min_null_feature_index = feature_index + null_start_logit = result.start_logits[0] + null_end_logit = result.end_logits[0] + + for start_index in start_indexes: + for end_index in end_indexes: + # We could hypothetically create invalid predictions, e.g., predict + # that the start of the span is in the question. We throw out all + # invalid predictions. + if start_index >= len(feature.tokens): + continue + if end_index >= len(feature.tokens): + continue + if start_index not in feature.token_to_orig_map: + continue + if end_index not in feature.token_to_orig_map: + continue + if not feature.token_is_max_context.get(start_index, False): + continue + if end_index < start_index: + continue + length = end_index - start_index + 1 + if length > max_answer_length: + continue + prelim_predictions.append( + _PrelimPrediction( + feature_index=feature_index, + start_index=start_index, + end_index=end_index, + start_logit=result.start_logits[start_index], + end_logit=result.end_logits[end_index])) + + if is_version2: + prelim_predictions.append( + _PrelimPrediction( + feature_index=min_null_feature_index, + start_index=0, + end_index=0, + start_logit=null_start_logit, + end_logit=null_end_logit)) + + prelim_predictions = sorted( + prelim_predictions, + key=lambda x: (x.start_logit + x.end_logit), + reverse=True) + + _NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name + "NbestPrediction", ["text", "start_logit", "end_logit"]) + + seen_predictions = {} + nbest = [] + for pred in prelim_predictions: + if len(nbest) >= n_best_size: + break + feature = features[pred.feature_index] + if pred.start_index > 0: + tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)] + orig_doc_start = feature.token_to_orig_map[pred.start_index] + orig_doc_end = feature.token_to_orig_map[pred.end_index] + orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)] + tok_text = " ".join(tok_tokens) + + # De-tokenize WordPieces that have been split off. + tok_text = tok_text.replace(" ##", "") + tok_text = tok_text.replace("##", "") + + # Clean whitespace + tok_text = tok_text.strip() + tok_text = " ".join(tok_text.split()) + orig_text = " ".join(orig_tokens) + + final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging) + if final_text in seen_predictions: + continue + seen_predictions[final_text] = True + else: + final_text = "" + seen_predictions[final_text] = True + + nbest.append( + _NbestPrediction( + text=final_text, + start_logit=pred.start_logit, + end_logit=pred.end_logit)) + + # if we didn't inlude the empty option in the n-best, inlcude it + if is_version2: + if "" not in seen_predictions: + nbest.append( + _NbestPrediction( + text="", start_logit=null_start_logit, + end_logit=null_end_logit)) + + # In very rare edge cases we could have no valid predictions. So we + # just create a nonce prediction in this case to avoid failure. + if not nbest: + nbest.append( + _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0)) + + assert len(nbest) >= 1 + + total_scores = [] + best_non_null_entry = None + for entry in nbest: + total_scores.append(entry.start_logit + entry.end_logit) + if not best_non_null_entry: + if entry.text: + best_non_null_entry = entry + + probs = _compute_softmax(total_scores) + + nbest_json = [] + for (i, entry) in enumerate(nbest): + output = collections.OrderedDict() + output["text"] = entry.text + output["probability"] = probs[i] + output["start_logit"] = entry.start_logit + output["end_logit"] = entry.end_logit + nbest_json.append(output) + + assert len(nbest_json) >= 1 + + + if not is_version2: + all_predictions[example.qas_id] = nbest_json[0]["text"] + else: + # predict "" iff the null score - the score of best non-null > threshold + score_diff = score_null - best_non_null_entry.start_logit - ( + best_non_null_entry.end_logit) + scores_diff_json[example.qas_id] = score_diff + if score_diff > null_score_diff_threshold: + all_predictions[example.qas_id] = "" + else: + all_predictions[example.qas_id] = best_non_null_entry.text + all_nbest_json[example.qas_id] = nbest_json + + with open(output_prediction_file, "w") as writer: + writer.write(json.dumps(all_predictions, indent=4) + "\n") + + with open(output_nbest_file, "w") as writer: + writer.write(json.dumps(all_nbest_json, indent=4) + "\n") + + if is_version2: + with open(output_null_log_odds_file, "w") as writer: + writer.write(json.dumps(scores_diff_json, indent=4) + "\n") + + +def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False): + """Project the tokenized prediction back to the original text.""" + + # When we created the data, we kept track of the alignment between original + # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So + # now `orig_text` contains the span of our original text corresponding to the + # span that we predicted. + # + # However, `orig_text` may contain extra characters that we don't want in + # our prediction. + # + # For example, let's say: + # pred_text = steve smith + # orig_text = Steve Smith's + # + # We don't want to return `orig_text` because it contains the extra "'s". + # + # We don't want to return `pred_text` because it's already been normalized + # (the SQuAD eval script also does punctuation stripping/lower casing but + # our tokenizer does additional normalization like stripping accent + # characters). + # + # What we really want to return is "Steve Smith". + # + # Therefore, we have to apply a semi-complicated alignment heruistic between + # `pred_text` and `orig_text` to get a character-to-charcter alignment. This + # can fail in certain cases in which case we just return `orig_text`. + + def _strip_spaces(text): + ns_chars = [] + ns_to_s_map = collections.OrderedDict() + for (i, c) in enumerate(text): + if c == " ": + continue + ns_to_s_map[len(ns_chars)] = i + ns_chars.append(c) + ns_text = "".join(ns_chars) + return (ns_text, ns_to_s_map) + + # We first tokenize `orig_text`, strip whitespace from the result + # and `pred_text`, and check if they are the same length. If they are + # NOT the same length, the heuristic has failed. If they are the same + # length, we assume the characters are one-to-one aligned. + tokenizer = BasicTokenizer(do_lower_case=do_lower_case) + + tok_text = " ".join(tokenizer.tokenize(orig_text)) + + start_position = tok_text.find(pred_text) + if start_position == -1: + if verbose_logging: + logger.info( + "Unable to find text: '%s' in '%s'" % (pred_text, orig_text)) + return orig_text + end_position = start_position + len(pred_text) - 1 + + (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text) + (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text) + + if len(orig_ns_text) != len(tok_ns_text): + if verbose_logging: + logger.info("Length not equal after stripping spaces: '%s' vs '%s'", + orig_ns_text, tok_ns_text) + return orig_text + + # We then project the characters in `pred_text` back to `orig_text` using + # the character-to-character alignment. + tok_s_to_ns_map = {} + for (i, tok_index) in tok_ns_to_s_map.items(): + tok_s_to_ns_map[tok_index] = i + + orig_start_position = None + if start_position in tok_s_to_ns_map: + ns_start_position = tok_s_to_ns_map[start_position] + if ns_start_position in orig_ns_to_s_map: + orig_start_position = orig_ns_to_s_map[ns_start_position] + + if orig_start_position is None: + if verbose_logging: + logger.info("Couldn't map start position") + return orig_text + + orig_end_position = None + if end_position in tok_s_to_ns_map: + ns_end_position = tok_s_to_ns_map[end_position] + if ns_end_position in orig_ns_to_s_map: + orig_end_position = orig_ns_to_s_map[ns_end_position] + + if orig_end_position is None: + if verbose_logging: + logger.info("Couldn't map end position") + return orig_text + + output_text = orig_text[orig_start_position:(orig_end_position + 1)] + return output_text + + +def _get_best_indexes(logits, n_best_size): + """Get the n-best logits from a list.""" + index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True) + + best_indexes = [] + for i in range(len(index_and_score)): + if i >= n_best_size: + break + best_indexes.append(index_and_score[i][0]) + return best_indexes + + +def _compute_softmax(scores): + """Compute softmax probability over raw logits.""" + if not scores: + return [] + + max_score = None + for score in scores: + if max_score is None or score > max_score: + max_score = score + + exp_scores = [] + total_sum = 0.0 + for score in scores: + x = math.exp(score - max_score) + exp_scores.append(x) + total_sum += x + + probs = [] + for score in exp_scores: + probs.append(score / total_sum) + return probs + +def warmup_linear(x, warmup=0.002): + if x < warmup: + return x/warmup + return 1.0 - x + +def main(): + parser = argparse.ArgumentParser() + + ## Required parameters + parser.add_argument("--bert_model", default=None, type=str, required=True, + help="Bert pre-trained model selected in the list: bert-base-uncased, " + "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.") + parser.add_argument("--output_dir", default=None, type=str, required=True, + help="The output directory where the model checkpoints and predictions will be written.") + + ## Other parameters + parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json") + parser.add_argument("--predict_file", default=None, type=str, + help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json") + parser.add_argument("--max_seq_length", default=384, type=int, + help="The maximum total input sequence length after WordPiece tokenization. Sequences " + "longer than this will be truncated, and sequences shorter than this will be padded.") + parser.add_argument("--doc_stride", default=128, type=int, + help="When splitting up a long document into chunks, how much stride to take between chunks.") + parser.add_argument("--max_query_length", default=64, type=int, + help="The maximum number of tokens for the question. Questions longer than this will " + "be truncated to this length.") + parser.add_argument("--do_train", default=False, action='store_true', help="Whether to run training.") + parser.add_argument("--do_predict", default=False, action='store_true', help="Whether to run eval on the dev set.") + parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.") + parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.") + parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") + parser.add_argument("--num_train_epochs", default=3.0, type=float, + help="Total number of training epochs to perform.") + parser.add_argument("--warmup_proportion", default=0.1, type=float, + help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10% " + "of training.") + parser.add_argument("--n_best_size", default=20, type=int, + help="The total number of n-best predictions to generate in the nbest_predictions.json " + "output file.") + parser.add_argument("--max_answer_length", default=30, type=int, + help="The maximum length of an answer that can be generated. This is needed because the start " + "and end predictions are not conditioned on one another.") + parser.add_argument("--verbose_logging", default=False, action='store_true', + help="If true, all of the warnings related to data processing will be printed. " + "A number of warnings are expected for a normal SQuAD evaluation.") + parser.add_argument("--no_cuda", + default=False, + action='store_true', + help="Whether not to use CUDA when available") + parser.add_argument('--seed', + type=int, + default=42, + help="random seed for initialization") + parser.add_argument('--gradient_accumulation_steps', + type=int, + default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.") + parser.add_argument("--do_lower_case", + default=True, + action='store_true', + help="Whether to lower case the input text. True for uncased models, False for cased models.") + parser.add_argument("--local_rank", + type=int, + default=-1, + help="local_rank for distributed training on gpus") + parser.add_argument('--fp16', + default=False, + action='store_true', + help="Whether to use 16-bit float precision instead of 32-bit") + parser.add_argument('--loss_scale', + type=float, default=0, + help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" + "0 (default value): dynamic loss scaling.\n" + "Positive power of 2: static loss scaling value.\n") + parser.add_argument('--null_score_diff_threshold', + type=float, default=0.0, + help="If null_score - best_non_null is greater than the threshold predict null.") + + args = parser.parse_args() + + if args.local_rank == -1 or args.no_cuda: + device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") + n_gpu = torch.cuda.device_count() + else: + torch.cuda.set_device(args.local_rank) + device = torch.device("cuda", args.local_rank) + n_gpu = 1 + # Initializes the distributed backend which will take care of sychronizing nodes/GPUs + torch.distributed.init_process_group(backend='nccl') + logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format( + device, n_gpu, bool(args.local_rank != -1), args.fp16)) + + if args.gradient_accumulation_steps < 1: + raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format( + args.gradient_accumulation_steps)) + + args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps) + + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if n_gpu > 0: + torch.cuda.manual_seed_all(args.seed) + + if not args.do_train and not args.do_predict: + raise ValueError("At least one of `do_train` or `do_predict` must be True.") + + if args.do_train: + if not args.train_file: + raise ValueError( + "If `do_train` is True, then `train_file` must be specified.") + if args.do_predict: + if not args.predict_file: + raise ValueError( + "If `do_predict` is True, then `predict_file` must be specified.") + + if os.path.exists(args.output_dir) and os.listdir(args.output_dir): + raise ValueError("Output directory () already exists and is not empty.") + os.makedirs(args.output_dir, exist_ok=True) + + tokenizer = BertTokenizer.from_pretrained(args.bert_model) + + train_examples = None + num_train_steps = None + if args.do_train: + train_examples = read_squad_examples( + input_file=args.train_file, is_training=True) + num_train_steps = int( + len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) + + # Prepare model + model = BertForQuestionAnswering.from_pretrained(args.bert_model, + cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank)) + + if args.fp16: + model.half() + model.to(device) + if args.local_rank != -1: + try: + from apex.parallel import DistributedDataParallel as DDP + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") + + model = DDP(model) + elif n_gpu > 1: + model = torch.nn.DataParallel(model) + + # Prepare optimizer + param_optimizer = list(model.named_parameters()) + + # hack to remove pooler, which is not used + # thus it produce None grad that break apex + param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]] + + no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] + optimizer_grouped_parameters = [ + {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, + {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} + ] + + t_total = num_train_steps + if args.local_rank != -1: + t_total = t_total // torch.distributed.get_world_size() + if args.fp16: + try: + from apex.optimizers import FP16_Optimizer + from apex.optimizers import FusedAdam + except ImportError: + raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") + + optimizer = FusedAdam(optimizer_grouped_parameters, + lr=args.learning_rate, + bias_correction=False, + max_grad_norm=1.0) + if args.loss_scale == 0: + optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) + else: + optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale) + else: + optimizer = BertAdam(optimizer_grouped_parameters, + lr=args.learning_rate, + warmup=args.warmup_proportion, + t_total=t_total) + + global_step = 0 + if args.do_train: + cached_train_features_file = args.train_file+'_{0}_{1}_{2}_{3}'.format( + args.bert_model, str(args.max_seq_length), str(args.doc_stride), str(args.max_query_length)) + train_features = None + try: + with open(cached_train_features_file, "rb") as reader: + train_features = pickle.load(reader) + except: + train_features = convert_examples_to_features( + examples=train_examples, + tokenizer=tokenizer, + max_seq_length=args.max_seq_length, + doc_stride=args.doc_stride, + max_query_length=args.max_query_length, + is_training=True) + if args.local_rank == -1 or torch.distributed.get_rank() == 0: + logger.info(" Saving train features into cached file %s", cached_train_features_file) + with open(cached_train_features_file, "wb") as writer: + pickle.dump(train_features, writer) + logger.info("***** Running training *****") + logger.info(" Num orig examples = %d", len(train_examples)) + logger.info(" Num split examples = %d", len(train_features)) + logger.info(" Batch size = %d", args.train_batch_size) + logger.info(" Num steps = %d", num_train_steps) + all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long) + all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long) + all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long) + all_start_positions = torch.tensor([f.start_position for f in train_features], dtype=torch.long) + all_end_positions = torch.tensor([f.end_position for f in train_features], dtype=torch.long) + all_is_impossibles = torch.tensor([int(f.is_impossible) for f in train_features], dtype=torch.long) + train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, + all_start_positions, all_end_positions, all_is_impossibles) + if args.local_rank == -1: + train_sampler = RandomSampler(train_data) + else: + train_sampler = DistributedSampler(train_data) + train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) + + model.train() + for _ in trange(int(args.num_train_epochs), desc="Epoch"): + for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): + if n_gpu == 1: + batch = tuple(t.to(device) for t in batch) # multi-gpu does scattering it-self + input_ids, input_mask, segment_ids, start_positions, end_positions, _ = batch + loss = model(input_ids, segment_ids, input_mask, start_positions, end_positions) + if n_gpu > 1: + loss = loss.mean() # mean() to average on multi-gpu. + if args.gradient_accumulation_steps > 1: + loss = loss / args.gradient_accumulation_steps + + if args.fp16: + optimizer.backward(loss) + else: + loss.backward() + if (step + 1) % args.gradient_accumulation_steps == 0: + # modify learning rate with special warm up BERT uses + lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion) + for param_group in optimizer.param_groups: + param_group['lr'] = lr_this_step + optimizer.step() + optimizer.zero_grad() + global_step += 1 + + # Save a trained model + model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self + output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") + torch.save(model_to_save.state_dict(), output_model_file) + + # Load a trained model that you have fine-tuned + model_state_dict = torch.load(output_model_file) + model = BertForQuestionAnswering.from_pretrained(args.bert_model, state_dict=model_state_dict) + model.to(device) + + if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0): + eval_examples = read_squad_examples( + input_file=args.predict_file, is_training=False) + eval_features = convert_examples_to_features( + examples=eval_examples, + tokenizer=tokenizer, + max_seq_length=args.max_seq_length, + doc_stride=args.doc_stride, + max_query_length=args.max_query_length, + is_training=False) + + logger.info("***** Running predictions *****") + logger.info(" Num orig examples = %d", len(eval_examples)) + logger.info(" Num split examples = %d", len(eval_features)) + logger.info(" Batch size = %d", args.predict_batch_size) + + all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) + all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) + all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) + all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) + eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index) + # Run prediction for full data + eval_sampler = SequentialSampler(eval_data) + eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.predict_batch_size) + + model.eval() + all_results = [] + logger.info("Start evaluating") + for input_ids, input_mask, segment_ids, example_indices in tqdm(eval_dataloader, desc="Evaluating"): + if len(all_results) % 1000 == 0: + logger.info("Processing example: %d" % (len(all_results))) + input_ids = input_ids.to(device) + input_mask = input_mask.to(device) + segment_ids = segment_ids.to(device) + with torch.no_grad(): + batch_start_logits, batch_end_logits = model(input_ids, segment_ids, input_mask) + for i, example_index in enumerate(example_indices): + start_logits = batch_start_logits[i].detach().cpu().tolist() + end_logits = batch_end_logits[i].detach().cpu().tolist() + eval_feature = eval_features[example_index.item()] + unique_id = int(eval_feature.unique_id) + all_results.append(RawResult(unique_id=unique_id, + start_logits=start_logits, + end_logits=end_logits)) + output_prediction_file = os.path.join(args.output_dir, "predictions.json") + output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json") + output_null_log_odds_file = os.path.join(args.output_dir, "null_odds.json") + write_predictions(eval_examples, eval_features, all_results, + args.n_best_size, args.max_answer_length, + args.do_lower_case, output_prediction_file, + output_nbest_file, output_null_log_odds_file, args.verbose_logging, True, args.null_score_diff_threshold) + + +if __name__ == "__main__": + main() From 64326dccfba46890edc93b5bf7be6974b71984d5 Mon Sep 17 00:00:00 2001 From: Sang-Kil Park Date: Thu, 10 Jan 2019 21:51:39 +0900 Subject: [PATCH 099/111] Fix it to run properly even if without `--do_train` param. It was modified similar to `run_classifier.py`, and Fixed to run properly even if without `--do_train` param. --- examples/run_squad.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/run_squad.py b/examples/run_squad.py index 245aee0ff26a..39e9c501996d 100644 --- a/examples/run_squad.py +++ b/examples/run_squad.py @@ -782,7 +782,7 @@ def main(): raise ValueError( "If `do_predict` is True, then `predict_file` must be specified.") - if os.path.exists(args.output_dir) and os.listdir(args.output_dir): + if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train: raise ValueError("Output directory () already exists and is not empty.") os.makedirs(args.output_dir, exist_ok=True) @@ -916,7 +916,8 @@ def main(): # Save a trained model model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") - torch.save(model_to_save.state_dict(), output_model_file) + if args.do_train: + torch.save(model_to_save.state_dict(), output_model_file) # Load a trained model that you have fine-tuned model_state_dict = torch.load(output_model_file) From 506e5bb0c849b210b4284d975338963029f2c0d5 Mon Sep 17 00:00:00 2001 From: tholor Date: Fri, 11 Jan 2019 08:31:37 +0100 Subject: [PATCH 100/111] add do_lower_case arg and adjust model saving for lm finetuning. --- examples/run_lm_finetuning.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/examples/run_lm_finetuning.py b/examples/run_lm_finetuning.py index 39df2e99f832..35d1808bbc49 100644 --- a/examples/run_lm_finetuning.py +++ b/examples/run_lm_finetuning.py @@ -461,6 +461,9 @@ def main(): parser.add_argument("--on_memory", action='store_true', help="Whether to load train samples into memory or use disk") + parser.add_argument("--do_lower_case", + action='store_true', + help="Whether to lower case the input text. True for uncased models, False for cased models.") parser.add_argument("--local_rank", type=int, default=-1, @@ -612,12 +615,12 @@ def main(): optimizer.zero_grad() global_step += 1 + # Save a trained model logger.info("** ** * Saving fine - tuned model ** ** * ") + model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") - if n_gpu > 1: - torch.save(model.module.bert.state_dict(), output_model_file) - else: - torch.save(model.bert.state_dict(), output_model_file) + if args.do_train: + torch.save(model_to_save.state_dict(), output_model_file) def _truncate_seq_pair(tokens_a, tokens_b, max_length): From a2da2b4109f88bb1312867affa21a2b89e927794 Mon Sep 17 00:00:00 2001 From: Li Dong Date: Sun, 13 Jan 2019 19:51:11 +0800 Subject: [PATCH 101/111] [bug fix] args.do_lower_case is always True The "default=True" makes args.do_lower_case always True. ```python parser.add_argument("--do_lower_case", default=True, action='store_true') ``` --- examples/run_squad2.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/run_squad2.py b/examples/run_squad2.py index 2f7de749055c..fd35beef1e20 100644 --- a/examples/run_squad2.py +++ b/examples/run_squad2.py @@ -818,7 +818,6 @@ def main(): default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument("--do_lower_case", - default=True, action='store_true', help="Whether to lower case the input text. True for uncased models, False for cased models.") parser.add_argument("--local_rank", From 6c65cb2492c9c89b4dd988814303f10457dacec6 Mon Sep 17 00:00:00 2001 From: nhatchan <46347328+nhatchan@users.noreply.github.com> Date: Sun, 13 Jan 2019 21:09:13 +0900 Subject: [PATCH 102/111] lm_finetuning compatibility with Python 3.5 dicts are not ordered in Python 3.5 or prior, which is a cause of #175. This PR replaces one with a list, to keep its order. --- examples/run_lm_finetuning.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/run_lm_finetuning.py b/examples/run_lm_finetuning.py index 35d1808bbc49..35a2f797c7e3 100644 --- a/examples/run_lm_finetuning.py +++ b/examples/run_lm_finetuning.py @@ -139,11 +139,11 @@ def __getitem__(self, item): # transform sample to features cur_features = convert_example_to_features(cur_example, self.seq_len, self.tokenizer) - cur_tensors = {"input_ids": torch.tensor(cur_features.input_ids), - "input_mask": torch.tensor(cur_features.input_mask), - "segment_ids": torch.tensor(cur_features.segment_ids), - "lm_label_ids": torch.tensor(cur_features.lm_label_ids), - "is_next": torch.tensor(cur_features.is_next)} + cur_tensors = (torch.tensor(cur_features.input_ids), + torch.tensor(cur_features.input_mask), + torch.tensor(cur_features.segment_ids), + torch.tensor(cur_features.lm_label_ids), + torch.tensor(cur_features.is_next)) return cur_tensors @@ -592,7 +592,7 @@ def main(): tr_loss = 0 nb_tr_examples, nb_tr_steps = 0, 0 for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): - batch = tuple(t.to(device) for t in batch.values()) + batch = tuple(t.to(device) for t in batch) input_ids, input_mask, segment_ids, lm_label_ids, is_next = batch loss = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next) if n_gpu > 1: From 8edc898f63eb9fe3739c2d8f518788a7a5f13ce0 Mon Sep 17 00:00:00 2001 From: nhatchan <46347328+nhatchan@users.noreply.github.com> Date: Sun, 13 Jan 2019 21:23:19 +0900 Subject: [PATCH 103/111] Fix documentation (missing backslashes) This PR adds missing backslashes in LM Fine-tuning subsection in README.md. --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 915ccf635ad2..4c35e8b27479 100644 --- a/README.md +++ b/README.md @@ -506,13 +506,13 @@ Training one epoch on this corpus takes about 1:20h on 4 x NVIDIA Tesla P100 wit ```shell python run_lm_finetuning.py \ - --bert_model bert-base-cased - --do_train - --train_file samples/sample_text.txt - --output_dir models - --num_train_epochs 5.0 - --learning_rate 3e-5 - --train_batch_size 32 + --bert_model bert-base-cased \ + --do_train \ + --train_file samples/sample_text.txt \ + --output_dir models \ + --num_train_epochs 5.0 \ + --learning_rate 3e-5 \ + --train_batch_size 32 \ --max_seq_length 128 ``` From cd30565aed0f4260b8ea0a4ebc0a4245a851218a Mon Sep 17 00:00:00 2001 From: nhatchan <46347328+nhatchan@users.noreply.github.com> Date: Mon, 14 Jan 2019 13:35:40 +0900 Subject: [PATCH 104/111] Fix importing unofficial TF models Importing unofficial TF models seems to be working well, at least for me. This PR resolves #50. --- pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py b/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py index 120624bc1b49..1ff6c073e329 100755 --- a/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py +++ b/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py @@ -50,7 +50,7 @@ def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytor name = name.split('/') # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model - if any(n in ["adam_v", "adam_m"] for n in name): + if any(n in ["adam_v", "adam_m", "global_step"] for n in name): print("Skipping {}".format("/".join(name))) continue pointer = model From 35115eaf9393abcf1e5cb920928a1f03c0717c9d Mon Sep 17 00:00:00 2001 From: Davide Fiocco Date: Wed, 16 Jan 2019 21:05:24 +0100 Subject: [PATCH 105/111] (very) minor update to README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 4c35e8b27479..4e7d3bb1090b 100644 --- a/README.md +++ b/README.md @@ -76,7 +76,7 @@ The repository further comprises: - [`run_classifier.py`](./examples/run_classifier.py) - Show how to fine-tune an instance of `BertForSequenceClassification` on GLUE's MRPC task, - [`run_squad.py`](./examples/run_squad.py) - Show how to fine-tune an instance of `BertForQuestionAnswering` on SQuAD v1.0 task. - [`run_swag.py`](./examples/run_swag.py) - Show how to fine-tune an instance of `BertForMultipleChoice` on Swag task. - - [`run_lm_finetuning`](./examples/run_lm_finetuning.py) - Show how to fine-tune an instance of `BertForPretraining' on a target text corpus. + - [`run_lm_finetuning.py`](./examples/run_lm_finetuning.py) - Show how to fine-tune an instance of `BertForPretraining' on a target text corpus. These examples are detailed in the [Examples](#examples) section of this readme. From be9fa192f06b515118ba8a8116ea43a4a45aa902 Mon Sep 17 00:00:00 2001 From: liangtaiwan Date: Fri, 18 Jan 2019 00:41:55 +0800 Subject: [PATCH 106/111] don't save if do not train --- examples/run_squad2.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/run_squad2.py b/examples/run_squad2.py index fd35beef1e20..558b24764e87 100644 --- a/examples/run_squad2.py +++ b/examples/run_squad2.py @@ -1010,7 +1010,8 @@ def main(): # Save a trained model model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") - torch.save(model_to_save.state_dict(), output_model_file) + if args.do_train: + torch.save(model_to_save.state_dict(), output_model_file) # Load a trained model that you have fine-tuned model_state_dict = torch.load(output_model_file) From c69add30bf0315fb7b09400fe1f911579db44d3e Mon Sep 17 00:00:00 2001 From: Da Xiao Date: Mon, 28 Jan 2019 14:39:25 +0800 Subject: [PATCH 107/111] Add notebook and json files. --- Untitled.ipynb | 992 ++ Untitled1.ipynb | 32 + Untitled2.ipynb | 54 + Untitled3.ipynb | 804 ++ Untitled_likunlin-Copy1.ipynb | 816 ++ Untitled_likunlin.ipynb | 801 ++ Untitled_linzhuo.ipynb | 239 + Untitled_linzhuo_maskedlm.ipynb | 1082 ++ Untitled_zeoliao.ipynb | 1174 +++ WSC_associative_label.json | 1 + WSC_child_problem.json | 11443 ++++++++++++++++++++++ WSC_selected.txt | 8 + WSC_switched_label.json | 3005 ++++++ mnist/processed/test.pt | Bin 0 -> 7920431 bytes mnist/processed/training.pt | Bin 0 -> 47520431 bytes mnist/raw/t10k-images-idx3-ubyte | Bin 0 -> 7840016 bytes mnist/raw/t10k-labels-idx1-ubyte | Bin 0 -> 10008 bytes mnist/raw/train-images-idx3-ubyte | Bin 0 -> 47040016 bytes mnist/raw/train-labels-idx1-ubyte | Bin 0 -> 60008 bytes probe_pretrained_model.py | 274 + score.py | 92 + test_WSC_child_problem.py | 345 + "\347\273\203\344\271\240pytorch.ipynb" | 609 ++ 23 files changed, 21771 insertions(+) create mode 100644 Untitled.ipynb create mode 100644 Untitled1.ipynb create mode 100644 Untitled2.ipynb create mode 100644 Untitled3.ipynb create mode 100644 Untitled_likunlin-Copy1.ipynb create mode 100644 Untitled_likunlin.ipynb create mode 100644 Untitled_linzhuo.ipynb create mode 100644 Untitled_linzhuo_maskedlm.ipynb create mode 100644 Untitled_zeoliao.ipynb create mode 100644 WSC_associative_label.json create mode 100644 WSC_child_problem.json create mode 100644 WSC_selected.txt create mode 100644 WSC_switched_label.json create mode 100644 mnist/processed/test.pt create mode 100644 mnist/processed/training.pt create mode 100644 mnist/raw/t10k-images-idx3-ubyte create mode 100644 mnist/raw/t10k-labels-idx1-ubyte create mode 100644 mnist/raw/train-images-idx3-ubyte create mode 100644 mnist/raw/train-labels-idx1-ubyte create mode 100644 probe_pretrained_model.py create mode 100644 score.py create mode 100644 test_WSC_child_problem.py create mode 100644 "\347\273\203\344\271\240pytorch.ipynb" diff --git a/Untitled.ipynb b/Untitled.ipynb new file mode 100644 index 000000000000..b8af7579d801 --- /dev/null +++ b/Untitled.ipynb @@ -0,0 +1,992 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from IPython.core.interactiveshell import InteractiveShell\n", + "InteractiveShell.ast_node_interactivity = 'all'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# import seaborn as sns\n", + "import os\n", + "import json\n", + "\n", + "import numpy as np\n", + "import math\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "from pylab import rcParams\n", + "\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from pytorch_pretrained_bert import tokenization, BertTokenizer, BertModel, BertForMaskedLM, BertForPreTraining, BertConfig\n", + "from examples.extract_features import *" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "01/21/2019 10:22:02 - INFO - pytorch_pretrained_bert.tokenization - loading vocabulary file /nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/vocab.txt\n", + "01/21/2019 10:22:02 - INFO - pytorch_pretrained_bert.modeling - loading archive file /nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/\n", + "01/21/2019 10:22:02 - INFO - pytorch_pretrained_bert.modeling - Model config {\n", + " \"attention_probs_dropout_prob\": 0.1,\n", + " \"hidden_act\": \"gelu\",\n", + " \"hidden_dropout_prob\": 0.1,\n", + " \"hidden_size\": 768,\n", + " \"initializer_range\": 0.02,\n", + " \"intermediate_size\": 3072,\n", + " \"max_position_embeddings\": 512,\n", + " \"num_attention_heads\": 12,\n", + " \"num_hidden_layers\": 12,\n", + " \"type_vocab_size\": 2,\n", + " \"vocab_size\": 30522\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "class Args:\n", + " def __init__(self):\n", + " pass\n", + " \n", + "args = Args()\n", + "args.no_cuda = False\n", + "\n", + "CONFIG_NAME = 'bert_config.json'\n", + "BERT_DIR = '/nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/'\n", + "config_file = os.path.join(BERT_DIR, CONFIG_NAME)\n", + "config = BertConfig.from_json_file(config_file)\n", + "\n", + "tokenizer = BertTokenizer.from_pretrained(os.path.join(BERT_DIR, 'vocab.txt'))\n", + "model = BertForPreTraining.from_pretrained(BERT_DIR)\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() and not args.no_cuda else \"cpu\")\n", + "_ = model.to(device)\n", + "_ = model.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "def convert_text_to_examples(text):\n", + " examples = []\n", + " unique_id = 0\n", + " if True:\n", + " for line in text:\n", + " line = line.strip()\n", + " text_a = None\n", + " text_b = None\n", + " m = re.match(r\"^(.*) \\|\\|\\| (.*)$\", line)\n", + " if m is None:\n", + " text_a = line\n", + " else:\n", + " text_a = m.group(1)\n", + " text_b = m.group(2)\n", + " examples.append(\n", + " InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b))\n", + " unique_id += 1\n", + " return examples\n", + "\n", + "def convert_examples_to_features(examples, tokenizer, append_special_tokens=True, replace_mask=True, print_info=False):\n", + " features = []\n", + " for (ex_index, example) in enumerate(examples):\n", + " tokens_a = tokenizer.tokenize(example.text_a)\n", + " tokens_b = None\n", + " if example.text_b:\n", + " tokens_b = tokenizer.tokenize(example.text_b)\n", + "\n", + " tokens = []\n", + " input_type_ids = []\n", + " if append_special_tokens:\n", + " tokens.append(\"[CLS]\")\n", + " input_type_ids.append(0)\n", + " for token in tokens_a:\n", + " if replace_mask and token == '_': # XD\n", + " token = \"[MASK]\"\n", + " tokens.append(token)\n", + " input_type_ids.append(0)\n", + " if append_special_tokens:\n", + " tokens.append(\"[SEP]\")\n", + " input_type_ids.append(0)\n", + "\n", + " if tokens_b:\n", + " for token in tokens_b:\n", + " if replace_mask and token == '_': # XD\n", + " token = \"[MASK]\"\n", + " tokens.append(token)\n", + " input_type_ids.append(1)\n", + " if append_special_tokens:\n", + " tokens.append(\"[SEP]\")\n", + " input_type_ids.append(1)\n", + "\n", + " input_ids = tokenizer.convert_tokens_to_ids(tokens)\n", + " input_mask = [1] * len(input_ids)\n", + "\n", + " if ex_index < 5:\n", + "# logger.info(\"*** Example ***\")\n", + "# logger.info(\"unique_id: %s\" % (example.unique_id))\n", + " logger.info(\"tokens: %s\" % \" \".join([str(x) for x in tokens]))\n", + "# logger.info(\"input_ids: %s\" % \" \".join([str(x) for x in input_ids]))\n", + "# logger.info(\"input_mask: %s\" % \" \".join([str(x) for x in input_mask]))\n", + "# logger.info(\n", + "# \"input_type_ids: %s\" % \" \".join([str(x) for x in input_type_ids]))\n", + " \n", + " features.append(\n", + " InputFeatures(\n", + " unique_id=example.unique_id,\n", + " tokens=tokens,\n", + " input_ids=input_ids,\n", + " input_mask=input_mask,\n", + " input_type_ids=input_type_ids))\n", + " return features\n", + "\n", + "def copy_and_mask_feature(feature, masked_tokens=None):\n", + " import copy\n", + " tokens = feature.tokens\n", + " masked_positions = [tokens.index(t) for t in masked_tokens if t in tokens] \\\n", + " if masked_tokens is not None else range(len(tokens))\n", + " assert len(masked_positions) > 0\n", + " masked_feature_copies = []\n", + " for masked_pos in masked_positions:\n", + " feature_copy = copy.deepcopy(feature)\n", + " feature_copy.input_ids[masked_pos] = tokenizer.vocab[\"[MASK]\"]\n", + " masked_feature_copies.append(feature_copy)\n", + " return masked_feature_copies, masked_positions" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def show_lm_probs(tokens, input_ids, probs, topk=5, firstk=20):\n", + " def print_pair(token, prob, end_str='', hit_mark=' '):\n", + " if i < firstk:\n", + " # token = token.replace('', '').replace('\\n', '/n')\n", + " print('{}{: >3} | {: <12}'.format(hit_mark, int(round(prob*100)), token), end=end_str)\n", + " \n", + " ret = None\n", + " for i in range(len(tokens)):\n", + " ind_ = input_ids[i].item() if input_ids is not None else tokenizer.vocab[tokens[i]]\n", + " prob_ = probs[i][ind_].item()\n", + " print_pair(tokens[i], prob_, end_str='\\t')\n", + " values, indices = probs[i].topk(topk)\n", + " top_pairs = []\n", + " for j in range(topk):\n", + " ind, prob = indices[j].item(), values[j].item()\n", + " hit_mark = '*' if ind == ind_ else ' '\n", + " token = tokenizer.ids_to_tokens[ind]\n", + " print_pair(token, prob, hit_mark=hit_mark, end_str='' if j < topk - 1 else '\\n')\n", + " top_pairs.append((token, prob))\n", + " if tokens[i] == \"[MASK]\":\n", + " ret = top_pairs\n", + " return ret" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import colored\n", + "from colored import stylize\n", + "\n", + "def show_abnormals(tokens, probs, show_suggestions=False):\n", + " def gap2color(gap):\n", + " if gap <= 5:\n", + " return 'yellow_1'\n", + " elif gap <= 10:\n", + " return 'orange_1'\n", + " else:\n", + " return 'red_1'\n", + " \n", + " def print_token(token, suggestion, gap):\n", + " if gap == 0:\n", + " print(stylize(token + ' ', colored.fg('white') + colored.bg('black')), end='')\n", + " else:\n", + " print(stylize(token, colored.fg(gap2color(gap)) + colored.bg('black')), end='')\n", + " if show_suggestions and gap > 5:\n", + " print(stylize('/' + suggestion + ' ', colored.fg('green' if gap > 10 else 'cyan') + colored.bg('black')), end='')\n", + " else:\n", + " print(stylize(' ', colored.fg(gap2color(gap)) + colored.bg('black')), end='')\n", + " # print('/' + suggestion, end=' ')\n", + " # print('%.2f' % gap, end=' ')\n", + " \n", + " avg_gap = 0.\n", + " for i in range(1, len(tokens) - 1): # skip first [CLS] and last [SEP]\n", + " ind_ = tokenizer.vocab[tokens[i]]\n", + " prob_ = probs[i][ind_].item()\n", + " top_prob = probs[i].max().item()\n", + " top_ind = probs[i].argmax().item()\n", + " gap = math.log(top_prob) - math.log(prob_)\n", + " suggestion = tokenizer.ids_to_tokens[top_ind]\n", + " print_token(tokens[i], suggestion, gap)\n", + " avg_gap += gap\n", + " avg_gap /= (len(tokens) - 2)\n", + " print()\n", + " print(avg_gap)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "analyzed_cache = {}\n", + "\n", + "def analyze_text(text, masked_tokens=None, show_suggestions=False, show_firstk_probs=20):\n", + " if text[0] in analyzed_cache:\n", + " features, mlm_probs = analyzed_cache[text[0]]\n", + " given_mask = \"[MASK]\" in features[0].tokens\n", + " tokens = features[0].tokens\n", + " else:\n", + " examples = convert_text_to_examples(text)\n", + " features = convert_examples_to_features(examples, tokenizer, print_info=False)\n", + " given_mask = \"[MASK]\" in features[0].tokens\n", + " if not given_mask or masked_tokens is not None:\n", + " assert len(features) == 1\n", + " features, masked_positions = copy_and_mask_feature(features[0], masked_tokens=masked_tokens)\n", + "\n", + " input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)\n", + " input_type_ids = torch.tensor([f.input_type_ids for f in features], dtype=torch.long)\n", + " input_ids = input_ids.to(device)\n", + " input_type_ids = input_type_ids.to(device)\n", + "\n", + " mlm_logits, _ = model(input_ids, input_type_ids)\n", + " mlm_probs = F.softmax(mlm_logits, dim=-1)\n", + "\n", + " tokens = features[0].tokens\n", + " if not given_mask or masked_tokens is not None:\n", + " bsz, seq_len, vocab_size = mlm_probs.size()\n", + " assert bsz == len(masked_positions)\n", + " # reduced_mlm_probs = torch.Tensor(1, seq_len, vocab_size)\n", + " # for i in range(seq_len):\n", + " # reduced_mlm_probs[0, i] = mlm_probs[i, i]\n", + " reduced_mlm_probs = torch.Tensor(1, len(masked_positions), vocab_size)\n", + " for i, pos in enumerate(masked_positions):\n", + " reduced_mlm_probs[0, i] = mlm_probs[i, pos]\n", + " mlm_probs = reduced_mlm_probs\n", + " tokens = [tokens[i] for i in masked_positions]\n", + " \n", + " analyzed_cache[text[0]] = (features, mlm_probs)\n", + " \n", + " top_pairs = show_lm_probs(tokens, None, mlm_probs[0], firstk=show_firstk_probs)\n", + " if not given_mask:\n", + " show_abnormals(tokens, mlm_probs[0], show_suggestions=show_suggestions)\n", + " return top_pairs" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "01/21/2019 16:24:23 - INFO - examples.extract_features - tokens: [CLS] the [MASK] overtook the bicycle because it was going so fast . [SEP]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 | [CLS] \t 3 | . 1 | the 1 | , 1 | ) 1 | \" \n", + " 100 | the \t*100 | the 0 | a 0 | and 0 | \" 0 | but \n", + " 0 | [MASK] \t 10 | car 6 | rider 5 | horse 4 | man 3 | driver \n", + " 39 | overtook \t* 39 | overtook 9 | passed 7 | took 4 | stopped 4 | caught \n", + " 100 | the \t*100 | the 0 | his 0 | her 0 | its 0 | their \n", + " 99 | bicycle \t* 99 | bicycle 1 | bike 0 | cyclist 0 | cycling 0 | cycle \n", + " 100 | because \t*100 | because 0 | . 0 | due 0 | and 0 | since \n", + " 100 | it \t*100 | it 0 | its 0 | he 0 | that 0 | the \n", + " 100 | was \t*100 | was 0 | is 0 | started 0 | kept 0 | began \n", + " 100 | going \t*100 | going 0 | coming 0 | running 0 | moving 0 | working \n", + " 100 | so \t*100 | so 0 | too 0 | very 0 | such 0 | this \n", + " 100 | fast \t*100 | fast 0 | faster 0 | speed 0 | quick 0 | slow \n", + " 100 | . \t*100 | . 0 | ; 0 | ! 0 | ? 0 | , \n", + " 0 | [SEP] \t 50 | . 5 | him 5 | \" 4 | ! 3 | he \n" + ] + }, + { + "data": { + "text/plain": [ + "[('car', 0.0958171933889389),\n", + " ('rider', 0.06241898238658905),\n", + " ('horse', 0.0470595583319664),\n", + " ('man', 0.03596588224172592),\n", + " ('driver', 0.032284241169691086)]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# text = [\"Who was Jim Henson? Jim Henson _ a puppeteer.\"]\n", + "text = [\"I went to school by bus. I was very tired.\"]\n", + "text = [\"I thought that John defeated Mary. I was wrong. _ beat _.\"]\n", + "text = [\"Did John defeat Mary? No, _ beat _.\"]\n", + "text = [\"That mary defeated John contradicts the fact that _ beat _.\"]\n", + "text = [\"the _ overtook the bicycle because it was going so fast.\"]\n", + "# text = [\"After the outbreak of the disease, the Ministry of Agriculture and rural areas immediately sent a supervision team to the local. Local Emergency Response Mechanism has been activated in accordance with the requirements, to take blockade, culling, harmless treatment, disinfection and other treatment measures to all disease and culling of pigs for harmless treatment. At the same time, all live pigs and their products are prohibited from transferring out of the blockade area, and live pigs are not allowed to be transported into the blockade area. At present, all the above measures have been implemented.\"]\n", + "# text = [\"Early critics of Emily Dickinson's poetry mistook for simplemindedness the surface of artlessness that in fact she constructed with such innocence.\"]\n", + "analyze_text(text, show_firstk_probs=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def words2heads(attns, tokens, words):\n", + " positions = [tokens.index(word) for word in words]\n", + "\n", + " for layer in range(config.num_hidden_layers):\n", + " for head in range(config.num_attention_heads):\n", + " for pos_indices in [(0, 1), (1, 0)]:\n", + " from_pos, to_pos = positions[pos_indices[0]], positions[pos_indices[1]]\n", + " if attns[layer][head][from_pos].max(0)[1].item() == to_pos:\n", + " print('Layer %d, head %d: %s -> %s' % (layer, head, tokens[from_pos], tokens[to_pos]), end='\\t')\n", + " print(attns[layer][head][from_pos].topk(5)[0].data)\n", + "\n", + "def head2words(attns, tokens, layer, head):\n", + " for from_pos in range(len(tokens)):\n", + " to_pos = attns[layer][head][from_pos].max(0)[1].item()\n", + " from_word, to_word = tokens[from_pos], tokens[to_pos]\n", + " if from_word.isalpha() and to_word.isalpha():\n", + " print('%s @ %d -> %s @ %d' % (from_word, from_pos, to_word, to_pos), end='\\t')\n", + " print(attns[layer][head][from_pos].topk(5)[0].data)\n", + " \n", + "special_tokens = ['[CLS]', '[SEP]']\n", + "\n", + "def get_salient_heads(attns, tokens, attn_thld=0.5):\n", + " for layer in range(config.num_hidden_layers):\n", + " for head in range(config.num_attention_heads):\n", + " pos_pairs = []\n", + " for from_pos in range(1, len(tokens) - 1): # skip [CLS] and [SEP]\n", + " top_attn, to_pos = attns[layer][head][from_pos].max(0)\n", + " top_attn, to_pos = top_attn.item(), to_pos.item()\n", + " from_word, to_word = tokens[from_pos], tokens[to_pos]\n", + "# if from_word.isalpha() and to_word.isalpha() and top_attn >= attn_thld:\n", + " if abs(from_pos - to_pos) <= 1:\n", + "# print('Layer %d, head %d: %s @ %d -> %s @ %d' % (layer, head, from_word, from_pos, to_word, to_pos), end='\\t')\n", + "# print(attns[layer][head][from_pos].topk(5)[0].data)\n", + " pos_pairs.append((from_pos, to_pos))\n", + " \n", + " ratio = len(pos_pairs) / (len(tokens) - 2)\n", + " if ratio > 0.5:\n", + " print(ratio)\n", + " for from_pos, to_pos in pos_pairs:\n", + " print('Layer %d, head %d: %s @ %d -> %s @ %d' % (layer, head, tokens[from_pos], from_pos, tokens[to_pos], to_pos), end='\\t')\n", + " print(attns[layer][head][from_pos].topk(5)[0].data)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "01/10/2019 21:46:20 - INFO - examples.extract_features - tokens: [CLS] jim laughed because he was so happy . [SEP]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "jim @ 1 -> jim @ 1\ttensor([0.7248, 0.0842, 0.0656, 0.0407, 0.0319], device='cuda:0')\n" + ] + } + ], + "source": [ + "# text, words = [\"The trophy doesn't fit into the brown suitcase because the it is too large.\"], ['fit', 'large']\n", + "# text, words = [\"Mary couldn't beat John in the match because he was too strong.\"], ['beat', 'strong']\n", + "text, words = [\"John is taller than Mary because he is older.\"], ['taller', 'older']\n", + "# text, words = [\"The red ball is heavier than the blue ball because the red ball is bigger.\"], ['heavier', 'bigger']\n", + "text, words = [\"Jim laughed because he was so happy.\"], ['cried', 'sad']\n", + "# text, words = [\"Jim ate the cake quickly because he was so hungry.\"], ['ate', 'hungry']\n", + "# text, words = [\"Jim drank the juice quickly because he was so thirsty.\"], ['drank', 'thirsty']\n", + "# text, words = [\"Tom's drawing hangs high. It is above Susan's drawing\"], ['high', 'above']\n", + "# text, words = [\"Tom's drawing hangs low. It is below Susan's drawing\"], ['low', 'below']\n", + "# text, words = [\"John is taller than Mary . Mary is shorter than John.\"], ['taller', 'shorter']\n", + "# text, words = [\"The drawing is above the cabinet. The cabinet is below the drawing\"], ['above', 'below']\n", + "# text, words = [\"Jim is very thin . He is not fat.\"], ['thin', 'fat']\n", + "\n", + "features = convert_examples_to_features(convert_text_to_examples(text), tokenizer, print_info=False)\n", + "input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long).to(device)\n", + "input_type_ids = torch.tensor([f.input_type_ids for f in features], dtype=torch.long).to(device)\n", + "mlm_logits, _ = model(input_ids, input_type_ids)\n", + "mlm_probs = F.softmax(mlm_logits, dim=-1)\n", + "tokens = features[0].tokens\n", + "# top_pairs = show_lm_probs(tokens, None, mlm_probs[0], firstk=100)\n", + "\n", + "attn_name = 'enc_self_attns'\n", + "hypo = {attn_name: [model.bert.encoder.layer[i].attention.self.attention_probs[0] for i in range(config.num_hidden_layers)]}\n", + "key_labels = query_labels = tokens\n", + "labels_dict = {attn_name: (key_labels, query_labels)}\n", + "result_tuple = (hypo, config.num_attention_heads, labels_dict)\n", + "# plot_layer_attn(result_tuple, attn_name=attn_name, layer=10, heads=None)\n", + "\n", + "attns = hypo[attn_name]\n", + " \n", + "# words2heads(attns, tokens, words)\n", + "head2words(attns, tokens, 2, 10)\n", + "# get_salient_heads(attns, tokens, attn_thld=0.0)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "0，2\t-1\n", + "0，3\t-1\n", + "0，10\t+1 动宾\n", + "1，1\t+1 动介\n", + "1，4\t-1\n", + "1，11\t0\n", + "2，0\t+1**\n", + "2，6\t0**\n", + "2，9\t+1**\n", + "3，5\t-1\n", + "7，4\t-1\n", + "11，8\t0\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "head_size = config.hidden_size // config.num_attention_heads\n", + "layer = 1\n", + "head = 1 # 2, 3, 10\n", + "wq = model.bert.encoder.layer[layer].attention.self.query.weight.data.view(-1, config.num_attention_heads, head_size).permute(1, 0, 2)\n", + "wk = model.bert.encoder.layer[layer].attention.self.key.weight.data.view(-1, config.num_attention_heads, head_size).permute(1, 0, 2)\n", + "\n", + "wqk = torch.bmm(wq, wk.transpose(-1, -2))\n", + "# (wqk * wqk.transpose(-1, -2)).sum((1, 2)) / (wqk * wqk).sum((1, 2))\n", + "# plt.imshow(wqk[head]*wqk[head])\n", + "# plt.show()\n", + "\n", + "# q = torch.matmul(pos_emb, wq)\n", + "# k = torch.matmul(pos_emb_prev, wk)\n", + "# (q * k).sum((-2, -1))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "pos_emb = model.bert.embeddings.position_embeddings.weight.data\n", + "pos_emb_prev = torch.zeros_like(pos_emb)\n", + "pos_emb_next = torch.zeros_like(pos_emb)\n", + "pos_emb_prev[1:] = pos_emb[:-1]\n", + "pos_emb_next[:-1] = pos_emb[1:]\n", + "pos_emb, pos_emb_prev, pos_emb_next = pos_emb[1:-1], pos_emb_prev[1:-1], pos_emb_next[1:-1]\n", + "\n", + "# pos_q = torch.matmul(pos_emb, wk[head])\n", + "# plt.imshow(pos_q[:32])\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have the same hair color.',\n", + " 'Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have different hair colors.']" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "text = [\n", + " # same / different\n", + " \"Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have the same hair color.\",\n", + " \"Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have different hair colors.\",\n", + " \"Tom has yellow hair. Mary has black hair. John has black hair. Mary and _ have the same hair color.\",\n", + " # because / although\n", + " \"John is taller/shorter than Mary because/although _ is older/younger.\",\n", + " \"The red ball is heavier/lighter than the blue ball because/although the _ ball is bigger/smaller.\",\n", + " \"Charles did a lot better/worse than his good friend Nancy on the test because/although _ had/hadn't studied so hard.\",\n", + " \"The trophy doesn't fit into the brown suitcase because/although the _ is too small/large.\",\n", + " \"John thought that he would arrive earlier than Susan, but/and indeed _ was the first to arrive.\",\n", + " # reverse\n", + " \"John came then Mary came. They left in reverse order. _ left then _ left.\",\n", + " \"John came after Mary. They left in reverse order. _ left after _ .\",\n", + " \"John came first, then came Mary. They left in reverse order: _ left first, then left _ .\",\n", + " # compare sentences with same / opposite meaning, 2nd order\n", + " \"Though John is tall, Tom is taller than John. So John is _ than Tom.\",\n", + " \"Tom is taller than John. So _ is shorter than _.\",\n", + " # WSC-style: before /after\n", + " # \"Mary came before/after John. _ was late/early .\",\n", + " # yes / no, 2nd order\n", + " \"Was Tom taller than Susan? Yes, _ was taller.\",\n", + " # right / wrong, epistemic modality, 2nd order\n", + " \"John said/thought that the red ball was heavier than the blue ball. He was wrong. The _ ball was heavier\",\n", + " \"John was wrong in saying/thinking that the red ball was heavier than the blue ball. The _ ball was heavier\",\n", + " \"John said the rain was about to stop. Mary said the rain would continue. Later the rain stopped. _ was wrong/right.\",\n", + " \n", + " \"The trophy doesn't fit into the brown suitcase because/although the _ is too small/large.\",\n", + " \"John thanked Mary because _ had given help to _ . \",\n", + " \"John felt vindicated/crushed when his longtime rival Mary revealed that _ was the winner of the competition.\",\n", + " \"John couldn't see the stage with Mary in front of him because _ is so short/tall.\",\n", + " \"Although they ran at about the same speed, John beat Sally because _ had such a bad start.\",\n", + " \"The fish ate the worm. The _ was hungry/tasty.\",\n", + " \n", + " \"John beat Mary. _ won the game/e winner.\",\n", + "]\n", + "text" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "with open('WSC_switched_label.json') as f:\n", + " examples = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "with open('WSC_child_problem.json') as f:\n", + " cexamples = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "for ce in cexamples:\n", + " for s in ce['sentences']:\n", + " for a in s['answer0'] + s['answer1']:\n", + " a = a.lower()\n", + "# if a not in tokenizer.vocab:\n", + "# ce\n", + "# print(a, 'not in vocab!!!')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "for ce in cexamples:\n", + " if len(ce['sentences']) > 0:\n", + " e = examples[ce['index']]\n", + " assert ce['index'] == e['index']\n", + " e['score'] = all([s['score'] for s in ce['sentences']])\n", + " assert len(set([s['adjacent_ref'] for s in ce['sentences']])) == 1, 'adjcent_refs are different!'\n", + " e['adjacent_ref'] = ce['sentences'][0]['adjacent_ref']" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "\n", + "groups = defaultdict(list)\n", + "for e in examples:\n", + " if 'score' in e:\n", + " index = e['index']\n", + " if index < 252:\n", + " if index % 2 == 1:\n", + " index -= 1\n", + " elif index in [252, 253, 254]:\n", + " index = 252\n", + " else:\n", + " if index % 2 == 0:\n", + " index -= 1\n", + " groups[index].append(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(2,\n", + " \"The trophy doesn't fit into the brown suitcase because [it] is too large.\",\n", + " 'fit into:large/small'),\n", + " (4,\n", + " 'Joan made sure to thank Susan for all the help [she] had recieved.',\n", + " 'thank:receive/give'),\n", + " (10,\n", + " 'The delivery truck zoomed by the school bus because [it] was going so fast.',\n", + " 'zoom by:fast/slow'),\n", + " (12,\n", + " 'Frank felt vindicated when his longtime rival Bill revealed that [he] was the winner of the competition.',\n", + " 'vindicated/crushed:be the winner'),\n", + " (16,\n", + " 'The large ball crashed right through the table because [it] was made of steel.',\n", + " 'crash through:[hard]/[soft]'),\n", + " (18,\n", + " \"John couldn't see the stage with Billy in front of him because [he] is so short.\",\n", + " '[block]:short/tall'),\n", + " (20,\n", + " 'Tom threw his schoolbag down to Ray after [he] reached the top of the stairs.',\n", + " 'down to:top/bottom'),\n", + " (22,\n", + " 'Although they ran at about the same speed, Sue beat Sally because [she] had such a good start.',\n", + " 'beat:good/bad'),\n", + " (26,\n", + " \"Sam's drawing was hung just above Tina's and [it] did look much better with another one below it.\",\n", + " 'above/below'),\n", + " (28,\n", + " 'Anna did a lot better than her good friend Lucy on the test because [she] had studied so hard.',\n", + " 'better/worse:study hard'),\n", + " (30,\n", + " 'The firemen arrived after the police because [they] were coming from so far away.',\n", + " 'after/before:far away'),\n", + " (32,\n", + " \"Frank was upset with Tom because the toaster [he] had bought from him didn't work.\",\n", + " 'be upset with:buy from not work/sell not work'),\n", + " (36,\n", + " 'The sack of potatoes had been placed above the bag of flour, so [it] had to be moved first.',\n", + " 'above/below:moved first'),\n", + " (38,\n", + " 'Pete envies Martin although [he] is very successful.',\n", + " 'although/because'),\n", + " (42,\n", + " 'I poured water from the bottle into the cup until [it] was empty.',\n", + " 'pour:empty/full'),\n", + " (46,\n", + " \"Sid explained his theory to Mark but [he] couldn't convince him.\",\n", + " 'explain:convince/understand'),\n", + " (48,\n", + " \"Susan knew that Ann's son had been in a car accident, so [she] told her about it.\",\n", + " '?know tell:so/because'),\n", + " (50,\n", + " \"Joe's uncle can still beat him at tennis, even though [he] is 30 years younger.\",\n", + " 'beat:younger/older'),\n", + " (64,\n", + " 'In the middle of the outdoor concert, the rain started falling, but [it] continued until 10.',\n", + " 'but/and'),\n", + " (68,\n", + " 'Ann asked Mary what time the library closes, because [she] had forgotten.',\n", + " 'because/but'),\n", + " (84,\n", + " 'If the con artist has succeeded in fooling Sam, [he] would have gotten a lot of money.',\n", + " 'fool:get/lose'),\n", + " (92,\n", + " 'Alice tried frantically to stop her daughter from chatting at the party, leaving us to wonder why [she] was behaving so strangely.',\n", + " '?stop normal/stop abnormal:strange'),\n", + " (98,\n", + " \"I was trying to open the lock with the key, but someone had filled the keyhole with chewing gum, and I couldn't get [it] in.\",\n", + " 'put ... into filled with ... :get in/get out'),\n", + " (100,\n", + " 'The dog chased the cat, which ran up a tree. [It] waited at the bottom.',\n", + " 'up:at the bottom/at the top'),\n", + " (106,\n", + " 'John was doing research in the library when he heard a man humming and whistling. [He] was very annoyed.',\n", + " 'hear ... humming and whistling:annoyed/annoying'),\n", + " (108,\n", + " 'John was jogging through the park when he saw a man juggling watermelons. [He] was very impressed.',\n", + " 'see ... juggling watermelons:impressed/impressive'),\n", + " (132,\n", + " 'Jane knocked on the door, and Susan answered it. [She] invited her to come out.',\n", + " 'visit:invite come out/invite come in'),\n", + " (150,\n", + " 'Jackson was greatly influenced by Arnold, though [he] lived two centuries later.',\n", + " 'influence:later/earlier'),\n", + " (160,\n", + " 'The actress used to be named Terpsichore, but she changed it to Tina a few years ago, because she figured [it] was too hard to pronounce.',\n", + " 'change:hard/easy'),\n", + " (166,\n", + " 'Fred is the only man still alive who remembers my great-grandfather. [He] is a remarkable man.',\n", + " 'alive:is/was'),\n", + " (170,\n", + " \"In July, Kamtchatka declared war on Yakutsk. Since Yakutsk's army was much better equipped and ten times larger, [they] were defeated within weeks.\",\n", + " 'better equipped and large:defeated/victorious'),\n", + " (186,\n", + " 'When the sponsors of the bill got to the town hall, they were surprised to find that the room was full of opponents. [They] were very much in the minority.',\n", + " 'be full of:minority/majority'),\n", + " (188,\n", + " 'Everyone really loved the oatmeal cookies; only a few people liked the chocolate chip cookies. Next time, we should make more of [them] .',\n", + " 'like over:more/fewer'),\n", + " (190,\n", + " 'We had hoped to place copies of our newsletter on all the chairs in the auditorium, but there were simply not enough of [them] .',\n", + " 'place on all:not enough/too many'),\n", + " (196,\n", + " \"Steve follows Fred's example in everything. [He] admires him hugely.\",\n", + " 'follow:admire/influence'),\n", + " (198,\n", + " \"The table won't fit through the doorway because [it] is too wide.\",\n", + " 'fit through:wide/narrow'),\n", + " (200,\n", + " 'Grace was happy to trade me her sweater for my jacket. She thinks [it] looks dowdy on her.',\n", + " 'trade:dowdy/great'),\n", + " (202,\n", + " 'John hired Bill to take care of [him] .',\n", + " 'hire/hire oneself to:take care of'),\n", + " (204,\n", + " 'John promised Bill to leave, so an hour later [he] left.',\n", + " 'promise/order'),\n", + " (210,\n", + " \"Jane knocked on Susan's door but [she] did not get an answer.\",\n", + " 'knock:get an answer/answer'),\n", + " (212,\n", + " 'Joe paid the detective after [he] received the final report on the case.',\n", + " 'pay:receive/deliver'),\n", + " (226,\n", + " 'Bill passed the half-empty plate to John because [he] was full.',\n", + " 'pass the plate:full/hungry'),\n", + " (252,\n", + " 'George got free tickets to the play, but he gave them to Eric, even though [he] was particularly eager to see it.',\n", + " 'even though/because/not'),\n", + " (255,\n", + " \"Jane gave Joan candy because [she] wasn't hungry.\",\n", + " 'give:not hungry/hungry'),\n", + " (259,\n", + " 'James asked Robert for a favor but [he] was refused.',\n", + " 'ask for a favor:refuse/be refused`'),\n", + " (261,\n", + " 'Kirilov ceded the presidency to Shatov because [he] was less popular.',\n", + " 'cede:less popular/more popular'),\n", + " (263,\n", + " 'Emma did not pass the ball to Janie although [she] saw that she was open.',\n", + " 'not pass although:see open/open')]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def filter_dict(d, keys=['index', 'sentence', 'correct_answer', 'relational_word', 'is_associative', 'score']):\n", + " return {k: d[k] for k in d if k in keys}\n", + "\n", + "# ([[filter_dict(e) for e in eg] for eg in groups.values() if eg[0]['relational_word'] != 'none' and all([e['score'] for e in eg])])# / len([eg for eg in groups.values() if eg[0]['relational_word'] != 'none'])\n", + "# [(index, eg[0]['relational_word'], all([e['score'] for e in eg])) for index, eg in groups.items() if eg[0]['relational_word'] != 'none']\n", + "# len([filter_dict(e) for e in examples if 'score' in e and not e['score'] and e['adjacent_ref']])\n", + "# for e in examples:\n", + "# if e['index'] % 2 == 0:\n", + "# print(e['sentence'])\n", + "[(eg[0]['index'], eg[0]['sentence'], eg[0]['relational_word']) for index, eg in groups.items() if '/' in eg[0]['relational_word']]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "179" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(['because' in e['sentence'] for e in examples]) + \\\n", + "sum(['so ' in e['sentence'] for e in examples]) + \\\n", + "sum(['but ' in e['sentence'] for e in examples]) + \\\n", + "sum(['though' in e['sentence'] for e in examples])" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "# with open('WSC_switched_label.json', 'w') as f:\n", + "# json.dump(examples, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "vis_attn_topk = 3\n", + "\n", + "def has_chinese_label(labels):\n", + " labels = [label.split('->')[0].strip() for label in labels]\n", + " r = sum([len(label) > 1 for label in labels if label not in ['BOS', 'EOS']]) * 1. / (len(labels) - 1)\n", + " return 0 < r < 0.5 # r == 0 means empty query labels used in self attention\n", + "\n", + "def _plot_attn(ax1, attn_name, attn, key_labels, query_labels, col, color='b'):\n", + " assert len(query_labels) == attn.size(0)\n", + " assert len(key_labels) == attn.size(1)\n", + "\n", + " ax1.set_xlim([-1, 1])\n", + " ax1.set_xticks([])\n", + " ax2 = ax1.twinx()\n", + " nlabels = max(len(key_labels), len(query_labels))\n", + " pos = range(nlabels)\n", + " \n", + " if 'self' in attn_name and col < ncols - 1:\n", + " query_labels = ['' for _ in query_labels]\n", + "\n", + " for ax, labels in [(ax1, key_labels), (ax2, query_labels)]:\n", + " ax.set_yticks(pos)\n", + " if has_chinese_label(labels):\n", + " ax.set_yticklabels(labels, fontproperties=zhfont)\n", + " else:\n", + " ax.set_yticklabels(labels)\n", + " ax.set_ylim([nlabels - 1, 0])\n", + " ax.tick_params(width=0, labelsize='xx-large')\n", + "\n", + " for spine in ax.spines.values():\n", + " spine.set_visible(False)\n", + "\n", + "# mask, attn = filter_attn(attn)\n", + " for qi in range(attn.size(0)):\n", + "# if not mask[qi]:\n", + "# continue\n", + "# for ki in range(attn.size(1)):\n", + " for ki in attn[qi].topk(vis_attn_topk)[1]:\n", + " a = attn[qi, ki]\n", + " ax1.plot((-1, 1), (ki, qi), color, alpha=a)\n", + "# print(attn.mean(dim=0).topk(5)[0])\n", + "# ax1.barh(pos, attn.mean(dim=0).data.cpu().numpy())\n", + "\n", + "def plot_layer_attn(result_tuple, attn_name='dec_self_attns', layer=0, heads=None):\n", + " hypo, nheads, labels_dict = result_tuple\n", + " key_labels, query_labels = labels_dict[attn_name]\n", + " if heads is None:\n", + " heads = range(nheads)\n", + " else:\n", + " nheads = len(heads)\n", + " \n", + " stride = 2 if attn_name == 'dec_enc_attns' else 1\n", + " nlabels = max(len(key_labels), len(query_labels))\n", + " rcParams['figure.figsize'] = 20, int(round(nlabels * stride * nheads / 8 * 1.0))\n", + " \n", + " rows = nheads // ncols * stride\n", + " fig, axes = plt.subplots(rows, ncols)\n", + " \n", + " # for head in range(nheads):\n", + " for head_i, head in enumerate(heads):\n", + " row, col = head_i * stride // ncols, head_i * stride % ncols\n", + " ax1 = axes[row, col]\n", + " attn = hypo[attn_name][layer][head]\n", + " _plot_attn(ax1, attn_name, attn, key_labels, query_labels, col)\n", + " if attn_name == 'dec_enc_attns':\n", + " col = col + 1\n", + " axes[row, col].axis('off') # next subfig acts as blank place holder\n", + " # plt.suptitle('%s with %d heads, Layer %d' % (attn_name, nheads, layer), fontsize=20)\n", + " plt.show() \n", + " \n", + "ncols = 4" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{\n", + " \"attention_probs_dropout_prob\": 0.1,\n", + " \"hidden_act\": \"gelu\",\n", + " \"hidden_dropout_prob\": 0.1,\n", + " \"hidden_size\": 768,\n", + " \"initializer_range\": 0.02,\n", + " \"intermediate_size\": 3072,\n", + " \"max_position_embeddings\": 512,\n", + " \"num_attention_heads\": 12,\n", + " \"num_hidden_layers\": 12,\n", + " \"type_vocab_size\": 2,\n", + " \"vocab_size\": 30522\n", + "}" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config.num" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Untitled1.ipynb b/Untitled1.ipynb new file mode 100644 index 000000000000..81f040b9f3ee --- /dev/null +++ b/Untitled1.ipynb @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Untitled2.ipynb b/Untitled2.ipynb new file mode 100644 index 000000000000..2f7fae791c45 --- /dev/null +++ b/Untitled2.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([4, 4]) torch.Size([16]) torch.Size([2, 8])\n" + ] + } + ], + "source": [ + "x = torch.randn(4, 4)\n", + "y = x.view(16)\n", + "z = x.view(-1, 8) # the size -1 is inferred from other dimensions\n", + "print(x.size(), y.size(), z.size())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Untitled3.ipynb b/Untitled3.ipynb new file mode 100644 index 000000000000..eee4c4c83576 --- /dev/null +++ b/Untitled3.ipynb @@ -0,0 +1,804 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from IPython.core.interactiveshell import InteractiveShell\n", + "InteractiveShell.ast_node_interactivity = 'all'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import itertools\n", + "from itertools import product, chain\n", + "\n", + "from pytorch_pretrained_bert import tokenization, BertTokenizer, BertModel, BertForMaskedLM, BertForPreTraining, BertConfig" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "01/24/2019 22:16:56 - INFO - pytorch_pretrained_bert.tokenization - loading vocabulary file /nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/vocab.txt\n" + ] + } + ], + "source": [ + "CONFIG_NAME = 'bert_config.json'\n", + "BERT_DIR = '/nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/'\n", + "tokenizer = BertTokenizer.from_pretrained(os.path.join(BERT_DIR, 'vocab.txt'))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def reverse(l):\n", + " return list(reversed(l))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def mask(ent_str):\n", + " tokens = ent_str.strip().split()\n", + " if len(tokens) == 1:\n", + " return '[%s]' % tokens[0]\n", + " elif len(tokens) == 2:\n", + " assert tokens[0] == 'the', ent_str\n", + " return '%s [%s]' % (tokens[0], tokens[1])\n", + " else:\n", + " assert False, ent_str" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "A_template = \"{dt} {ent0} {rel} {dt} {ent1} {rel_suffix}\"\n", + "B_template = \"{dt} {ent} {pred}\"\n", + "\n", + "causal_templates = [[\"{A} because {B}.\"],# \"{B} so {A}.\"], \n", + " [\"{A} so {B}.\"],# \"{B} because {A}.\"]\n", + " ]\n", + "turning_templates = [[\"{A} although {B}.\"],# \"{B} but {A}.\"], \n", + " [\"{A} but {B}.\"],# \"{B} although {A}.\"]\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "def make_sentences(A_template, B_template, causal_templates, turning_templates,\n", + " index=-1, orig_sentence='', entities=[\"John\", \"Mary\"], entity_substitutes=None, determiner=\"\", \n", + " packed_relations=[\"rel/~rel\", \"rev_rel/~rev_rel\"], packed_relation_substitutes=None, relation_suffix=\"\",\n", + " packed_predicates=[\"pred0/~pred0\", \"pred1/~pred1\"], predicate_substitutes=None,\n", + " predicate_dichotomy=True, reverse_causal=False):\n", + " assert entities[0].lower() in tokenizer.vocab , entities[0]\n", + " assert entities[1].lower() in tokenizer.vocab , entities[1]\n", + " \n", + " relations, neg_relations = zip(*[rel.split(\"/\") for rel in packed_relations])\n", + " relations, neg_relations = list(relations), list(neg_relations)\n", + " predicates, neg_predicates = zip(*[pred.split(\"/\") for pred in packed_predicates])\n", + " predicates, neg_predicates = list(predicates), list(neg_predicates)\n", + " \n", + " As = [A_template.format(dt=determiner, ent0=ent0, ent1=ent1, rel=rel, rel_suffix=relation_suffix) \n", + " for ent0, ent1, rel in [entities + relations[:1], reverse(entities) + reverse(relations)[:1]]]\n", + " negAs = [A_template.format(dt=determiner, ent0=ent0, ent1=ent1, rel=rel, rel_suffix=relation_suffix) \n", + " for ent0, ent1, rel in [entities + neg_relations[:1], reverse(entities) + reverse(neg_relations)[:1]]]\n", + "\n", + " Bs = [B_template.format(dt=determiner, ent=mask(ent), pred=pred) for ent, pred in zip(entities, predicates)]\n", + " negBs = [B_template.format(dt=determiner, ent=mask(ent), pred=pred) for ent, pred in zip(entities, neg_predicates)]\n", + " if predicate_dichotomy:\n", + " Bs += [B_template.format(dt=determiner, ent=mask(ent), pred=pred) for ent, pred in zip(entities, reversed(neg_predicates))]\n", + " negBs += [B_template.format(dt=determiner, ent=mask(ent), pred=pred) for ent, pred in zip(entities, reversed(predicates))]\n", + "\n", + " def form_sentences(sentence_template, As, Bs):\n", + " return [\" \".join(sentence_template.format(A=A, B=B).split()) for A, B in product(As, Bs)]\n", + "\n", + " causal_sentences = []\n", + " for causal_template in causal_templates[int(reverse_causal)]:\n", + " for A, B in [(As, Bs), (negAs, negBs)]:\n", + " causal_sentences.extend(form_sentences(causal_template, A, B))\n", + "\n", + " turning_sentences = []\n", + " for turning_template in turning_templates[int(reverse_causal)]:\n", + " for A, B in [(As, negBs), (negAs, Bs)]:\n", + " turning_sentences.extend(form_sentences(turning_template, A, B))\n", + " \n", + " sentences = causal_sentences + turning_sentences\n", + " substituted_sentences = sentences\n", + " \n", + " if packed_relation_substitutes is not None:\n", + " packed_relation_substitutes = list(itertools.product(packed_relations[:1] + packed_relation_substitutes[0], \n", + " packed_relations[1:] + packed_relation_substitutes[1]))\n", + " substituted_sentences = []\n", + " for packed_sub_relations in packed_relation_substitutes:\n", + " sub_relations, sub_neg_relations = zip(*[rel.split(\"/\") for rel in packed_sub_relations])\n", + " substituted_sentences += [sent.replace(relations[0], sub_relations[0]).replace(relations[1], sub_relations[1])\n", + " .replace(neg_relations[0], sub_neg_relations[0]).replace(neg_relations[1], sub_neg_relations[1]) \n", + " for sent in sentences]\n", + " substituted_sentences = list(set(substituted_sentences))\n", + " \n", + " if entity_substitutes is not None:\n", + " for sub in entity_substitutes:\n", + " for ent in sub:\n", + " assert ent.lower() in tokenizer.vocab , ent + \" not in BERT vocab\"\n", + " assert len(set(chain.from_iterable(entity_substitutes))) == 4, entity_substitutes\n", + " assert len(set(chain.from_iterable(entity_substitutes)).union(set(entities))) == 6 \n", + " \n", + " entity_substitutes = list(itertools.product(entities[:1] + entity_substitutes[0], entities[1:] + entity_substitutes[1]))\n", + " substituted_sentences = [sent.replace(entities[0], sub[0]).replace(entities[1], sub[1]) \n", + " for sent in substituted_sentences for sub in entity_substitutes]\n", + " return causal_sentences, turning_sentences, substituted_sentences" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "frames = \\\n", + "[\n", + " {\n", + " \"index\": 2,\n", + " \"orig_sentence\": \"The trophy doesn't fit into the brown suitcase because [it] is too large/small.\",\n", + " \"entities\": [\"trophy\", \"suitcase\"],\n", + " \"entitity_substitutes\": [[\"ball\", \"toy\"], [\"bag\", \"box\"]],\n", + " \"determiner\": \"the\",\n", + " \"packed_relations\": [\"doesn't fit into/can fit into\", \"doesn't hold/can hold\"],\n", + " \"packed_relation_substitutes\": [[\"can't be put into/can be put into\"], [\"doesn't have enough room for/has enough room for\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"is large/isn't large\", \"is small/isn't small\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": False\n", + " },\n", + " {\n", + " \"index\": 4,\n", + " \"orig_sentence\": \"Joan made sure to thank Susan for all the help [she] had recieved/given.\",\n", + " \"entities\": [\"John\", \"Susan\"],\n", + " \"entity_substitutes\": [[\"David\", \"Michael\"], [\"Mary\", \"Linda\"]],\n", + " \"determiner\": \"\",\n", + " \"packed_relations\": [\"thanked/didn't thank\", \"took good care of/didn't good care of\"],\n", + " \"packed_relation_substitutes\": [[\"felt grateful to/didn't feel grateful to\"], [\"was appreciated by/wasn't appreciated by\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"had received a lot of help/hadn't received a lot of help\", \"had given a lot of help/hadn't given a lot of help\"],\n", + " \"predicate_dichotomy\": False,\n", + " \"reverse_causal\": False\n", + " },\n", + " {\n", + " \"index\": 4000,\n", + " \"orig_sentence\": \"John gave a lot of money to Susan because [he] was very rich/poor.\",\n", + " \"entities\": [\"John\", \"Susan\"],\n", + " \"entity_substitutes\": [[\"David\", \"Michael\"], [\"Mary\", \"Linda\"]],\n", + " \"determiner\": \"\",\n", + " \"packed_relations\": [\"gave a lot of money to/didn't give a lot of money to\", \"received a lot of money from/didn't receive a lot of money from\"],\n", + " \"packed_relation_substitutes\": [[\"subsidized/didn't subsidize\"], [\"borrowed a lot of money from/didn't borrow any money from\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"was rich/wasn't rich\", \"was poor/wasn't poor\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": False\n", + " },\n", + " {\n", + " \"index\": 10,\n", + " \"orig_sentence\": \"The delivery truck zoomed by the school bus because [it] was going so fast/slow.\",\n", + " \"entities\": [\"truck\", \"bus\"],\n", + " \"entity_substitutes\": [[\"car\", \"ambulance\"], [\"bicycle\", \"tram\"]],\n", + " \"determiner\": \"the\",\n", + " \"packed_relations\": [\"overtook/couldn't overtake\", \"fell far behind/didn't fall far behind\"],\n", + " \"packed_relation_substitutes\": [[\"zoomed by/didn't pass\"], [\"was left behind/wasn't left far behind\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"was going fast/wasn't going fast\", \"was going slow/wasn't going slow\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": False\n", + " },\n", + " {\n", + " \"index\": 12,\n", + " \"orig_sentence\": \"Frank felt vindicated/crushed when his longtime rival Bill revealed that [he] was the winner of the competition.\",\n", + " \"entities\": [\"John\", \"Susan\"],\n", + " \"entity_substitutes\": [[\"David\", \"Michael\"], [\"Mary\", \"Linda\"]],\n", + " \"determiner\": \"\",\n", + " \"packed_relations\": [\"beat/didn't beat\", \"lost to/didn't lose to\"],\n", + " \"relation_suffix\": \"in the game\",\n", + " \"packed_predicates\": [\"was happy/wasn't happy\", \"was sad/wasn't sad\"],\n", + " \"packed_relation_substitutes\": None,\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": True\n", + " },\n", + " {\n", + " \"index\": 16,\n", + " \"orig_sentence\": \"The large ball crashed right through the table because [it] was made of steel/styrofoam.\",\n", + " \"entities\": [\"ball\", \"board\"],\n", + " \"substitutes\": [[\"bullet\", \"arrow\"], [\"shield\", \"disk\"]],\n", + " \"determiner\": \"the\",\n", + " \"relations\": [\"crashed right through\", \"failed to block\"],\n", + " \"neg_relations\": [\"didn't crash through\", \"blocked\"],\n", + " \"relation_suffix\": \"\",\n", + " \"predicates\": [\"was hard\", \"was soft\"],\n", + " \"neg_predicates\": [\"wasn't hard\", \"wasn't soft\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": False\n", + " },\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "causal_sentences, turning_sentences, substituted_sentences = \\\n", + " make_sentences(A_template, B_template, causal_templates, turning_templates, **frames[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['John beat Susan in the game so [John] was happy.',\n", + " 'John beat Susan in the game so [Susan] was sad.',\n", + " \"John beat Susan in the game so [John] wasn't sad.\",\n", + " \"John beat Susan in the game so [Susan] wasn't happy.\",\n", + " 'Susan lost to John in the game so [John] was happy.',\n", + " 'Susan lost to John in the game so [Susan] was sad.',\n", + " \"Susan lost to John in the game so [John] wasn't sad.\",\n", + " \"Susan lost to John in the game so [Susan] wasn't happy.\",\n", + " \"John didn't beat Susan in the game so [John] wasn't happy.\",\n", + " \"John didn't beat Susan in the game so [Susan] wasn't sad.\",\n", + " \"John didn't beat Susan in the game so [John] was sad.\",\n", + " \"John didn't beat Susan in the game so [Susan] was happy.\",\n", + " \"Susan didn't lose to John in the game so [John] wasn't happy.\",\n", + " \"Susan didn't lose to John in the game so [Susan] wasn't sad.\",\n", + " \"Susan didn't lose to John in the game so [John] was sad.\",\n", + " \"Susan didn't lose to John in the game so [Susan] was happy.\",\n", + " \"John beat Susan in the game but [John] wasn't happy.\",\n", + " \"John beat Susan in the game but [Susan] wasn't sad.\",\n", + " 'John beat Susan in the game but [John] was sad.',\n", + " 'John beat Susan in the game but [Susan] was happy.',\n", + " \"Susan lost to John in the game but [John] wasn't happy.\",\n", + " \"Susan lost to John in the game but [Susan] wasn't sad.\",\n", + " 'Susan lost to John in the game but [John] was sad.',\n", + " 'Susan lost to John in the game but [Susan] was happy.',\n", + " \"John didn't beat Susan in the game but [John] was happy.\",\n", + " \"John didn't beat Susan in the game but [Susan] was sad.\",\n", + " \"John didn't beat Susan in the game but [John] wasn't sad.\",\n", + " \"John didn't beat Susan in the game but [Susan] wasn't happy.\",\n", + " \"Susan didn't lose to John in the game but [John] was happy.\",\n", + " \"Susan didn't lose to John in the game but [Susan] was sad.\",\n", + " \"Susan didn't lose to John in the game but [John] wasn't sad.\",\n", + " \"Susan didn't lose to John in the game but [Susan] wasn't happy.\"]" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "['John beat Susan in the game so [John] was happy.',\n", + " 'John beat Mary in the game so [John] was happy.',\n", + " 'John beat Linda in the game so [John] was happy.',\n", + " 'David beat Susan in the game so [David] was happy.',\n", + " 'David beat Mary in the game so [David] was happy.',\n", + " 'David beat Linda in the game so [David] was happy.',\n", + " 'Michael beat Susan in the game so [Michael] was happy.',\n", + " 'Michael beat Mary in the game so [Michael] was happy.',\n", + " 'Michael beat Linda in the game so [Michael] was happy.',\n", + " 'John beat Susan in the game so [Susan] was sad.',\n", + " 'John beat Mary in the game so [Mary] was sad.',\n", + " 'John beat Linda in the game so [Linda] was sad.',\n", + " 'David beat Susan in the game so [Susan] was sad.',\n", + " 'David beat Mary in the game so [Mary] was sad.',\n", + " 'David beat Linda in the game so [Linda] was sad.',\n", + " 'Michael beat Susan in the game so [Susan] was sad.',\n", + " 'Michael beat Mary in the game so [Mary] was sad.',\n", + " 'Michael beat Linda in the game so [Linda] was sad.',\n", + " \"John beat Susan in the game so [John] wasn't sad.\",\n", + " \"John beat Mary in the game so [John] wasn't sad.\",\n", + " \"John beat Linda in the game so [John] wasn't sad.\",\n", + " \"David beat Susan in the game so [David] wasn't sad.\",\n", + " \"David beat Mary in the game so [David] wasn't sad.\",\n", + " \"David beat Linda in the game so [David] wasn't sad.\",\n", + " \"Michael beat Susan in the game so [Michael] wasn't sad.\",\n", + " \"Michael beat Mary in the game so [Michael] wasn't sad.\",\n", + " \"Michael beat Linda in the game so [Michael] wasn't sad.\",\n", + " \"John beat Susan in the game so [Susan] wasn't happy.\",\n", + " \"John beat Mary in the game so [Mary] wasn't happy.\",\n", + " \"John beat Linda in the game so [Linda] wasn't happy.\",\n", + " \"David beat Susan in the game so [Susan] wasn't happy.\",\n", + " \"David beat Mary in the game so [Mary] wasn't happy.\",\n", + " \"David beat Linda in the game so [Linda] wasn't happy.\",\n", + " \"Michael beat Susan in the game so [Susan] wasn't happy.\",\n", + " \"Michael beat Mary in the game so [Mary] wasn't happy.\",\n", + " \"Michael beat Linda in the game so [Linda] wasn't happy.\",\n", + " 'Susan lost to John in the game so [John] was happy.',\n", + " 'Mary lost to John in the game so [John] was happy.',\n", + " 'Linda lost to John in the game so [John] was happy.',\n", + " 'Susan lost to David in the game so [David] was happy.',\n", + " 'Mary lost to David in the game so [David] was happy.',\n", + " 'Linda lost to David in the game so [David] was happy.',\n", + " 'Susan lost to Michael in the game so [Michael] was happy.',\n", + " 'Mary lost to Michael in the game so [Michael] was happy.',\n", + " 'Linda lost to Michael in the game so [Michael] was happy.',\n", + " 'Susan lost to John in the game so [Susan] was sad.',\n", + " 'Mary lost to John in the game so [Mary] was sad.',\n", + " 'Linda lost to John in the game so [Linda] was sad.',\n", + " 'Susan lost to David in the game so [Susan] was sad.',\n", + " 'Mary lost to David in the game so [Mary] was sad.',\n", + " 'Linda lost to David in the game so [Linda] was sad.',\n", + " 'Susan lost to Michael in the game so [Susan] was sad.',\n", + " 'Mary lost to Michael in the game so [Mary] was sad.',\n", + " 'Linda lost to Michael in the game so [Linda] was sad.',\n", + " \"Susan lost to John in the game so [John] wasn't sad.\",\n", + " \"Mary lost to John in the game so [John] wasn't sad.\",\n", + " \"Linda lost to John in the game so [John] wasn't sad.\",\n", + " \"Susan lost to David in the game so [David] wasn't sad.\",\n", + " \"Mary lost to David in the game so [David] wasn't sad.\",\n", + " \"Linda lost to David in the game so [David] wasn't sad.\",\n", + " \"Susan lost to Michael in the game so [Michael] wasn't sad.\",\n", + " \"Mary lost to Michael in the game so [Michael] wasn't sad.\",\n", + " \"Linda lost to Michael in the game so [Michael] wasn't sad.\",\n", + " \"Susan lost to John in the game so [Susan] wasn't happy.\",\n", + " \"Mary lost to John in the game so [Mary] wasn't happy.\",\n", + " \"Linda lost to John in the game so [Linda] wasn't happy.\",\n", + " \"Susan lost to David in the game so [Susan] wasn't happy.\",\n", + " \"Mary lost to David in the game so [Mary] wasn't happy.\",\n", + " \"Linda lost to David in the game so [Linda] wasn't happy.\",\n", + " \"Susan lost to Michael in the game so [Susan] wasn't happy.\",\n", + " \"Mary lost to Michael in the game so [Mary] wasn't happy.\",\n", + " \"Linda lost to Michael in the game so [Linda] wasn't happy.\",\n", + " \"John didn't beat Susan in the game so [John] wasn't happy.\",\n", + " \"John didn't beat Mary in the game so [John] wasn't happy.\",\n", + " \"John didn't beat Linda in the game so [John] wasn't happy.\",\n", + " \"David didn't beat Susan in the game so [David] wasn't happy.\",\n", + " \"David didn't beat Mary in the game so [David] wasn't happy.\",\n", + " \"David didn't beat Linda in the game so [David] wasn't happy.\",\n", + " \"Michael didn't beat Susan in the game so [Michael] wasn't happy.\",\n", + " \"Michael didn't beat Mary in the game so [Michael] wasn't happy.\",\n", + " \"Michael didn't beat Linda in the game so [Michael] wasn't happy.\",\n", + " \"John didn't beat Susan in the game so [Susan] wasn't sad.\",\n", + " \"John didn't beat Mary in the game so [Mary] wasn't sad.\",\n", + " \"John didn't beat Linda in the game so [Linda] wasn't sad.\",\n", + " \"David didn't beat Susan in the game so [Susan] wasn't sad.\",\n", + " \"David didn't beat Mary in the game so [Mary] wasn't sad.\",\n", + " \"David didn't beat Linda in the game so [Linda] wasn't sad.\",\n", + " \"Michael didn't beat Susan in the game so [Susan] wasn't sad.\",\n", + " \"Michael didn't beat Mary in the game so [Mary] wasn't sad.\",\n", + " \"Michael didn't beat Linda in the game so [Linda] wasn't sad.\",\n", + " \"John didn't beat Susan in the game so [John] was sad.\",\n", + " \"John didn't beat Mary in the game so [John] was sad.\",\n", + " \"John didn't beat Linda in the game so [John] was sad.\",\n", + " \"David didn't beat Susan in the game so [David] was sad.\",\n", + " \"David didn't beat Mary in the game so [David] was sad.\",\n", + " \"David didn't beat Linda in the game so [David] was sad.\",\n", + " \"Michael didn't beat Susan in the game so [Michael] was sad.\",\n", + " \"Michael didn't beat Mary in the game so [Michael] was sad.\",\n", + " \"Michael didn't beat Linda in the game so [Michael] was sad.\",\n", + " \"John didn't beat Susan in the game so [Susan] was happy.\",\n", + " \"John didn't beat Mary in the game so [Mary] was happy.\",\n", + " \"John didn't beat Linda in the game so [Linda] was happy.\",\n", + " \"David didn't beat Susan in the game so [Susan] was happy.\",\n", + " \"David didn't beat Mary in the game so [Mary] was happy.\",\n", + " \"David didn't beat Linda in the game so [Linda] was happy.\",\n", + " \"Michael didn't beat Susan in the game so [Susan] was happy.\",\n", + " \"Michael didn't beat Mary in the game so [Mary] was happy.\",\n", + " \"Michael didn't beat Linda in the game so [Linda] was happy.\",\n", + " \"Susan didn't lose to John in the game so [John] wasn't happy.\",\n", + " \"Mary didn't lose to John in the game so [John] wasn't happy.\",\n", + " \"Linda didn't lose to John in the game so [John] wasn't happy.\",\n", + " \"Susan didn't lose to David in the game so [David] wasn't happy.\",\n", + " \"Mary didn't lose to David in the game so [David] wasn't happy.\",\n", + " \"Linda didn't lose to David in the game so [David] wasn't happy.\",\n", + " \"Susan didn't lose to Michael in the game so [Michael] wasn't happy.\",\n", + " \"Mary didn't lose to Michael in the game so [Michael] wasn't happy.\",\n", + " \"Linda didn't lose to Michael in the game so [Michael] wasn't happy.\",\n", + " \"Susan didn't lose to John in the game so [Susan] wasn't sad.\",\n", + " \"Mary didn't lose to John in the game so [Mary] wasn't sad.\",\n", + " \"Linda didn't lose to John in the game so [Linda] wasn't sad.\",\n", + " \"Susan didn't lose to David in the game so [Susan] wasn't sad.\",\n", + " \"Mary didn't lose to David in the game so [Mary] wasn't sad.\",\n", + " \"Linda didn't lose to David in the game so [Linda] wasn't sad.\",\n", + " \"Susan didn't lose to Michael in the game so [Susan] wasn't sad.\",\n", + " \"Mary didn't lose to Michael in the game so [Mary] wasn't sad.\",\n", + " \"Linda didn't lose to Michael in the game so [Linda] wasn't sad.\",\n", + " \"Susan didn't lose to John in the game so [John] was sad.\",\n", + " \"Mary didn't lose to John in the game so [John] was sad.\",\n", + " \"Linda didn't lose to John in the game so [John] was sad.\",\n", + " \"Susan didn't lose to David in the game so [David] was sad.\",\n", + " \"Mary didn't lose to David in the game so [David] was sad.\",\n", + " \"Linda didn't lose to David in the game so [David] was sad.\",\n", + " \"Susan didn't lose to Michael in the game so [Michael] was sad.\",\n", + " \"Mary didn't lose to Michael in the game so [Michael] was sad.\",\n", + " \"Linda didn't lose to Michael in the game so [Michael] was sad.\",\n", + " \"Susan didn't lose to John in the game so [Susan] was happy.\",\n", + " \"Mary didn't lose to John in the game so [Mary] was happy.\",\n", + " \"Linda didn't lose to John in the game so [Linda] was happy.\",\n", + " \"Susan didn't lose to David in the game so [Susan] was happy.\",\n", + " \"Mary didn't lose to David in the game so [Mary] was happy.\",\n", + " \"Linda didn't lose to David in the game so [Linda] was happy.\",\n", + " \"Susan didn't lose to Michael in the game so [Susan] was happy.\",\n", + " \"Mary didn't lose to Michael in the game so [Mary] was happy.\",\n", + " \"Linda didn't lose to Michael in the game so [Linda] was happy.\",\n", + " \"John beat Susan in the game but [John] wasn't happy.\",\n", + " \"John beat Mary in the game but [John] wasn't happy.\",\n", + " \"John beat Linda in the game but [John] wasn't happy.\",\n", + " \"David beat Susan in the game but [David] wasn't happy.\",\n", + " \"David beat Mary in the game but [David] wasn't happy.\",\n", + " \"David beat Linda in the game but [David] wasn't happy.\",\n", + " \"Michael beat Susan in the game but [Michael] wasn't happy.\",\n", + " \"Michael beat Mary in the game but [Michael] wasn't happy.\",\n", + " \"Michael beat Linda in the game but [Michael] wasn't happy.\",\n", + " \"John beat Susan in the game but [Susan] wasn't sad.\",\n", + " \"John beat Mary in the game but [Mary] wasn't sad.\",\n", + " \"John beat Linda in the game but [Linda] wasn't sad.\",\n", + " \"David beat Susan in the game but [Susan] wasn't sad.\",\n", + " \"David beat Mary in the game but [Mary] wasn't sad.\",\n", + " \"David beat Linda in the game but [Linda] wasn't sad.\",\n", + " \"Michael beat Susan in the game but [Susan] wasn't sad.\",\n", + " \"Michael beat Mary in the game but [Mary] wasn't sad.\",\n", + " \"Michael beat Linda in the game but [Linda] wasn't sad.\",\n", + " 'John beat Susan in the game but [John] was sad.',\n", + " 'John beat Mary in the game but [John] was sad.',\n", + " 'John beat Linda in the game but [John] was sad.',\n", + " 'David beat Susan in the game but [David] was sad.',\n", + " 'David beat Mary in the game but [David] was sad.',\n", + " 'David beat Linda in the game but [David] was sad.',\n", + " 'Michael beat Susan in the game but [Michael] was sad.',\n", + " 'Michael beat Mary in the game but [Michael] was sad.',\n", + " 'Michael beat Linda in the game but [Michael] was sad.',\n", + " 'John beat Susan in the game but [Susan] was happy.',\n", + " 'John beat Mary in the game but [Mary] was happy.',\n", + " 'John beat Linda in the game but [Linda] was happy.',\n", + " 'David beat Susan in the game but [Susan] was happy.',\n", + " 'David beat Mary in the game but [Mary] was happy.',\n", + " 'David beat Linda in the game but [Linda] was happy.',\n", + " 'Michael beat Susan in the game but [Susan] was happy.',\n", + " 'Michael beat Mary in the game but [Mary] was happy.',\n", + " 'Michael beat Linda in the game but [Linda] was happy.',\n", + " \"Susan lost to John in the game but [John] wasn't happy.\",\n", + " \"Mary lost to John in the game but [John] wasn't happy.\",\n", + " \"Linda lost to John in the game but [John] wasn't happy.\",\n", + " \"Susan lost to David in the game but [David] wasn't happy.\",\n", + " \"Mary lost to David in the game but [David] wasn't happy.\",\n", + " \"Linda lost to David in the game but [David] wasn't happy.\",\n", + " \"Susan lost to Michael in the game but [Michael] wasn't happy.\",\n", + " \"Mary lost to Michael in the game but [Michael] wasn't happy.\",\n", + " \"Linda lost to Michael in the game but [Michael] wasn't happy.\",\n", + " \"Susan lost to John in the game but [Susan] wasn't sad.\",\n", + " \"Mary lost to John in the game but [Mary] wasn't sad.\",\n", + " \"Linda lost to John in the game but [Linda] wasn't sad.\",\n", + " \"Susan lost to David in the game but [Susan] wasn't sad.\",\n", + " \"Mary lost to David in the game but [Mary] wasn't sad.\",\n", + " \"Linda lost to David in the game but [Linda] wasn't sad.\",\n", + " \"Susan lost to Michael in the game but [Susan] wasn't sad.\",\n", + " \"Mary lost to Michael in the game but [Mary] wasn't sad.\",\n", + " \"Linda lost to Michael in the game but [Linda] wasn't sad.\",\n", + " 'Susan lost to John in the game but [John] was sad.',\n", + " 'Mary lost to John in the game but [John] was sad.',\n", + " 'Linda lost to John in the game but [John] was sad.',\n", + " 'Susan lost to David in the game but [David] was sad.',\n", + " 'Mary lost to David in the game but [David] was sad.',\n", + " 'Linda lost to David in the game but [David] was sad.',\n", + " 'Susan lost to Michael in the game but [Michael] was sad.',\n", + " 'Mary lost to Michael in the game but [Michael] was sad.',\n", + " 'Linda lost to Michael in the game but [Michael] was sad.',\n", + " 'Susan lost to John in the game but [Susan] was happy.',\n", + " 'Mary lost to John in the game but [Mary] was happy.',\n", + " 'Linda lost to John in the game but [Linda] was happy.',\n", + " 'Susan lost to David in the game but [Susan] was happy.',\n", + " 'Mary lost to David in the game but [Mary] was happy.',\n", + " 'Linda lost to David in the game but [Linda] was happy.',\n", + " 'Susan lost to Michael in the game but [Susan] was happy.',\n", + " 'Mary lost to Michael in the game but [Mary] was happy.',\n", + " 'Linda lost to Michael in the game but [Linda] was happy.',\n", + " \"John didn't beat Susan in the game but [John] was happy.\",\n", + " \"John didn't beat Mary in the game but [John] was happy.\",\n", + " \"John didn't beat Linda in the game but [John] was happy.\",\n", + " \"David didn't beat Susan in the game but [David] was happy.\",\n", + " \"David didn't beat Mary in the game but [David] was happy.\",\n", + " \"David didn't beat Linda in the game but [David] was happy.\",\n", + " \"Michael didn't beat Susan in the game but [Michael] was happy.\",\n", + " \"Michael didn't beat Mary in the game but [Michael] was happy.\",\n", + " \"Michael didn't beat Linda in the game but [Michael] was happy.\",\n", + " \"John didn't beat Susan in the game but [Susan] was sad.\",\n", + " \"John didn't beat Mary in the game but [Mary] was sad.\",\n", + " \"John didn't beat Linda in the game but [Linda] was sad.\",\n", + " \"David didn't beat Susan in the game but [Susan] was sad.\",\n", + " \"David didn't beat Mary in the game but [Mary] was sad.\",\n", + " \"David didn't beat Linda in the game but [Linda] was sad.\",\n", + " \"Michael didn't beat Susan in the game but [Susan] was sad.\",\n", + " \"Michael didn't beat Mary in the game but [Mary] was sad.\",\n", + " \"Michael didn't beat Linda in the game but [Linda] was sad.\",\n", + " \"John didn't beat Susan in the game but [John] wasn't sad.\",\n", + " \"John didn't beat Mary in the game but [John] wasn't sad.\",\n", + " \"John didn't beat Linda in the game but [John] wasn't sad.\",\n", + " \"David didn't beat Susan in the game but [David] wasn't sad.\",\n", + " \"David didn't beat Mary in the game but [David] wasn't sad.\",\n", + " \"David didn't beat Linda in the game but [David] wasn't sad.\",\n", + " \"Michael didn't beat Susan in the game but [Michael] wasn't sad.\",\n", + " \"Michael didn't beat Mary in the game but [Michael] wasn't sad.\",\n", + " \"Michael didn't beat Linda in the game but [Michael] wasn't sad.\",\n", + " \"John didn't beat Susan in the game but [Susan] wasn't happy.\",\n", + " \"John didn't beat Mary in the game but [Mary] wasn't happy.\",\n", + " \"John didn't beat Linda in the game but [Linda] wasn't happy.\",\n", + " \"David didn't beat Susan in the game but [Susan] wasn't happy.\",\n", + " \"David didn't beat Mary in the game but [Mary] wasn't happy.\",\n", + " \"David didn't beat Linda in the game but [Linda] wasn't happy.\",\n", + " \"Michael didn't beat Susan in the game but [Susan] wasn't happy.\",\n", + " \"Michael didn't beat Mary in the game but [Mary] wasn't happy.\",\n", + " \"Michael didn't beat Linda in the game but [Linda] wasn't happy.\",\n", + " \"Susan didn't lose to John in the game but [John] was happy.\",\n", + " \"Mary didn't lose to John in the game but [John] was happy.\",\n", + " \"Linda didn't lose to John in the game but [John] was happy.\",\n", + " \"Susan didn't lose to David in the game but [David] was happy.\",\n", + " \"Mary didn't lose to David in the game but [David] was happy.\",\n", + " \"Linda didn't lose to David in the game but [David] was happy.\",\n", + " \"Susan didn't lose to Michael in the game but [Michael] was happy.\",\n", + " \"Mary didn't lose to Michael in the game but [Michael] was happy.\",\n", + " \"Linda didn't lose to Michael in the game but [Michael] was happy.\",\n", + " \"Susan didn't lose to John in the game but [Susan] was sad.\",\n", + " \"Mary didn't lose to John in the game but [Mary] was sad.\",\n", + " \"Linda didn't lose to John in the game but [Linda] was sad.\",\n", + " \"Susan didn't lose to David in the game but [Susan] was sad.\",\n", + " \"Mary didn't lose to David in the game but [Mary] was sad.\",\n", + " \"Linda didn't lose to David in the game but [Linda] was sad.\",\n", + " \"Susan didn't lose to Michael in the game but [Susan] was sad.\",\n", + " \"Mary didn't lose to Michael in the game but [Mary] was sad.\",\n", + " \"Linda didn't lose to Michael in the game but [Linda] was sad.\",\n", + " \"Susan didn't lose to John in the game but [John] wasn't sad.\",\n", + " \"Mary didn't lose to John in the game but [John] wasn't sad.\",\n", + " \"Linda didn't lose to John in the game but [John] wasn't sad.\",\n", + " \"Susan didn't lose to David in the game but [David] wasn't sad.\",\n", + " \"Mary didn't lose to David in the game but [David] wasn't sad.\",\n", + " \"Linda didn't lose to David in the game but [David] wasn't sad.\",\n", + " \"Susan didn't lose to Michael in the game but [Michael] wasn't sad.\",\n", + " \"Mary didn't lose to Michael in the game but [Michael] wasn't sad.\",\n", + " \"Linda didn't lose to Michael in the game but [Michael] wasn't sad.\",\n", + " \"Susan didn't lose to John in the game but [Susan] wasn't happy.\",\n", + " \"Mary didn't lose to John in the game but [Mary] wasn't happy.\",\n", + " \"Linda didn't lose to John in the game but [Linda] wasn't happy.\",\n", + " \"Susan didn't lose to David in the game but [Susan] wasn't happy.\",\n", + " \"Mary didn't lose to David in the game but [Mary] wasn't happy.\",\n", + " \"Linda didn't lose to David in the game but [Linda] wasn't happy.\",\n", + " \"Susan didn't lose to Michael in the game but [Susan] wasn't happy.\",\n", + " \"Mary didn't lose to Michael in the game but [Mary] wasn't happy.\",\n", + " \"Linda didn't lose to Michael in the game but [Linda] wasn't happy.\"]" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "causal_sentences\n", + "turning_sentences\n", + "# substituted_sentences" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "examples = [(2,\n", + " \"The trophy doesn't fit into the brown suitcase because [it] is too large.\",\n", + " 'fit into:large/small'),\n", + " (4,\n", + " 'Joan made sure to thank Susan for all the help [she] had recieved.',\n", + " 'thank:receive/give'),\n", + " (10,\n", + " 'The delivery truck zoomed by the school bus because [it] was going so fast.',\n", + " 'zoom by:fast/slow'),\n", + " (12,\n", + " 'Frank felt vindicated when his longtime rival Bill revealed that [he] was the winner of the competition.',\n", + " 'vindicated/crushed:be the winner'),\n", + " (16,\n", + " 'The large ball crashed right through the table because [it] was made of steel.',\n", + " 'crash through:[hard]/[soft]'),\n", + " (18,\n", + " \"John couldn't see the stage with Billy in front of him because [he] is so short.\",\n", + " '[block]:short/tall'),\n", + " (20,\n", + " 'Tom threw his schoolbag down to Ray after [he] reached the top of the stairs.',\n", + " 'down to:top/bottom'),\n", + " (22,\n", + " 'Although they ran at about the same speed, Sue beat Sally because [she] had such a good start.',\n", + " 'beat:good/bad'),\n", + " (26,\n", + " \"Sam's drawing was hung just above Tina's and [it] did look much better with another one below it.\",\n", + " 'above/below'),\n", + " (28,\n", + " 'Anna did a lot better than her good friend Lucy on the test because [she] had studied so hard.',\n", + " 'better/worse:study hard'),\n", + " (30,\n", + " 'The firemen arrived after the police because [they] were coming from so far away.',\n", + " 'after/before:far away'),\n", + " (32,\n", + " \"Frank was upset with Tom because the toaster [he] had bought from him didn't work.\",\n", + " 'be upset with:buy from not work/sell not work'),\n", + " (36,\n", + " 'The sack of potatoes had been placed above the bag of flour, so [it] had to be moved first.',\n", + " 'above/below:moved first'),\n", + " (38,\n", + " 'Pete envies Martin although [he] is very successful.',\n", + " 'although/because'),\n", + " (42,\n", + " 'I poured water from the bottle into the cup until [it] was empty.',\n", + " 'pour:empty/full'),\n", + " (46,\n", + " \"Sid explained his theory to Mark but [he] couldn't convince him.\",\n", + " 'explain:convince/understand'),\n", + " (48,\n", + " \"Susan knew that Ann's son had been in a car accident, so [she] told her about it.\",\n", + " '?know tell:so/because'),\n", + " (50,\n", + " \"Joe's uncle can still beat him at tennis, even though [he] is 30 years younger.\",\n", + " 'beat:younger/older'),\n", + " (64,\n", + " 'In the middle of the outdoor concert, the rain started falling, but [it] continued until 10.',\n", + " 'but/and'),\n", + " (68,\n", + " 'Ann asked Mary what time the library closes, because [she] had forgotten.',\n", + " 'because/but'),\n", + " (84,\n", + " 'If the con artist has succeeded in fooling Sam, [he] would have gotten a lot of money.',\n", + " 'fool:get/lose'),\n", + " (92,\n", + " 'Alice tried frantically to stop her daughter from chatting at the party, leaving us to wonder why [she] was behaving so strangely.',\n", + " '?stop normal/stop abnormal:strange'),\n", + " (98,\n", + " \"I was trying to open the lock with the key, but someone had filled the keyhole with chewing gum, and I couldn't get [it] in.\",\n", + " 'put ... into filled with ... :get in/get out'),\n", + " (100,\n", + " 'The dog chased the cat, which ran up a tree. [It] waited at the bottom.',\n", + " 'up:at the bottom/at the top'),\n", + " (106,\n", + " 'John was doing research in the library when he heard a man humming and whistling. [He] was very annoyed.',\n", + " 'hear ... humming and whistling:annoyed/annoying'),\n", + " (108,\n", + " 'John was jogging through the park when he saw a man juggling watermelons. [He] was very impressed.',\n", + " 'see ... juggling watermelons:impressed/impressive'),\n", + " (132,\n", + " 'Jane knocked on the door, and Susan answered it. [She] invited her to come out.',\n", + " 'visit:invite come out/invite come in'),\n", + " (150,\n", + " 'Jackson was greatly influenced by Arnold, though [he] lived two centuries later.',\n", + " 'influence:later/earlier'),\n", + " (160,\n", + " 'The actress used to be named Terpsichore, but she changed it to Tina a few years ago, because she figured [it] was too hard to pronounce.',\n", + " 'change:hard/easy'),\n", + " (166,\n", + " 'Fred is the only man still alive who remembers my great-grandfather. [He] is a remarkable man.',\n", + " 'alive:is/was'),\n", + " (170,\n", + " \"In July, Kamtchatka declared war on Yakutsk. Since Yakutsk's army was much better equipped and ten times larger, [they] were defeated within weeks.\",\n", + " 'better equipped and large:defeated/victorious'),\n", + " (186,\n", + " 'When the sponsors of the bill got to the town hall, they were surprised to find that the room was full of opponents. [They] were very much in the minority.',\n", + " 'be full of:minority/majority'),\n", + " (188,\n", + " 'Everyone really loved the oatmeal cookies; only a few people liked the chocolate chip cookies. Next time, we should make more of [them] .',\n", + " 'like over:more/fewer'),\n", + " (190,\n", + " 'We had hoped to place copies of our newsletter on all the chairs in the auditorium, but there were simply not enough of [them] .',\n", + " 'place on all:not enough/too many'),\n", + " (196,\n", + " \"Steve follows Fred's example in everything. [He] admires him hugely.\",\n", + " 'follow:admire/influence'),\n", + " (198,\n", + " \"The table won't fit through the doorway because [it] is too wide.\",\n", + " 'fit through:wide/narrow'),\n", + " (200,\n", + " 'Grace was happy to trade me her sweater for my jacket. She thinks [it] looks dowdy on her.',\n", + " 'trade:dowdy/great'),\n", + " (202,\n", + " 'John hired Bill to take care of [him] .',\n", + " 'hire/hire oneself to:take care of'),\n", + " (204,\n", + " 'John promised Bill to leave, so an hour later [he] left.',\n", + " 'promise/order'),\n", + " (210,\n", + " \"Jane knocked on Susan's door but [she] did not get an answer.\",\n", + " 'knock:get an answer/answer'),\n", + " (212,\n", + " 'Joe paid the detective after [he] received the final report on the case.',\n", + " 'pay:receive/deliver'),\n", + " (226,\n", + " 'Bill passed the half-empty plate to John because [he] was full.',\n", + " 'pass the plate:full/hungry'),\n", + " (252,\n", + " 'George got free tickets to the play, but he gave them to Eric, even though [he] was particularly eager to see it.',\n", + " 'even though/because/not'),\n", + " (255,\n", + " \"Jane gave Joan candy because [she] wasn't hungry.\",\n", + " 'give:not hungry/hungry'),\n", + " (259,\n", + " 'James asked Robert for a favor but [he] was refused.',\n", + " 'ask for a favor:refuse/be refused`'),\n", + " (261,\n", + " 'Kirilov ceded the presidency to Shatov because [he] was less popular.',\n", + " 'cede:less popular/more popular'),\n", + " (263,\n", + " 'Emma did not pass the ball to Janie although [she] saw that she was open.',\n", + " 'not pass although:see open/open')]" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "47" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(examples)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Untitled_likunlin-Copy1.ipynb b/Untitled_likunlin-Copy1.ipynb new file mode 100644 index 000000000000..083d764b886b --- /dev/null +++ b/Untitled_likunlin-Copy1.ipynb @@ -0,0 +1,816 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from IPython.core.interactiveshell import InteractiveShell\n", + "InteractiveShell.ast_node_interactivity = 'all'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "\n", + "import numpy as np\n", + "import math\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "from pylab import rcParams\n", + "\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from pytorch_pretrained_bert import tokenization, BertTokenizer, BertModel, BertForMaskedLM, BertForPreTraining, BertConfig\n", + "from examples.extract_features import *" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "01/03/2019 16:37:32 - INFO - pytorch_pretrained_bert.tokenization - loading vocabulary file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt from cache at /home/xd/.pytorch_pretrained_bert/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084\n", + "01/03/2019 16:37:32 - INFO - pytorch_pretrained_bert.modeling - loading archive file /nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/\n", + "01/03/2019 16:37:32 - INFO - pytorch_pretrained_bert.modeling - Model config {\n", + " \"attention_probs_dropout_prob\": 0.1,\n", + " \"hidden_act\": \"gelu\",\n", + " \"hidden_dropout_prob\": 0.1,\n", + " \"hidden_size\": 768,\n", + " \"initializer_range\": 0.02,\n", + " \"intermediate_size\": 3072,\n", + " \"max_position_embeddings\": 512,\n", + " \"num_attention_heads\": 12,\n", + " \"num_hidden_layers\": 12,\n", + " \"type_vocab_size\": 2,\n", + " \"vocab_size\": 30522\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "class Args:\n", + " def __init__(self):\n", + " pass\n", + " \n", + "args = Args()\n", + "args.no_cuda = False\n", + "\n", + "CONFIG_NAME = 'bert_config.json'\n", + "BERT_DIR = '/nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/'\n", + "config_file = os.path.join(BERT_DIR, CONFIG_NAME)\n", + "config = BertConfig.from_json_file(config_file)\n", + "\n", + "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n", + "model = BertForPreTraining.from_pretrained(BERT_DIR)\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() and not args.no_cuda else \"cpu\")\n", + "_ = model.to(device)\n", + "_ = model.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "def convert_text_to_examples(text):\n", + " examples = []\n", + " unique_id = 0\n", + " if True:\n", + " for line in text:\n", + " line = line.strip()\n", + " text_a = None\n", + " text_b = None\n", + " m = re.match(r\"^(.*) \\|\\|\\| (.*)$\", line)\n", + " if m is None:\n", + " text_a = line\n", + " else:\n", + " text_a = m.group(1)\n", + " text_b = m.group(2)\n", + " examples.append(\n", + " InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b))\n", + " unique_id += 1\n", + " return examples\n", + "\n", + "def convert_examples_to_features(examples, tokenizer, append_special_tokens=True, replace_mask=True, print_info=False):\n", + " features = []\n", + " for (ex_index, example) in enumerate(examples):\n", + " tokens_a = tokenizer.tokenize(example.text_a)\n", + " tokens_b = None\n", + " if example.text_b:\n", + " tokens_b = tokenizer.tokenize(example.text_b)\n", + "\n", + " tokens = []\n", + " input_type_ids = []\n", + " if append_special_tokens:\n", + " tokens.append(\"[CLS]\")\n", + " input_type_ids.append(0)\n", + " for token in tokens_a:\n", + " if replace_mask and token == '_': # XD\n", + " token = \"[MASK]\"\n", + " tokens.append(token)\n", + " input_type_ids.append(0)\n", + " if append_special_tokens:\n", + " tokens.append(\"[SEP]\")\n", + " input_type_ids.append(0)\n", + "\n", + " if tokens_b:\n", + " for token in tokens_b:\n", + " if replace_mask and token == '_': # XD\n", + " token = \"[MASK]\"\n", + " tokens.append(token)\n", + " input_type_ids.append(1)\n", + " if append_special_tokens:\n", + " tokens.append(\"[SEP]\")\n", + " input_type_ids.append(1)\n", + "\n", + " input_ids = tokenizer.convert_tokens_to_ids(tokens)\n", + " input_mask = [1] * len(input_ids)\n", + "\n", + " if ex_index < 5:\n", + "# logger.info(\"*** Example ***\")\n", + "# logger.info(\"unique_id: %s\" % (example.unique_id))\n", + " logger.info(\"tokens: %s\" % \" \".join([str(x) for x in tokens]))\n", + "# logger.info(\"input_ids: %s\" % \" \".join([str(x) for x in input_ids]))\n", + "# logger.info(\"input_mask: %s\" % \" \".join([str(x) for x in input_mask]))\n", + "# logger.info(\n", + "# \"input_type_ids: %s\" % \" \".join([str(x) for x in input_type_ids]))\n", + " \n", + " features.append(\n", + " InputFeatures(\n", + " unique_id=example.unique_id,\n", + " tokens=tokens,\n", + " input_ids=input_ids,\n", + " input_mask=input_mask,\n", + " input_type_ids=input_type_ids))\n", + " return features\n", + "\n", + "def copy_and_mask_feature(feature, masked_tokens=None):\n", + " import copy\n", + " tokens = feature.tokens\n", + " masked_positions = [tokens.index(t) for t in masked_tokens if t in tokens] \\\n", + " if masked_tokens is not None else range(len(tokens))\n", + " assert len(masked_positions) > 0\n", + " masked_feature_copies = []\n", + " for masked_pos in masked_positions:\n", + " feature_copy = copy.deepcopy(feature)\n", + " feature_copy.input_ids[masked_pos] = tokenizer.vocab[\"[MASK]\"]\n", + " masked_feature_copies.append(feature_copy)\n", + " return masked_feature_copies, masked_positions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def show_lm_probs(tokens, input_ids, probs, topk=5, firstk=20):\n", + " def print_pair(token, prob, end_str='', hit_mark=' '):\n", + " if i < firstk:\n", + " # token = token.replace('', '').replace('\\n', '/n')\n", + " print('{}{: >3} | {: <12}'.format(hit_mark, int(round(prob*100)), token), end=end_str)\n", + " \n", + " ret = None\n", + " for i in range(len(tokens)):\n", + " ind_ = input_ids[i].item() if input_ids is not None else tokenizer.vocab[tokens[i]]\n", + " prob_ = probs[i][ind_].item()\n", + " print_pair(tokens[i], prob_, end_str='\\t')\n", + " values, indices = probs[i].topk(topk)\n", + " top_pairs = []\n", + " for j in range(topk):\n", + " ind, prob = indices[j].item(), values[j].item()\n", + " hit_mark = '*' if ind == ind_ else ' '\n", + " token = tokenizer.ids_to_tokens[ind]\n", + " print_pair(token, prob, hit_mark=hit_mark, end_str='' if j < topk - 1 else '\\n')\n", + " top_pairs.append((token, prob))\n", + " if tokens[i] == \"[MASK]\":\n", + " ret = top_pairs\n", + " return ret" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import colored\n", + "from colored import stylize\n", + "\n", + "def show_abnormals(tokens, probs, show_suggestions=False):\n", + " def gap2color(gap):\n", + " if gap <= 5:\n", + " return 'yellow_1'\n", + " elif gap <= 10:\n", + " return 'orange_1'\n", + " else:\n", + " return 'red_1'\n", + " \n", + " def print_token(token, suggestion, gap):\n", + " if gap == 0:\n", + " print(stylize(token + ' ', colored.fg('white') + colored.bg('black')), end='')\n", + " else:\n", + " print(stylize(token, colored.fg(gap2color(gap)) + colored.bg('black')), end='')\n", + " if show_suggestions and gap > 5:\n", + " print(stylize('/' + suggestion + ' ', colored.fg('green' if gap > 10 else 'cyan') + colored.bg('black')), end='')\n", + " else:\n", + " print(stylize(' ', colored.fg(gap2color(gap)) + colored.bg('black')), end='')\n", + " # print('/' + suggestion, end=' ')\n", + " # print('%.2f' % gap, end=' ')\n", + " \n", + " avg_gap = 0.\n", + " for i in range(1, len(tokens) - 1): # skip first [CLS] and last [SEP]\n", + " ind_ = tokenizer.vocab[tokens[i]]\n", + " prob_ = probs[i][ind_].item()\n", + " top_prob = probs[i].max().item()\n", + " top_ind = probs[i].argmax().item()\n", + " gap = math.log(top_prob) - math.log(prob_)\n", + " suggestion = tokenizer.ids_to_tokens[top_ind]\n", + " print_token(tokens[i], suggestion, gap)\n", + " avg_gap += gap\n", + " avg_gap /= (len(tokens) - 2)\n", + " print()\n", + " print(avg_gap)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "analyzed_cache = {}\n", + "\n", + "def analyze_text(text, masked_tokens=None, show_suggestions=False, show_firstk_probs=20):\n", + " if text[0] in analyzed_cache:\n", + " features, mlm_probs = analyzed_cache[text[0]]\n", + " given_mask = \"[MASK]\" in features[0].tokens\n", + " tokens = features[0].tokens\n", + " else:\n", + " examples = convert_text_to_examples(text)\n", + " features = convert_examples_to_features(examples, tokenizer, print_info=False)\n", + " given_mask = \"[MASK]\" in features[0].tokens\n", + " if not given_mask or masked_tokens is not None:\n", + " assert len(features) == 1\n", + " features, masked_positions = copy_and_mask_feature(features[0], masked_tokens=masked_tokens)\n", + "\n", + " input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)\n", + " input_type_ids = torch.tensor([f.input_type_ids for f in features], dtype=torch.long)\n", + " input_ids = input_ids.to(device)\n", + " input_type_ids = input_type_ids.to(device)\n", + "\n", + " mlm_logits, _ = model(input_ids, input_type_ids)\n", + " mlm_probs = F.softmax(mlm_logits, dim=-1)\n", + "\n", + " tokens = features[0].tokens\n", + " if not given_mask or masked_tokens is not None:\n", + " bsz, seq_len, vocab_size = mlm_probs.size()\n", + " assert bsz == len(masked_positions)\n", + " # reduced_mlm_probs = torch.Tensor(1, seq_len, vocab_size)\n", + " # for i in range(seq_len):\n", + " # reduced_mlm_probs[0, i] = mlm_probs[i, i]\n", + " reduced_mlm_probs = torch.Tensor(1, len(masked_positions), vocab_size)\n", + " for i, pos in enumerate(masked_positions):\n", + " reduced_mlm_probs[0, i] = mlm_probs[i, pos]\n", + " mlm_probs = reduced_mlm_probs\n", + " tokens = [tokens[i] for i in masked_positions]\n", + " \n", + " analyzed_cache[text[0]] = (features, mlm_probs)\n", + " \n", + " top_pairs = show_lm_probs(tokens, None, mlm_probs[0], firstk=show_firstk_probs)\n", + " if not given_mask:\n", + " show_abnormals(tokens, mlm_probs[0], show_suggestions=show_suggestions)\n", + " return top_pairs" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "01/03/2019 17:13:21 - INFO - examples.extract_features - tokens: [CLS] what ingredients account for the marvelous function of a dream ? [SEP]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 | [CLS] \t 3 | . 1 | the 1 | , 1 | ) 1 | \" \n", + " 35 | what \t* 35 | what 25 | do 9 | can 7 | could 5 | would \n", + " 0 | ingredients \t 51 | could 23 | would 13 | can 8 | might 2 | may \n", + " 0 | account \t 32 | were 26 | are 7 | remained 6 | existed 6 | exist \n", + " 100 | for \t*100 | for 0 | to 0 | of 0 | up 0 | all \n", + " 98 | the \t* 98 | the 2 | this 0 | a 0 | that 0 | such \n", + " 0 | marvelous \t 5 | biological 5 | normal 4 | cognitive 2 | specific 2 | physiological\n", + " 0 | function \t 21 | ##ness 8 | beauty 5 | quality 5 | nature 4 | power \n", + " 91 | of \t* 91 | of 8 | in 0 | within 0 | as 0 | during \n", + " 14 | a \t 55 | the 16 | this * 14 | a 4 | my 3 | his \n", + " 0 | dream \t 3 | heart 3 | plant 3 | soul 2 | brain 2 | body \n", + " 98 | ? \t* 98 | ? 2 | . 0 | ; 0 | ! 0 | | \n", + " 0 | [SEP] \t 13 | what 12 | \" 7 | they 4 | and 4 | ' \n", + "\u001b[38;5;15m\u001b[48;5;0mwhat \u001b[0m\u001b[38;5;196m\u001b[48;5;0mingredients\u001b[0m\u001b[38;5;196m\u001b[48;5;0m \u001b[0m\u001b[38;5;226m\u001b[48;5;0maccount\u001b[0m\u001b[38;5;226m\u001b[48;5;0m \u001b[0m\u001b[38;5;15m\u001b[48;5;0mfor \u001b[0m\u001b[38;5;15m\u001b[48;5;0mthe \u001b[0m\u001b[38;5;214m\u001b[48;5;0mmarvelous\u001b[0m\u001b[38;5;214m\u001b[48;5;0m \u001b[0m\u001b[38;5;214m\u001b[48;5;0mfunction\u001b[0m\u001b[38;5;214m\u001b[48;5;0m \u001b[0m\u001b[38;5;15m\u001b[48;5;0mof \u001b[0m\u001b[38;5;226m\u001b[48;5;0ma\u001b[0m\u001b[38;5;226m\u001b[48;5;0m \u001b[0m\u001b[38;5;226m\u001b[48;5;0mdream\u001b[0m\u001b[38;5;226m\u001b[48;5;0m \u001b[0m\u001b[38;5;15m\u001b[48;5;0m? \u001b[0m\n", + "3.421217077676471\n" + ] + } + ], + "source": [ + "# text = [\"Who was Jim Henson? Jim Henson _ a puppeteer.\"]\n", + "text = [\"What ingredients account for the marvelous function of a dream?\"]\n", + "# text = [\"Last week I went to the theatre. I had a very good seat. The play was very interesting. But I didn't enjoy it. A young man and a young woman were sitting behind me. They were talking loudly. I got very angry. I couldn't hear a word. I turned round. I looked at the man angrily. They didn't pay any attention.In the end, I couldn't bear it. I turned round again. 'I can't hear a word!' I said angrily. 'It's none of your business,' the young man said rudely. 'This is a private conversation!'\"]\n", + "# text = [\"After the outbreak of the disease, the Ministry of Agriculture and rural areas immediately sent a supervision team to the local. Local Emergency Response Mechanism has been activated in accordance with the requirements, to take blockade, culling, harmless treatment, disinfection and other treatment measures to all disease and culling of pigs for harmless treatment. At the same time, all live pigs and their products are prohibited from transferring out of the blockade area, and live pigs are not allowed to be transported into the blockade area. At present, all the above measures have been implemented.\"]\n", + "# text = [\"Early critics of Emily Dickinson's poetry mistook for simplemindedness the surface of artlessness that in fact she constructed with such innocence.\"]\n", + "analyze_text(text, show_firstk_probs=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "01/03/2019 17:10:45 - INFO - examples.extract_features - tokens: [CLS] the trophy doesn ' t fit into the brown suitcase because the [MASK] is too large . [SEP]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 | [CLS] \t 2 | . 1 | ) 1 | the 1 | , 1 | \" \n", + " 100 | the \t*100 | the 0 | his 0 | a 0 | its 0 | her \n", + " 97 | trophy \t* 97 | trophy 0 | cup 0 | prize 0 | trophies 0 | competition \n", + " 100 | doesn \t*100 | doesn 0 | can 0 | does 0 | won 0 | didn \n", + " 100 | ' \t*100 | ' 0 | t 0 | \" 0 | = 0 | ` \n", + " 100 | t \t*100 | t 0 | not 0 | s 0 | n 0 | to \n", + " 100 | fit \t*100 | fit 0 | fits 0 | sit 0 | get 0 | fitting \n", + " 100 | into \t*100 | into 0 | in 0 | inside 0 | onto 0 | within \n", + " 100 | the \t*100 | the 0 | her 0 | his 0 | a 0 | my \n", + " 100 | brown \t*100 | brown 0 | black 0 | green 0 | blue 0 | plastic \n", + " 95 | suitcase \t* 95 | suitcase 3 | bag 1 | luggage 0 | backpack 0 | trunk \n", + " 100 | because \t*100 | because 0 | as 0 | since 0 | due 0 | . \n", + " 100 | the \t*100 | the 0 | its 0 | his 0 | it 0 | her \n", + " 0 | [MASK] \t 21 | suitcase 19 | bag 6 | box 2 | luggage 2 | case \n", + " 99 | is \t* 99 | is 1 | was 0 | being 0 | has 0 | it \n", + " 100 | too \t*100 | too 0 | very 0 | extra 0 | overly 0 | more \n", + " 87 | large \t* 87 | large 11 | big 1 | small 1 | huge 0 | larger \n", + " 100 | . \t*100 | . 0 | ; 0 | , 0 | ! 0 | ' \n", + " 0 | [SEP] \t 35 | . 8 | ) 5 | , 4 | ( 3 | it \n" + ] + } + ], + "source": [ + "text = [\"The trophy doesn't fit into the brown suitcase because the _ is too large.\"]\n", + "# text = [\"Mary beat John in the match because _ was very strong.\"]\n", + "features = convert_examples_to_features(convert_text_to_examples(text), tokenizer, print_info=False)\n", + "input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long).to(device)\n", + "input_type_ids = torch.tensor([f.input_type_ids for f in features], dtype=torch.long).to(device)\n", + "mlm_logits, _ = model(input_ids, input_type_ids)\n", + "mlm_probs = F.softmax(mlm_logits, dim=-1)\n", + "tokens = features[0].tokens\n", + "top_pairs = show_lm_probs(tokens, None, mlm_probs[0], firstk=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have the same hair color.',\n", + " 'Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have different hair colors.']" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "text = [\n", + " # same / different\n", + " \"Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have the same hair color.\",\n", + " \"Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have different hair colors.\",\n", + " \"Tom has yellow hair. Mary has black hair. John has black hair. Mary and _ have the same hair color.\",\n", + " # because / although\n", + " \"John is taller/shorter than Mary because/although _ is older/younger.\",\n", + " \"The red ball is heavier/lighter than the blue ball because/although the _ ball is bigger/smaller.\",\n", + " \"Charles did a lot better/worse than his good friend Nancy on the test because/although _ had/hadn't studied so hard.\",\n", + " \"The trophy doesn't fit into the brown suitcase because/although the _ is too small/large.\",\n", + " \"John thought that he would arrive earlier than Susan, but/and indeed _ was the first to arrive.\",\n", + " # reverse\n", + " \"John came then Mary came. They left in reverse order. _ left then _ left.\",\n", + " \"John came after Mary. They left in reverse order. _ left after _ .\",\n", + " \"John came first, then came Mary. They left in reverse order: _ left first, then left _ .\",\n", + " # compare\n", + " \"Though John is tall, Tom is taller than John. So John is _ than Tom.\",\n", + " \"Tom is taller than John. So _ is shorter than _.\",\n", + " # WSC-style: before /after\n", + " \"Mary came before/after John. _ was late/early .\",\n", + " # yes / no\n", + " \"Was Tom taller than Susan? Yes, _ was taller.\",\n", + " # right / wrong, epistemic modality\n", + " \"John said the rain was about to stop. Mary said the rain would continue. Later the rain stopped. _ was wrong.\",\n", + " \n", + " \"The trophy doesn't fit into the brown suitcase because/although the _ is too small/large.\",\n", + " \"John thanked Mary because _ had given help to _ . \",\n", + " \"John felt vindicated/crushed when his longtime rival Mary revealed that _ was the winner of the competition.\",\n", + " \"John couldn't see the stage with Mary in front of him because _ is so short/tall.\",\n", + " \"Although they ran at about the same speed, John beat Sally because _ had such a bad start.\",\n", + " \"The fish ate the worm. The _ was hungry/tasty.\",\n", + " \n", + " \"John beat Mary. _ won the game/e winner.\",\n", + "]\n", + "text" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "with open('WSC_switched_label.json') as f:\n", + " examples = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "with open('WSC_child_problem.json') as f:\n", + " cexamples = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "for ce in cexamples:\n", + " for s in ce['sentences']:\n", + " for a in s['answer0'] + s['answer1']:\n", + " a = a.lower()\n", + " if a not in tokenizer.vocab:\n", + " ce\n", + " print(a, 'not in vocab!!!')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "for ce in cexamples:\n", + " if len(ce['sentences']) > 0:\n", + " e = examples[ce['index']]\n", + " assert ce['index'] == e['index']\n", + " e['score'] = all([s['score'] for s in ce['sentences']])\n", + " assert len(set([s['adjacent_ref'] for s in ce['sentences']])) == 1, 'adjcent_refs are different!'\n", + " e['adjacent_ref'] = ce['sentences'][0]['adjacent_ref']" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "\n", + "groups = defaultdict(list)\n", + "for e in examples:\n", + " if 'score' in e:\n", + " index = e['index']\n", + " if index < 252:\n", + " if index % 2 == 1:\n", + " index -= 1\n", + " elif index in [252, 253, 254]:\n", + " index = 252\n", + " else:\n", + " if index % 2 == 0:\n", + " index -= 1\n", + " groups[index].append(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(2, 'fit into:large/small', False),\n", + " (4, 'thank:receive/give', False),\n", + " (6, 'call:successful available', True),\n", + " (8, 'ask:repeat answer', False),\n", + " (10, 'zoom by:fast/slow', False),\n", + " (12, 'vindicated/crushed:be the winner', False),\n", + " (14, 'lift:weak heavy', False),\n", + " (16, 'crash through:[hard]/[soft]', False),\n", + " (18, '[block]:short/tall', False),\n", + " (20, 'down to:top/bottom', False),\n", + " (22, 'beat:good/bad', False),\n", + " (24, 'roll off:anchored level', False),\n", + " (26, 'above/below', False),\n", + " (28, 'better/worse:study hard', False),\n", + " (30, 'after/before:far away', False),\n", + " (32, 'be upset with:buy from not work/sell not work', True),\n", + " (34, '?yell at comfort:upset', False),\n", + " (36, 'above/below:moved first', False),\n", + " (38, 'although/because', False),\n", + " (40, 'bully:punish rescue', False),\n", + " (42, 'pour:empty/full', False),\n", + " (44, 'know:nosy indiscreet', False),\n", + " (46, 'explain:convince/understand', True),\n", + " (48, '?know tell:so/because', True),\n", + " (50, 'beat:younger/older', False),\n", + " (56, 'clog:cleaned removed', True),\n", + " (58, '?immediately follow:short delayed', False),\n", + " (60, '?between:see see around', True),\n", + " (64, 'but/and', False),\n", + " (66, 'clean:put in the trash put in the drawer', False),\n", + " (68, 'because/but', False),\n", + " (70, 'out of:handy lighter', False),\n", + " (72, 'put:tall high', False),\n", + " (74, 'show:good famous', True),\n", + " (76, 'pay for:generous grateful', False),\n", + " (78, 'but', False),\n", + " (80, 'if', False),\n", + " (82, 'if', False),\n", + " (84, 'fool:get/lose', False),\n", + " (88, 'wait:impatient cautious', False),\n", + " (90, 'give birth:woman baby', True),\n", + " (92, '?stop normal/stop abnormal:strange', False),\n", + " (96, 'eat:hungry tasty', False),\n", + " (98, 'put ... into filled with ... :get in/get out', False),\n", + " (100, 'up:at the bottom/at the top', False),\n", + " (102, 'crash through:removed repaired', False),\n", + " (104, 'stab:taken to the police station taken to the hospital', False),\n", + " (106, 'hear ... humming and whistling:annoyed/annoying', True),\n", + " (108, 'see ... juggling watermelons:impressed/impressive', True),\n", + " (114, 'tell lies: truthful skeptical', True),\n", + " (130, 'but:disappointed', True),\n", + " (132, 'visit:invite come out/invite come in', True),\n", + " (134, 'take classes from:eager known to speak it fluently', False),\n", + " (138, 'cover:out gone', True),\n", + " (144, 'tuck:work sleep', True),\n", + " (150, 'influence:later/earlier', False),\n", + " (152, 'can not cut:thick small', False),\n", + " (154, 'attack:kill guard', False),\n", + " (156, 'attack:bold nervous', False),\n", + " (160, 'change:hard:easy', False),\n", + " (166, 'alive:is/was', False),\n", + " (168, 'infant:twelve years old twelve months old', False),\n", + " (170, 'better equipped and large:defeated/victorious', False),\n", + " (178, 'interview:persistent cooperative', False),\n", + " (186, 'be full of:minority/majority', False),\n", + " (188, 'like over:more/fewer', False),\n", + " (190, 'place on all:not enough/too many', True),\n", + " (192, 'stick:leave have', True),\n", + " (196, 'follow:admire/influence', True),\n", + " (198, 'fit through:wide/narrow', False),\n", + " (200, 'trade:dowdy/great', False),\n", + " (202, 'hire/hire oneself to:take care of', True),\n", + " (204, 'promise/order', False),\n", + " (208, 'mother:education place', True),\n", + " (210, 'knock:get an answer/answer', True),\n", + " (212, 'pay:receive/deliver', False),\n", + " (218, '?', False),\n", + " (220, 'say check:move take', False),\n", + " (222, '?', False),\n", + " (224, 'give a life:drive alone walk', False),\n", + " (226, 'pass the plate:full/hungry', False),\n", + " (228, 'pass:turn over turn next', False),\n", + " (232, 'stretch pat', True),\n", + " (234, 'accept share', False),\n", + " (236, 'speak:break silence break concentration', False),\n", + " (240, 'carry:leg ache leg dangle', True),\n", + " (242, 'carry:in arms in bassinet', False),\n", + " (244, 'hold:against chest against will', True),\n", + " (250, 'stop', False),\n", + " (252, 'even though/because/not', False),\n", + " (255, 'give:not hungry/hungry', False),\n", + " (259, 'ask for a favor:refuse/be refused`', False),\n", + " (261, 'cede:less popular/more popular', False),\n", + " (263, 'not pass although:see open/open', True),\n", + " (271, 'suspect regret', True)]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def filter_dict(d, keys=['index', 'sentence', 'correct_answer', 'relational_word', 'is_associative', 'score']):\n", + " return {k: d[k] for k in d if k in keys}\n", + "\n", + "# ([[filter_dict(e) for e in eg] for eg in groups.values() if eg[0]['relational_word'] != 'none' and all([e['score'] for e in eg])])# / len([eg for eg in groups.values() if eg[0]['relational_word'] != 'none'])\n", + "[(index, eg[0]['relational_word'], all([e['score'] for e in eg])) for index, eg in groups.items() if eg[0]['relational_word'] != 'none']\n", + "# len([filter_dict(e) for e in examples if 'score' in e and not e['score'] and e['adjacent_ref']])\n", + "# for e in examples:\n", + "# if e['index'] % 2 == 0:\n", + "# print(e['sentence'])" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "179" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(['because' in e['sentence'] for e in examples]) + \\\n", + "sum(['so ' in e['sentence'] for e in examples]) + \\\n", + "sum(['but ' in e['sentence'] for e in examples]) + \\\n", + "sum(['though' in e['sentence'] for e in examples])" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "# with open('WSC_switched_label.json', 'w') as f:\n", + "# json.dump(examples, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "vis_attn_topk = 3\n", + "\n", + "def has_chinese_label(labels):\n", + " labels = [label.split('->')[0].strip() for label in labels]\n", + " r = sum([len(label) > 1 for label in labels if label not in ['BOS', 'EOS']]) * 1. / (len(labels) - 1)\n", + " return 0 < r < 0.5 # r == 0 means empty query labels used in self attention\n", + "\n", + "def _plot_attn(ax1, attn_name, attn, key_labels, query_labels, col, color='b'):\n", + " assert len(query_labels) == attn.size(0)\n", + " assert len(key_labels) == attn.size(1)\n", + "\n", + " ax1.set_xlim([-1, 1])\n", + " ax1.set_xticks([])\n", + " ax2 = ax1.twinx()\n", + " nlabels = max(len(key_labels), len(query_labels))\n", + " pos = range(nlabels)\n", + " \n", + " if 'self' in attn_name and col < ncols - 1:\n", + " query_labels = ['' for _ in query_labels]\n", + "\n", + " for ax, labels in [(ax1, key_labels), (ax2, query_labels)]:\n", + " ax.set_yticks(pos)\n", + " if has_chinese_label(labels):\n", + " ax.set_yticklabels(labels, fontproperties=zhfont)\n", + " else:\n", + " ax.set_yticklabels(labels)\n", + " ax.set_ylim([nlabels - 1, 0])\n", + " ax.tick_params(width=0, labelsize='xx-large')\n", + "\n", + " for spine in ax.spines.values():\n", + " spine.set_visible(False)\n", + "\n", + "# mask, attn = filter_attn(attn)\n", + " for qi in range(attn.size(0)):\n", + "# if not mask[qi]:\n", + "# continue\n", + "# for ki in range(attn.size(1)):\n", + " for ki in attn[qi].topk(vis_attn_topk)[1]:\n", + " a = attn[qi, ki]\n", + " ax1.plot((-1, 1), (ki, qi), color, alpha=a)\n", + "# print(attn.mean(dim=0).topk(5)[0])\n", + "# ax1.barh(pos, attn.mean(dim=0).data.cpu().numpy())\n", + "\n", + "def plot_layer_attn(result_tuple, attn_name='dec_self_attns', layer=0, heads=None):\n", + " hypo, nheads, labels_dict = result_tuple\n", + " key_labels, query_labels = labels_dict[attn_name]\n", + " if heads is None:\n", + " heads = range(nheads)\n", + " else:\n", + " nheads = len(heads)\n", + " \n", + " stride = 2 if attn_name == 'dec_enc_attns' else 1\n", + " nlabels = max(len(key_labels), len(query_labels))\n", + " rcParams['figure.figsize'] = 20, int(round(nlabels * stride * nheads / 8 * 1.0))\n", + " \n", + " rows = nheads // ncols * stride\n", + " fig, axes = plt.subplots(rows, ncols)\n", + " \n", + " # for head in range(nheads):\n", + " for head_i, head in enumerate(heads):\n", + " row, col = head_i * stride // ncols, head_i * stride % ncols\n", + " ax1 = axes[row, col]\n", + " attn = hypo[attn_name][layer][head]\n", + " _plot_attn(ax1, attn_name, attn, key_labels, query_labels, col)\n", + " if attn_name == 'dec_enc_attns':\n", + " col = col + 1\n", + " axes[row, col].axis('off') # next subfig acts as blank place holder\n", + " # plt.suptitle('%s with %d heads, Layer %d' % (attn_name, nheads, layer), fontsize=20)\n", + " plt.show() \n", + " \n", + "ncols = 4" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "attn_name = 'enc_self_attns'\n", + "hypo = {attn_name: [model.bert.encoder.layer[i].attention.self.attention_probs[0] for i in range(config.num_hidden_layers)]}\n", + "key_labels = query_labels = tokens\n", + "labels_dict = {attn_name: (key_labels, query_labels)}\n", + "result_tuple = (hypo, config.num_attention_heads, labels_dict)\n", + "plot_layer_attn(result_tuple, attn_name=attn_name, layer=10, heads=None)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Untitled_likunlin.ipynb b/Untitled_likunlin.ipynb new file mode 100644 index 000000000000..966a1ecde52c --- /dev/null +++ b/Untitled_likunlin.ipynb @@ -0,0 +1,801 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from IPython.core.interactiveshell import InteractiveShell\n", + "InteractiveShell.ast_node_interactivity = 'all'" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "\n", + "import numpy as np\n", + "import math\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "from pylab import rcParams\n", + "\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from pytorch_pretrained_bert import tokenization, BertTokenizer, BertModel, BertForMaskedLM, BertForPreTraining, BertConfig\n", + "from examples.extract_features import *" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "01/21/2019 15:55:04 - INFO - pytorch_pretrained_bert.tokenization - loading vocabulary file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt from cache at /home/xd/.pytorch_pretrained_bert/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084\n", + "01/21/2019 15:55:04 - INFO - pytorch_pretrained_bert.modeling - loading archive file /nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/\n", + "01/21/2019 15:55:04 - INFO - pytorch_pretrained_bert.modeling - Model config {\n", + " \"attention_probs_dropout_prob\": 0.1,\n", + " \"hidden_act\": \"gelu\",\n", + " \"hidden_dropout_prob\": 0.1,\n", + " \"hidden_size\": 768,\n", + " \"initializer_range\": 0.02,\n", + " \"intermediate_size\": 3072,\n", + " \"max_position_embeddings\": 512,\n", + " \"num_attention_heads\": 12,\n", + " \"num_hidden_layers\": 12,\n", + " \"type_vocab_size\": 2,\n", + " \"vocab_size\": 30522\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "class Args:\n", + " def __init__(self):\n", + " pass\n", + " \n", + "args = Args()\n", + "args.no_cuda = True\n", + "\n", + "CONFIG_NAME = 'bert_config.json'\n", + "BERT_DIR = '/nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/'\n", + "config_file = os.path.join(BERT_DIR, CONFIG_NAME)\n", + "config = BertConfig.from_json_file(config_file)\n", + "\n", + "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n", + "model = BertForPreTraining.from_pretrained(BERT_DIR)\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() and not args.no_cuda else \"cpu\")\n", + "_ = model.to(device)\n", + "_ = model.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "def convert_text_to_examples(text):\n", + " examples = []\n", + " unique_id = 0\n", + " if True:\n", + " for line in text:\n", + " line = line.strip()\n", + " text_a = None\n", + " text_b = None\n", + " m = re.match(r\"^(.*) \\|\\|\\| (.*)$\", line)\n", + " if m is None:\n", + " text_a = line\n", + " else:\n", + " text_a = m.group(1)\n", + " text_b = m.group(2)\n", + " examples.append(\n", + " InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b))\n", + " unique_id += 1\n", + " return examples\n", + "\n", + "def convert_examples_to_features(examples, tokenizer, append_special_tokens=True, replace_mask=True, print_info=False):\n", + " features = []\n", + " for (ex_index, example) in enumerate(examples):\n", + " tokens_a = tokenizer.tokenize(example.text_a)\n", + " tokens_b = None\n", + " if example.text_b:\n", + " tokens_b = tokenizer.tokenize(example.text_b)\n", + "\n", + " tokens = []\n", + " input_type_ids = []\n", + " if append_special_tokens:\n", + " tokens.append(\"[CLS]\")\n", + " input_type_ids.append(0)\n", + " for token in tokens_a:\n", + " if replace_mask and token == '_': # XD\n", + " token = \"[MASK]\"\n", + " tokens.append(token)\n", + " input_type_ids.append(0)\n", + " if append_special_tokens:\n", + " tokens.append(\"[SEP]\")\n", + " input_type_ids.append(0)\n", + "\n", + " if tokens_b:\n", + " for token in tokens_b:\n", + " if replace_mask and token == '_': # XD\n", + " token = \"[MASK]\"\n", + " tokens.append(token)\n", + " input_type_ids.append(1)\n", + " if append_special_tokens:\n", + " tokens.append(\"[SEP]\")\n", + " input_type_ids.append(1)\n", + "\n", + " input_ids = tokenizer.convert_tokens_to_ids(tokens)\n", + " input_mask = [1] * len(input_ids)\n", + "\n", + " if ex_index < 5:\n", + "# logger.info(\"*** Example ***\")\n", + "# logger.info(\"unique_id: %s\" % (example.unique_id))\n", + " logger.info(\"tokens: %s\" % \" \".join([str(x) for x in tokens]))\n", + "# logger.info(\"input_ids: %s\" % \" \".join([str(x) for x in input_ids]))\n", + "# logger.info(\"input_mask: %s\" % \" \".join([str(x) for x in input_mask]))\n", + "# logger.info(\n", + "# \"input_type_ids: %s\" % \" \".join([str(x) for x in input_type_ids]))\n", + " \n", + " features.append(\n", + " InputFeatures(\n", + " unique_id=example.unique_id,\n", + " tokens=tokens,\n", + " input_ids=input_ids,\n", + " input_mask=input_mask,\n", + " input_type_ids=input_type_ids))\n", + " return features\n", + "\n", + "def copy_and_mask_feature(feature, masked_tokens=None):\n", + " import copy\n", + " tokens = feature.tokens\n", + " masked_positions = [tokens.index(t) for t in masked_tokens if t in tokens] \\\n", + " if masked_tokens is not None else range(len(tokens))\n", + " assert len(masked_positions) > 0\n", + " masked_feature_copies = []\n", + " for masked_pos in masked_positions:\n", + " feature_copy = copy.deepcopy(feature)\n", + " feature_copy.input_ids[masked_pos] = tokenizer.vocab[\"[MASK]\"]\n", + " masked_feature_copies.append(feature_copy)\n", + " return masked_feature_copies, masked_positions" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def show_lm_probs(tokens, input_ids, probs, topk=5, firstk=20):\n", + " def print_pair(token, prob, end_str='', hit_mark=' '):\n", + " if i < firstk:\n", + " # token = token.replace('', '').replace('\\n', '/n')\n", + " print('{}{: >3} | {: <12}'.format(hit_mark, int(round(prob*100)), token), end=end_str)\n", + " \n", + " ret = None\n", + " for i in range(len(tokens)):\n", + " ind_ = input_ids[i].item() if input_ids is not None else tokenizer.vocab[tokens[i]]\n", + " prob_ = probs[i][ind_].item()\n", + " print_pair(tokens[i], prob_, end_str='\\t')\n", + " values, indices = probs[i].topk(topk)\n", + " top_pairs = []\n", + " for j in range(topk):\n", + " ind, prob = indices[j].item(), values[j].item()\n", + " hit_mark = '*' if ind == ind_ else ' '\n", + " token = tokenizer.ids_to_tokens[ind]\n", + " print_pair(token, prob, hit_mark=hit_mark, end_str='' if j < topk - 1 else '\\n')\n", + " top_pairs.append((token, prob))\n", + " if tokens[i] == \"[MASK]\":\n", + " ret = top_pairs\n", + " return ret" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "import colored\n", + "from colored import stylize\n", + "\n", + "def show_abnormals(tokens, probs, show_suggestions=False):\n", + " def gap2color(gap):\n", + " if gap <= 5:\n", + " return 'yellow_1'\n", + " elif gap <= 10:\n", + " return 'orange_1'\n", + " else:\n", + " return 'red_1'\n", + " \n", + " def print_token(token, suggestion, gap):\n", + " if gap == 0:\n", + " print(stylize(token + ' ', colored.fg('white') + colored.bg('black')), end='')\n", + " else:\n", + " print(stylize(token, colored.fg(gap2color(gap)) + colored.bg('black')), end='')\n", + " if show_suggestions and gap > 5:\n", + " print(stylize('/' + suggestion + ' ', colored.fg('green' if gap > 10 else 'cyan') + colored.bg('black')), end='')\n", + " else:\n", + " print(stylize(' ', colored.fg(gap2color(gap)) + colored.bg('black')), end='')\n", + " # print('/' + suggestion, end=' ')\n", + " # print('%.2f' % gap, end=' ')\n", + " \n", + " avg_gap = 0.\n", + " for i in range(1, len(tokens) - 1): # skip first [CLS] and last [SEP]\n", + " ind_ = tokenizer.vocab[tokens[i]]\n", + " prob_ = probs[i][ind_].item()\n", + " top_prob = probs[i].max().item()\n", + " top_ind = probs[i].argmax().item()\n", + " gap = math.log(top_prob) - math.log(prob_)\n", + " suggestion = tokenizer.ids_to_tokens[top_ind]\n", + " print_token(tokens[i], suggestion, gap)\n", + " avg_gap += gap\n", + " avg_gap /= (len(tokens) - 2)\n", + " print()\n", + " print(avg_gap)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "analyzed_cache = {}\n", + "\n", + "def analyze_text(text, masked_tokens=None, show_suggestions=False, show_firstk_probs=20):\n", + " if text[0] in analyzed_cache:\n", + " features, mlm_probs = analyzed_cache[text[0]]\n", + " given_mask = \"[MASK]\" in features[0].tokens\n", + " tokens = features[0].tokens\n", + " else:\n", + " examples = convert_text_to_examples(text)\n", + " features = convert_examples_to_features(examples, tokenizer, print_info=False)\n", + " given_mask = \"[MASK]\" in features[0].tokens\n", + " if not given_mask or masked_tokens is not None:\n", + " assert len(features) == 1\n", + " features, masked_positions = copy_and_mask_feature(features[0], masked_tokens=masked_tokens)\n", + "\n", + " input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)\n", + " input_type_ids = torch.tensor([f.input_type_ids for f in features], dtype=torch.long)\n", + " input_ids = input_ids.to(device)\n", + " input_type_ids = input_type_ids.to(device)\n", + "\n", + " mlm_logits, _ = model(input_ids, input_type_ids)\n", + " mlm_probs = F.softmax(mlm_logits, dim=-1)\n", + "\n", + " tokens = features[0].tokens\n", + " if not given_mask or masked_tokens is not None:\n", + " bsz, seq_len, vocab_size = mlm_probs.size()\n", + " assert bsz == len(masked_positions)\n", + " # reduced_mlm_probs = torch.Tensor(1, seq_len, vocab_size)\n", + " # for i in range(seq_len):\n", + " # reduced_mlm_probs[0, i] = mlm_probs[i, i]\n", + " reduced_mlm_probs = torch.Tensor(1, len(masked_positions), vocab_size)\n", + " for i, pos in enumerate(masked_positions):\n", + " reduced_mlm_probs[0, i] = mlm_probs[i, pos]\n", + " mlm_probs = reduced_mlm_probs\n", + " tokens = [tokens[i] for i in masked_positions]\n", + " \n", + " analyzed_cache[text[0]] = (features, mlm_probs)\n", + " \n", + " top_pairs = show_lm_probs(tokens, None, mlm_probs[0], firstk=show_firstk_probs)\n", + " if not given_mask:\n", + " show_abnormals(tokens, mlm_probs[0], show_suggestions=show_suggestions)\n", + " return top_pairs" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 | [CLS] \t 3 | . 1 | the 1 | , 1 | ) 1 | \" \n", + " 99 | i \t* 99 | i 0 | we 0 | they 0 | and 0 | always \n", + " 67 | love \t* 67 | love 5 | miss 5 | need 4 | want 2 | promise \n", + " 0 | you \t 87 | . 11 | ! 2 | ; 0 | ... 0 | ? \n", + " 0 | [SEP] \t 12 | . 9 | , 4 | - 2 | and 1 | on \n", + "\u001b[38;5;15m\u001b[48;5;0mi \u001b[0m\u001b[38;5;15m\u001b[48;5;0mlove \u001b[0m\u001b[38;5;214m\u001b[48;5;0myou\u001b[0m\u001b[38;5;214m\u001b[48;5;0m \u001b[0m\n", + "2.310520330937367\n" + ] + } + ], + "source": [ + "# text = [\"Who was Jim Henson? Jim Henson _ a puppeteer.\"]\n", + "# text = [\"Last week I went to the theatre. I had a very good seat. The play was very interesting. But I didn't enjoy it. A young man and a young woman were sitting behind me. They were talking loudly. I got very angry. I couldn't hear a word. I turned round. I looked at the man angrily. They didn't pay any attention.In the end, I couldn't bear it. I turned round again. 'I can't hear a word!' I said angrily. 'It's none of your business,' the young man said rudely. 'This is a private conversation!'\"]\n", + "# text = [\"After the outbreak of the disease, the Ministry of Agriculture and rural areas immediately sent a supervision team to the local. Local Emergency Response Mechanism has been activated in accordance with the requirements, to take blockade, culling, harmless treatment, disinfection and other treatment measures to all disease and culling of pigs for harmless treatment. At the same time, all live pigs and their products are prohibited from transferring out of the blockade area, and live pigs are not allowed to be transported into the blockade area. At present, all the above measures have been implemented.\"]\n", + "# text = [\"Early critics of Emily Dickinson's poetry mistook for simplemindedness the surface of artlessness that in fact she constructed with such innocence.\"]\n", + "text = [\"i love you\"]\n", + "analyze_text(text, show_firstk_probs=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "01/03/2019 17:10:45 - INFO - examples.extract_features - tokens: [CLS] the trophy doesn ' t fit into the brown suitcase because the [MASK] is too large . [SEP]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 | [CLS] \t 2 | . 1 | ) 1 | the 1 | , 1 | \" \n", + " 100 | the \t*100 | the 0 | his 0 | a 0 | its 0 | her \n", + " 97 | trophy \t* 97 | trophy 0 | cup 0 | prize 0 | trophies 0 | competition \n", + " 100 | doesn \t*100 | doesn 0 | can 0 | does 0 | won 0 | didn \n", + " 100 | ' \t*100 | ' 0 | t 0 | \" 0 | = 0 | ` \n", + " 100 | t \t*100 | t 0 | not 0 | s 0 | n 0 | to \n", + " 100 | fit \t*100 | fit 0 | fits 0 | sit 0 | get 0 | fitting \n", + " 100 | into \t*100 | into 0 | in 0 | inside 0 | onto 0 | within \n", + " 100 | the \t*100 | the 0 | her 0 | his 0 | a 0 | my \n", + " 100 | brown \t*100 | brown 0 | black 0 | green 0 | blue 0 | plastic \n", + " 95 | suitcase \t* 95 | suitcase 3 | bag 1 | luggage 0 | backpack 0 | trunk \n", + " 100 | because \t*100 | because 0 | as 0 | since 0 | due 0 | . \n", + " 100 | the \t*100 | the 0 | its 0 | his 0 | it 0 | her \n", + " 0 | [MASK] \t 21 | suitcase 19 | bag 6 | box 2 | luggage 2 | case \n", + " 99 | is \t* 99 | is 1 | was 0 | being 0 | has 0 | it \n", + " 100 | too \t*100 | too 0 | very 0 | extra 0 | overly 0 | more \n", + " 87 | large \t* 87 | large 11 | big 1 | small 1 | huge 0 | larger \n", + " 100 | . \t*100 | . 0 | ; 0 | , 0 | ! 0 | ' \n", + " 0 | [SEP] \t 35 | . 8 | ) 5 | , 4 | ( 3 | it \n" + ] + } + ], + "source": [ + "text = [\"The trophy doesn't fit into the brown suitcase because the _ is too large.\"]\n", + "# text = [\"Mary beat John in the match because _ was very strong.\"]\n", + "features = convert_examples_to_features(convert_text_to_examples(text), tokenizer, print_info=False)\n", + "input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long).to(device)\n", + "input_type_ids = torch.tensor([f.input_type_ids for f in features], dtype=torch.long).to(device)\n", + "mlm_logits, _ = model(input_ids, input_type_ids)\n", + "mlm_probs = F.softmax(mlm_logits, dim=-1)\n", + "tokens = features[0].tokens\n", + "top_pairs = show_lm_probs(tokens, None, mlm_probs[0], firstk=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have the same hair color.',\n", + " 'Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have different hair colors.']" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "text = [\n", + " # same / different\n", + " \"Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have the same hair color.\",\n", + " \"Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have different hair colors.\",\n", + " \"Tom has yellow hair. Mary has black hair. John has black hair. Mary and _ have the same hair color.\",\n", + " # because / although\n", + " \"John is taller/shorter than Mary because/although _ is older/younger.\",\n", + " \"The red ball is heavier/lighter than the blue ball because/although the _ ball is bigger/smaller.\",\n", + " \"Charles did a lot better/worse than his good friend Nancy on the test because/although _ had/hadn't studied so hard.\",\n", + " \"The trophy doesn't fit into the brown suitcase because/although the _ is too small/large.\",\n", + " \"John thought that he would arrive earlier than Susan, but/and indeed _ was the first to arrive.\",\n", + " # reverse\n", + " \"John came then Mary came. They left in reverse order. _ left then _ left.\",\n", + " \"John came after Mary. They left in reverse order. _ left after _ .\",\n", + " \"John came first, then came Mary. They left in reverse order: _ left first, then left _ .\",\n", + " # compare\n", + " \"Though John is tall, Tom is taller than John. So John is _ than Tom.\",\n", + " \"Tom is taller than John. So _ is shorter than _.\",\n", + " # WSC-style: before /after\n", + " \"Mary came before/after John. _ was late/early .\",\n", + " # yes / no\n", + " \"Was Tom taller than Susan? Yes, _ was taller.\",\n", + " # right / wrong, epistemic modality\n", + " \"John said the rain was about to stop. Mary said the rain would continue. Later the rain stopped. _ was wrong.\",\n", + " \n", + " \"The trophy doesn't fit into the brown suitcase because/although the _ is too small/large.\",\n", + " \"John thanked Mary because _ had given help to _ . \",\n", + " \"John felt vindicated/crushed when his longtime rival Mary revealed that _ was the winner of the competition.\",\n", + " \"John couldn't see the stage with Mary in front of him because _ is so short/tall.\",\n", + " \"Although they ran at about the same speed, John beat Sally because _ had such a bad start.\",\n", + " \"The fish ate the worm. The _ was hungry/tasty.\",\n", + " \n", + " \"John beat Mary. _ won the game/e winner.\",\n", + "]\n", + "text" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "with open('WSC_switched_label.json') as f:\n", + " examples = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "with open('WSC_child_problem.json') as f:\n", + " cexamples = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "for ce in cexamples:\n", + " for s in ce['sentences']:\n", + " for a in s['answer0'] + s['answer1']:\n", + " a = a.lower()\n", + " if a not in tokenizer.vocab:\n", + " ce\n", + " print(a, 'not in vocab!!!')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "for ce in cexamples:\n", + " if len(ce['sentences']) > 0:\n", + " e = examples[ce['index']]\n", + " assert ce['index'] == e['index']\n", + " e['score'] = all([s['score'] for s in ce['sentences']])\n", + " assert len(set([s['adjacent_ref'] for s in ce['sentences']])) == 1, 'adjcent_refs are different!'\n", + " e['adjacent_ref'] = ce['sentences'][0]['adjacent_ref']" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "\n", + "groups = defaultdict(list)\n", + "for e in examples:\n", + " if 'score' in e:\n", + " index = e['index']\n", + " if index < 252:\n", + " if index % 2 == 1:\n", + " index -= 1\n", + " elif index in [252, 253, 254]:\n", + " index = 252\n", + " else:\n", + " if index % 2 == 0:\n", + " index -= 1\n", + " groups[index].append(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(2, 'fit into:large/small', False),\n", + " (4, 'thank:receive/give', False),\n", + " (6, 'call:successful available', True),\n", + " (8, 'ask:repeat answer', False),\n", + " (10, 'zoom by:fast/slow', False),\n", + " (12, 'vindicated/crushed:be the winner', False),\n", + " (14, 'lift:weak heavy', False),\n", + " (16, 'crash through:[hard]/[soft]', False),\n", + " (18, '[block]:short/tall', False),\n", + " (20, 'down to:top/bottom', False),\n", + " (22, 'beat:good/bad', False),\n", + " (24, 'roll off:anchored level', False),\n", + " (26, 'above/below', False),\n", + " (28, 'better/worse:study hard', False),\n", + " (30, 'after/before:far away', False),\n", + " (32, 'be upset with:buy from not work/sell not work', True),\n", + " (34, '?yell at comfort:upset', False),\n", + " (36, 'above/below:moved first', False),\n", + " (38, 'although/because', False),\n", + " (40, 'bully:punish rescue', False),\n", + " (42, 'pour:empty/full', False),\n", + " (44, 'know:nosy indiscreet', False),\n", + " (46, 'explain:convince/understand', True),\n", + " (48, '?know tell:so/because', True),\n", + " (50, 'beat:younger/older', False),\n", + " (56, 'clog:cleaned removed', True),\n", + " (58, '?immediately follow:short delayed', False),\n", + " (60, '?between:see see around', True),\n", + " (64, 'but/and', False),\n", + " (66, 'clean:put in the trash put in the drawer', False),\n", + " (68, 'because/but', False),\n", + " (70, 'out of:handy lighter', False),\n", + " (72, 'put:tall high', False),\n", + " (74, 'show:good famous', True),\n", + " (76, 'pay for:generous grateful', False),\n", + " (78, 'but', False),\n", + " (80, 'if', False),\n", + " (82, 'if', False),\n", + " (84, 'fool:get/lose', False),\n", + " (88, 'wait:impatient cautious', False),\n", + " (90, 'give birth:woman baby', True),\n", + " (92, '?stop normal/stop abnormal:strange', False),\n", + " (96, 'eat:hungry tasty', False),\n", + " (98, 'put ... into filled with ... :get in/get out', False),\n", + " (100, 'up:at the bottom/at the top', False),\n", + " (102, 'crash through:removed repaired', False),\n", + " (104, 'stab:taken to the police station taken to the hospital', False),\n", + " (106, 'hear ... humming and whistling:annoyed/annoying', True),\n", + " (108, 'see ... juggling watermelons:impressed/impressive', True),\n", + " (114, 'tell lies: truthful skeptical', True),\n", + " (130, 'but:disappointed', True),\n", + " (132, 'visit:invite come out/invite come in', True),\n", + " (134, 'take classes from:eager known to speak it fluently', False),\n", + " (138, 'cover:out gone', True),\n", + " (144, 'tuck:work sleep', True),\n", + " (150, 'influence:later/earlier', False),\n", + " (152, 'can not cut:thick small', False),\n", + " (154, 'attack:kill guard', False),\n", + " (156, 'attack:bold nervous', False),\n", + " (160, 'change:hard:easy', False),\n", + " (166, 'alive:is/was', False),\n", + " (168, 'infant:twelve years old twelve months old', False),\n", + " (170, 'better equipped and large:defeated/victorious', False),\n", + " (178, 'interview:persistent cooperative', False),\n", + " (186, 'be full of:minority/majority', False),\n", + " (188, 'like over:more/fewer', False),\n", + " (190, 'place on all:not enough/too many', True),\n", + " (192, 'stick:leave have', True),\n", + " (196, 'follow:admire/influence', True),\n", + " (198, 'fit through:wide/narrow', False),\n", + " (200, 'trade:dowdy/great', False),\n", + " (202, 'hire/hire oneself to:take care of', True),\n", + " (204, 'promise/order', False),\n", + " (208, 'mother:education place', True),\n", + " (210, 'knock:get an answer/answer', True),\n", + " (212, 'pay:receive/deliver', False),\n", + " (218, '?', False),\n", + " (220, 'say check:move take', False),\n", + " (222, '?', False),\n", + " (224, 'give a life:drive alone walk', False),\n", + " (226, 'pass the plate:full/hungry', False),\n", + " (228, 'pass:turn over turn next', False),\n", + " (232, 'stretch pat', True),\n", + " (234, 'accept share', False),\n", + " (236, 'speak:break silence break concentration', False),\n", + " (240, 'carry:leg ache leg dangle', True),\n", + " (242, 'carry:in arms in bassinet', False),\n", + " (244, 'hold:against chest against will', True),\n", + " (250, 'stop', False),\n", + " (252, 'even though/because/not', False),\n", + " (255, 'give:not hungry/hungry', False),\n", + " (259, 'ask for a favor:refuse/be refused`', False),\n", + " (261, 'cede:less popular/more popular', False),\n", + " (263, 'not pass although:see open/open', True),\n", + " (271, 'suspect regret', True)]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def filter_dict(d, keys=['index', 'sentence', 'correct_answer', 'relational_word', 'is_associative', 'score']):\n", + " return {k: d[k] for k in d if k in keys}\n", + "\n", + "# ([[filter_dict(e) for e in eg] for eg in groups.values() if eg[0]['relational_word'] != 'none' and all([e['score'] for e in eg])])# / len([eg for eg in groups.values() if eg[0]['relational_word'] != 'none'])\n", + "[(index, eg[0]['relational_word'], all([e['score'] for e in eg])) for index, eg in groups.items() if eg[0]['relational_word'] != 'none']\n", + "# len([filter_dict(e) for e in examples if 'score' in e and not e['score'] and e['adjacent_ref']])\n", + "# for e in examples:\n", + "# if e['index'] % 2 == 0:\n", + "# print(e['sentence'])" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "179" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(['because' in e['sentence'] for e in examples]) + \\\n", + "sum(['so ' in e['sentence'] for e in examples]) + \\\n", + "sum(['but ' in e['sentence'] for e in examples]) + \\\n", + "sum(['though' in e['sentence'] for e in examples])" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "# with open('WSC_switched_label.json', 'w') as f:\n", + "# json.dump(examples, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "vis_attn_topk = 3\n", + "\n", + "def has_chinese_label(labels):\n", + " labels = [label.split('->')[0].strip() for label in labels]\n", + " r = sum([len(label) > 1 for label in labels if label not in ['BOS', 'EOS']]) * 1. / (len(labels) - 1)\n", + " return 0 < r < 0.5 # r == 0 means empty query labels used in self attention\n", + "\n", + "def _plot_attn(ax1, attn_name, attn, key_labels, query_labels, col, color='b'):\n", + " assert len(query_labels) == attn.size(0)\n", + " assert len(key_labels) == attn.size(1)\n", + "\n", + " ax1.set_xlim([-1, 1])\n", + " ax1.set_xticks([])\n", + " ax2 = ax1.twinx()\n", + " nlabels = max(len(key_labels), len(query_labels))\n", + " pos = range(nlabels)\n", + " \n", + " if 'self' in attn_name and col < ncols - 1:\n", + " query_labels = ['' for _ in query_labels]\n", + "\n", + " for ax, labels in [(ax1, key_labels), (ax2, query_labels)]:\n", + " ax.set_yticks(pos)\n", + " if has_chinese_label(labels):\n", + " ax.set_yticklabels(labels, fontproperties=zhfont)\n", + " else:\n", + " ax.set_yticklabels(labels)\n", + " ax.set_ylim([nlabels - 1, 0])\n", + " ax.tick_params(width=0, labelsize='xx-large')\n", + "\n", + " for spine in ax.spines.values():\n", + " spine.set_visible(False)\n", + "\n", + "# mask, attn = filter_attn(attn)\n", + " for qi in range(attn.size(0)):\n", + "# if not mask[qi]:\n", + "# continue\n", + "# for ki in range(attn.size(1)):\n", + " for ki in attn[qi].topk(vis_attn_topk)[1]:\n", + " a = attn[qi, ki]\n", + " ax1.plot((-1, 1), (ki, qi), color, alpha=a)\n", + "# print(attn.mean(dim=0).topk(5)[0])\n", + "# ax1.barh(pos, attn.mean(dim=0).data.cpu().numpy())\n", + "\n", + "def plot_layer_attn(result_tuple, attn_name='dec_self_attns', layer=0, heads=None):\n", + " hypo, nheads, labels_dict = result_tuple\n", + " key_labels, query_labels = labels_dict[attn_name]\n", + " if heads is None:\n", + " heads = range(nheads)\n", + " else:\n", + " nheads = len(heads)\n", + " \n", + " stride = 2 if attn_name == 'dec_enc_attns' else 1\n", + " nlabels = max(len(key_labels), len(query_labels))\n", + " rcParams['figure.figsize'] = 20, int(round(nlabels * stride * nheads / 8 * 1.0))\n", + " \n", + " rows = nheads // ncols * stride\n", + " fig, axes = plt.subplots(rows, ncols)\n", + " \n", + " # for head in range(nheads):\n", + " for head_i, head in enumerate(heads):\n", + " row, col = head_i * stride // ncols, head_i * stride % ncols\n", + " ax1 = axes[row, col]\n", + " attn = hypo[attn_name][layer][head]\n", + " _plot_attn(ax1, attn_name, attn, key_labels, query_labels, col)\n", + " if attn_name == 'dec_enc_attns':\n", + " col = col + 1\n", + " axes[row, col].axis('off') # next subfig acts as blank place holder\n", + " # plt.suptitle('%s with %d heads, Layer %d' % (attn_name, nheads, layer), fontsize=20)\n", + " plt.show() \n", + " \n", + "ncols = 4" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "attn_name = 'enc_self_attns'\n", + "hypo = {attn_name: [model.bert.encoder.layer[i].attention.self.attention_probs[0] for i in range(config.num_hidden_layers)]}\n", + "key_labels = query_labels = tokens\n", + "labels_dict = {attn_name: (key_labels, query_labels)}\n", + "result_tuple = (hypo, config.num_attention_heads, labels_dict)\n", + "plot_layer_attn(result_tuple, attn_name=attn_name, layer=10, heads=None)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Untitled_linzhuo.ipynb b/Untitled_linzhuo.ipynb new file mode 100644 index 000000000000..9627f95eb0ef --- /dev/null +++ b/Untitled_linzhuo.ipynb @@ -0,0 +1,239 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from IPython.core.interactiveshell import InteractiveShell\n", + "InteractiveShell.ast_node_interactivity = 'all'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "\n", + "import numpy as np\n", + "import math\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "from pylab import rcParams\n", + "\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from pytorch_pretrained_bert import tokenization, BertTokenizer, BertModel, BertForMaskedLM, BertForPreTraining, BertConfig\n", + "from examples.extract_features import *" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "01/09/2019 14:00:34 - INFO - pytorch_pretrained_bert.tokenization - loading vocabulary file /nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/vocab.txt\n", + "01/09/2019 14:00:34 - INFO - pytorch_pretrained_bert.modeling - loading archive file /nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/\n", + "01/09/2019 14:00:34 - INFO - pytorch_pretrained_bert.modeling - Model config {\n", + " \"attention_probs_dropout_prob\": 0.1,\n", + " \"hidden_act\": \"gelu\",\n", + " \"hidden_dropout_prob\": 0.1,\n", + " \"hidden_size\": 768,\n", + " \"initializer_range\": 0.02,\n", + " \"intermediate_size\": 3072,\n", + " \"max_position_embeddings\": 512,\n", + " \"num_attention_heads\": 12,\n", + " \"num_hidden_layers\": 12,\n", + " \"type_vocab_size\": 2,\n", + " \"vocab_size\": 30522\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "class Args:\n", + " def __init__(self):\n", + " pass\n", + " \n", + "args = Args()\n", + "args.no_cuda = True\n", + "\n", + "CONFIG_NAME = 'bert_config.json'\n", + "BERT_DIR = '/nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/'\n", + "config_file = os.path.join(BERT_DIR, CONFIG_NAME)\n", + "config = BertConfig.from_json_file(config_file)\n", + "\n", + "tokenizer = BertTokenizer.from_pretrained(os.path.join(BERT_DIR, 'vocab.txt'))\n", + "model = BertForPreTraining.from_pretrained(BERT_DIR)\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() and not args.no_cuda else \"cpu\")\n", + "_ = model.to(device)\n", + "_ = model.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "embedding_layer = model.bert.embeddings\n", + "layers = model.bert.encoder.layer" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "layer = layers[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "BertSelfAttention(\n", + " (query): Linear(in_features=768, out_features=768, bias=True)\n", + " (key): Linear(in_features=768, out_features=768, bias=True)\n", + " (value): Linear(in_features=768, out_features=768, bias=True)\n", + " (dropout): Dropout(p=0.1)\n", + ")" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "layer.attention.self" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([14460])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "words = 'policeman'\n", + "tokens = tokenizer.tokenize(words)\n", + "assert len(tokens) == len(words.split()), tokens\n", + "input_ids = [tokenizer.vocab[token] for token in tokens]\n", + "input_ids = torch.tensor(input_ids, dtype=torch.long).to(device)\n", + "input_ids" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 768])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embedding_layer.average_position_embeddings = embedding_layer.position_embeddings.weight.mean(dim=0, keepdim=True)\n", + "\n", + "def embedding_forward(self, input_ids, token_type_ids=None): \n", + " if token_type_ids is None:\n", + " token_type_ids = torch.zeros_like(input_ids)\n", + " \n", + " word_embeddings = self.word_embeddings(input_ids)\n", + " position_embeddings = self.average_position_embeddings\n", + " token_type_embeddings = self.token_type_embeddings(token_type_ids)\n", + " \n", + " embeddings = word_embeddings + position_embeddings + token_type_embeddings\n", + " embeddings = self.LayerNorm(embeddings)\n", + " return embeddings\n", + "\n", + "embeddings = embedding_forward(embedding_layer, input_ids)\n", + "embeddings.size()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([30522, 768])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def embedding_get_all(self):\n", + " all_embeddings = self.word_embeddings.weight\n", + " token_type_ids = torch.zeros(all_embeddings.size(0), dtype=torch.long)\n", + " token_type_embeddings = self.token_type_embeddings(token_type_ids)\n", + " all_embeddings = all_embeddings + self.average_position_embeddings + token_type_embeddings\n", + " return all_embeddings\n", + "\n", + "all_embeddings = embedding_get_all(embedding_layer)\n", + "all_embeddings.size()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Untitled_linzhuo_maskedlm.ipynb b/Untitled_linzhuo_maskedlm.ipynb new file mode 100644 index 000000000000..20caa7bf8b94 --- /dev/null +++ b/Untitled_linzhuo_maskedlm.ipynb @@ -0,0 +1,1082 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from IPython.core.interactiveshell import InteractiveShell\n", + "InteractiveShell.ast_node_interactivity = 'all'" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "# import seaborn as sns\n", + "import os\n", + "import json\n", + "\n", + "import numpy as np\n", + "import math\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "from pylab import rcParams\n", + "\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from pytorch_pretrained_bert import tokenization, BertTokenizer, BertModel, BertForMaskedLM, BertForPreTraining, BertConfig\n", + "from examples.extract_features import *" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "01/17/2019 18:31:04 - INFO - pytorch_pretrained_bert.tokenization - loading vocabulary file /nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/vocab.txt\n", + "01/17/2019 18:31:04 - INFO - pytorch_pretrained_bert.modeling - loading archive file /nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/\n", + "01/17/2019 18:31:04 - INFO - pytorch_pretrained_bert.modeling - Model config {\n", + " \"attention_probs_dropout_prob\": 0.1,\n", + " \"hidden_act\": \"gelu\",\n", + " \"hidden_dropout_prob\": 0.1,\n", + " \"hidden_size\": 768,\n", + " \"initializer_range\": 0.02,\n", + " \"intermediate_size\": 3072,\n", + " \"max_position_embeddings\": 512,\n", + " \"num_attention_heads\": 12,\n", + " \"num_hidden_layers\": 12,\n", + " \"type_vocab_size\": 2,\n", + " \"vocab_size\": 30522\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "class Args:\n", + " def __init__(self):\n", + " pass\n", + " \n", + "args = Args()\n", + "args.no_cuda = True\n", + "\n", + "CONFIG_NAME = 'bert_config.json'\n", + "BERT_DIR = '/nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/'\n", + "config_file = os.path.join(BERT_DIR, CONFIG_NAME)\n", + "config = BertConfig.from_json_file(config_file)\n", + "\n", + "tokenizer = BertTokenizer.from_pretrained(os.path.join(BERT_DIR, 'vocab.txt'))\n", + "model = BertForPreTraining.from_pretrained(BERT_DIR)\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() and not args.no_cuda else \"cpu\")\n", + "_ = model.to(device)\n", + "_ = model.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "def convert_text_to_examples(text):\n", + " examples = []\n", + " unique_id = 0\n", + " if True:\n", + " for line in text:\n", + " line = line.strip()\n", + " text_a = None\n", + " text_b = None\n", + " m = re.match(r\"^(.*) \\|\\|\\| (.*)$\", line)\n", + " if m is None:\n", + " text_a = line\n", + " else:\n", + " text_a = m.group(1)\n", + " text_b = m.group(2)\n", + " examples.append(\n", + " InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b))\n", + " unique_id += 1\n", + " return examples\n", + "\n", + "def convert_examples_to_features(examples, tokenizer, append_special_tokens=True, replace_mask=True, print_info=False):\n", + " features = []\n", + " for (ex_index, example) in enumerate(examples):\n", + " tokens_a = tokenizer.tokenize(example.text_a)\n", + " tokens_b = None\n", + " if example.text_b:\n", + " tokens_b = tokenizer.tokenize(example.text_b)\n", + "\n", + " tokens = []\n", + " input_type_ids = []\n", + " if append_special_tokens:\n", + " tokens.append(\"[CLS]\")\n", + " input_type_ids.append(0)\n", + " for token in tokens_a:\n", + " if replace_mask and token == '_': # XD\n", + " token = \"[MASK]\"\n", + " tokens.append(token)\n", + " input_type_ids.append(0)\n", + " if append_special_tokens:\n", + " tokens.append(\"[SEP]\")\n", + " input_type_ids.append(0)\n", + "\n", + " if tokens_b:\n", + " for token in tokens_b:\n", + " if replace_mask and token == '_': # XD\n", + " token = \"[MASK]\"\n", + " tokens.append(token)\n", + " input_type_ids.append(1)\n", + " if append_special_tokens:\n", + " tokens.append(\"[SEP]\")\n", + " input_type_ids.append(1)\n", + "\n", + " input_ids = tokenizer.convert_tokens_to_ids(tokens)\n", + " input_mask = [1] * len(input_ids)\n", + "\n", + " if ex_index < 5:\n", + "# logger.info(\"*** Example ***\")\n", + "# logger.info(\"unique_id: %s\" % (example.unique_id))\n", + " logger.info(\"tokens: %s\" % \" \".join([str(x) for x in tokens]))\n", + "# logger.info(\"input_ids: %s\" % \" \".join([str(x) for x in input_ids]))\n", + "# logger.info(\"input_mask: %s\" % \" \".join([str(x) for x in input_mask]))\n", + "# logger.info(\n", + "# \"input_type_ids: %s\" % \" \".join([str(x) for x in input_type_ids]))\n", + " \n", + " features.append(\n", + " InputFeatures(\n", + " unique_id=example.unique_id,\n", + " tokens=tokens,\n", + " input_ids=input_ids,\n", + " input_mask=input_mask,\n", + " input_type_ids=input_type_ids))\n", + " return features\n", + "\n", + "def copy_and_mask_feature(feature, masked_tokens=None):\n", + " import copy\n", + " tokens = feature.tokens\n", + " masked_positions = [tokens.index(t) for t in masked_tokens if t in tokens] \\\n", + " if masked_tokens is not None else range(len(tokens))\n", + " assert len(masked_positions) > 0\n", + " masked_feature_copies = []\n", + " for masked_pos in masked_positions:\n", + " feature_copy = copy.deepcopy(feature)\n", + " feature_copy.input_ids[masked_pos] = tokenizer.vocab[\"[MASK]\"]\n", + " masked_feature_copies.append(feature_copy)\n", + " return masked_feature_copies, masked_positions" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "def show_lm_probs(tokens, input_ids, probs, topk=5, firstk=20):\n", + " def print_pair(token, prob, end_str='', hit_mark=' '):\n", + " if i < firstk:\n", + " # token = token.replace('', '').replace('\\n', '/n')\n", + " print('{}{: >3} | {: <12}'.format(hit_mark, int(round(prob*100)), token), end=end_str)\n", + " \n", + " ret = None\n", + " for i in range(len(tokens)):\n", + " ind_ = input_ids[i].item() if input_ids is not None else tokenizer.vocab[tokens[i]]\n", + " prob_ = probs[i][ind_].item()\n", + " print_pair(tokens[i], prob_, end_str='\\t')\n", + " values, indices = probs[i].topk(topk)\n", + " top_pairs = []\n", + " for j in range(topk):\n", + " ind, prob = indices[j].item(), values[j].item()\n", + " hit_mark = '*' if ind == ind_ else ' '\n", + " token = tokenizer.ids_to_tokens[ind]\n", + " print_pair(token, prob, hit_mark=hit_mark, end_str='' if j < topk - 1 else '\\n')\n", + " top_pairs.append((token, prob))\n", + " if tokens[i] == \"[MASK]\":\n", + " ret = top_pairs\n", + " return ret" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "import colored\n", + "from colored import stylize\n", + "\n", + "def show_abnormals(tokens, probs, show_suggestions=False):\n", + " def gap2color(gap):\n", + " if gap <= 5:\n", + " return 'yellow_1'\n", + " elif gap <= 10:\n", + " return 'orange_1'\n", + " else:\n", + " return 'red_1'\n", + " \n", + " def print_token(token, suggestion, gap):\n", + " if gap == 0:\n", + " print(stylize(token + ' ', colored.fg('white') + colored.bg('black')), end='')\n", + " else:\n", + " print(stylize(token, colored.fg(gap2color(gap)) + colored.bg('black')), end='')\n", + " if show_suggestions and gap > 5:\n", + " print(stylize('/' + suggestion + ' ', colored.fg('green' if gap > 10 else 'cyan') + colored.bg('black')), end='')\n", + " else:\n", + " print(stylize(' ', colored.fg(gap2color(gap)) + colored.bg('black')), end='')\n", + " # print('/' + suggestion, end=' ')\n", + " # print('%.2f' % gap, end=' ')\n", + " \n", + " avg_gap = 0.\n", + " for i in range(1, len(tokens) - 1): # skip first [CLS] and last [SEP]\n", + " ind_ = tokenizer.vocab[tokens[i]]\n", + " prob_ = probs[i][ind_].item()\n", + " top_prob = probs[i].max().item()\n", + " top_ind = probs[i].argmax().item()\n", + " gap = math.log(top_prob) - math.log(prob_)\n", + " suggestion = tokenizer.ids_to_tokens[top_ind]\n", + " print_token(tokens[i], suggestion, gap)\n", + " avg_gap += gap\n", + " avg_gap /= (len(tokens) - 2)\n", + " print()\n", + " print(avg_gap)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "analyzed_cache = {}\n", + "\n", + "def analyze_text(text, masked_tokens=None, show_suggestions=False, show_firstk_probs=20):\n", + " if text[0] in analyzed_cache:\n", + " features, mlm_probs = analyzed_cache[text[0]]\n", + " given_mask = \"[MASK]\" in features[0].tokens\n", + " tokens = features[0].tokens\n", + " else:\n", + " examples = convert_text_to_examples(text)\n", + " features = convert_examples_to_features(examples, tokenizer, print_info=False)\n", + " given_mask = \"[MASK]\" in features[0].tokens\n", + " if not given_mask or masked_tokens is not None:\n", + " assert len(features) == 1\n", + " features, masked_positions = copy_and_mask_feature(features[0], masked_tokens=masked_tokens)\n", + "\n", + " input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)\n", + " input_type_ids = torch.tensor([f.input_type_ids for f in features], dtype=torch.long)\n", + " input_ids = input_ids.to(device)\n", + " input_type_ids = input_type_ids.to(device)\n", + "\n", + " mlm_logits, _ = model(input_ids, input_type_ids)\n", + " mlm_probs = F.softmax(mlm_logits, dim=-1)\n", + "\n", + " tokens = features[0].tokens\n", + " if not given_mask or masked_tokens is not None:\n", + " bsz, seq_len, vocab_size = mlm_probs.size()\n", + " assert bsz == len(masked_positions)\n", + " # reduced_mlm_probs = torch.Tensor(1, seq_len, vocab_size)\n", + " # for i in range(seq_len):\n", + " # reduced_mlm_probs[0, i] = mlm_probs[i, i]\n", + " reduced_mlm_probs = torch.Tensor(1, len(masked_positions), vocab_size)\n", + " for i, pos in enumerate(masked_positions):\n", + " reduced_mlm_probs[0, i] = mlm_probs[i, pos]\n", + " mlm_probs = reduced_mlm_probs\n", + " tokens = [tokens[i] for i in masked_positions]\n", + " \n", + " analyzed_cache[text[0]] = (features, mlm_probs)\n", + " \n", + " top_pairs = show_lm_probs(tokens, None, mlm_probs[0], firstk=show_firstk_probs)\n", + " if not given_mask:\n", + " show_abnormals(tokens, mlm_probs[0], show_suggestions=show_suggestions)\n", + " return top_pairs" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/qsj/miniconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", + " return f(*args, **kwds)\n" + ] + } + ], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "110300" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_train =pd.read_csv('/nas/xd/data/gan_prompt_remain_OPENAI_TOKENED_new2.txt',delimiter='\\t',header=None,quotechar='&')\n", + "len(df_train)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "ss = [row[0]+' '+row[1] for row in df_train[[3,4]].values]" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Why didn't the skeleton go to the dance? He had no-BODY to go with.\"" + ] + }, + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sss=ss[22]\n", + "sss" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [], + "source": [ + "sss =\"why didn ' t the girls come to the party ? i had no - one to party with .\"" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "01/17/2019 19:40:30 - INFO - examples.extract_features - tokens: [CLS] why didn ' t the girls come to the party ? i had no - one to party with . [SEP]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 | [CLS] \t 3 | . 1 | the 1 | ) 1 | , 1 | \" \n", + " 100 | why \t*100 | why 0 | and 0 | but 0 | \" 0 | ' \n", + " 63 | didn \t* 63 | didn 15 | couldn 9 | wouldn 5 | don 3 | hadn \n", + " 100 | ' \t*100 | ' 0 | - 0 | , 0 | = 0 | ` \n", + " 100 | t \t*100 | t 0 | d 0 | s 0 | n 0 | ts \n", + " 69 | the \t* 69 | the 9 | other 6 | my 3 | these 2 | any \n", + " 32 | girls \t* 32 | girls 8 | boys 7 | guys 4 | police 4 | others \n", + " 73 | come \t* 73 | come 25 | go 0 | get 0 | stay 0 | came \n", + " 98 | to \t* 98 | to 1 | for 0 | into 0 | at 0 | after \n", + " 42 | the \t* 42 | the 40 | my 5 | this 3 | our 3 | that \n", + " 83 | party \t* 83 | party 3 | house 2 | club 2 | parties 1 | dance \n", + " 99 | ? \t* 99 | ? 0 | when 0 | . 0 | because 0 | , \n", + " 38 | i \t* 38 | i 20 | she 16 | they 10 | we 5 | he \n", + " 81 | had \t* 81 | had 17 | have 0 | was 0 | saw 0 | has \n", + " 100 | no \t*100 | no 0 | twenty 0 | number 0 | non 0 | zero \n", + " 88 | - \t* 88 | - 4 | other 2 | ' 1 | real 1 | . \n", + " 99 | one \t* 99 | one 0 | ones 0 | friends 0 | girls 0 | girl \n", + " 100 | to \t*100 | to 0 | i 0 | they 0 | a 0 | the \n", + " 15 | party \t* 15 | party 9 | be 8 | celebrate 8 | dance 7 | play \n", + " 97 | with \t* 97 | with 1 | to 1 | for 0 | tonight 0 | around \n", + " 96 | . \t* 96 | . 2 | ! 2 | ; 0 | ? 0 | | \n", + " 0 | [SEP] \t 5 | i 5 | \" 2 | and 2 | the 2 | . \n", + "\u001b[38;5;15m\u001b[48;5;0mwhy \u001b[0m\u001b[38;5;15m\u001b[48;5;0mdidn \u001b[0m\u001b[38;5;15m\u001b[48;5;0m' \u001b[0m\u001b[38;5;15m\u001b[48;5;0mt \u001b[0m\u001b[38;5;15m\u001b[48;5;0mthe \u001b[0m\u001b[38;5;15m\u001b[48;5;0mgirls \u001b[0m\u001b[38;5;15m\u001b[48;5;0mcome \u001b[0m\u001b[38;5;15m\u001b[48;5;0mto \u001b[0m\u001b[38;5;15m\u001b[48;5;0mthe \u001b[0m\u001b[38;5;15m\u001b[48;5;0mparty \u001b[0m\u001b[38;5;15m\u001b[48;5;0m? \u001b[0m\u001b[38;5;15m\u001b[48;5;0mi \u001b[0m\u001b[38;5;15m\u001b[48;5;0mhad \u001b[0m\u001b[38;5;15m\u001b[48;5;0mno \u001b[0m\u001b[38;5;15m\u001b[48;5;0m- \u001b[0m\u001b[38;5;15m\u001b[48;5;0mone \u001b[0m\u001b[38;5;15m\u001b[48;5;0mto \u001b[0m\u001b[38;5;15m\u001b[48;5;0mparty \u001b[0m\u001b[38;5;15m\u001b[48;5;0mwith \u001b[0m\u001b[38;5;15m\u001b[48;5;0m. \u001b[0m\n", + "0.0\n" + ] + } + ], + "source": [ + "# text = [\"Who was Jim Henson? Jim Henson _ a puppeteer.\"]\n", + "text = [\"I went to school by myself. I had no seat on the bus.\"]\n", + "# text = [\"I thought that John defeated Mary. I was wrong. _ beat _.\"]\n", + "# text = [\"Did John defeat Mary? No, _ beat _.\"]\n", + "# text = [\"That mary defeated John contradicts the fact that _ beat _.\"]\n", + "# text = [\"After the outbreak of the disease, the Ministry of Agriculture and rural areas immediately sent a supervision team to the local. Local Emergency Response Mechanism has been activated in accordance with the requirements, to take blockade, culling, harmless treatment, disinfection and other treatment measures to all disease and culling of pigs for harmless treatment. At the same time, all live pigs and their products are prohibited from transferring out of the blockade area, and live pigs are not allowed to be transported into the blockade area. At present, all the above measures have been implemented.\"]\n", + "# text = [\"Early critics of Emily Dickinson's poetry mistook for simplemindedness the surface of artlessness that in fact she constructed with such innocence.\"]\n", + "\n", + "text = [sss] #\n", + "analyze_text(text, show_firstk_probs=100)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def words2heads(attns, tokens, words):\n", + " positions = [tokens.index(word) for word in words]\n", + "\n", + " for layer in range(config.num_hidden_layers):\n", + " for head in range(config.num_attention_heads):\n", + " for pos_indices in [(0, 1), (1, 0)]:\n", + " from_pos, to_pos = positions[pos_indices[0]], positions[pos_indices[1]]\n", + " if attns[layer][head][from_pos].max(0)[1].item() == to_pos:\n", + " print('Layer %d, head %d: %s -> %s' % (layer, head, tokens[from_pos], tokens[to_pos]), end='\\t')\n", + " print(attns[layer][head][from_pos].topk(5)[0].data)\n", + "\n", + "def head2words(attns, tokens, layer, head):\n", + " for from_pos in range(len(tokens)):\n", + " to_pos = attns[layer][head][from_pos].max(0)[1].item()\n", + " from_word, to_word = tokens[from_pos], tokens[to_pos]\n", + " if from_word.isalpha() and to_word.isalpha():\n", + " print('%s @ %d -> %s @ %d' % (from_word, from_pos, to_word, to_pos), end='\\t')\n", + " print(attns[layer][head][from_pos].topk(5)[0].data)\n", + " \n", + "special_tokens = ['[CLS]', '[SEP]']\n", + "\n", + "def get_salient_heads(attns, tokens, attn_thld=0.5):\n", + " for layer in range(config.num_hidden_layers):\n", + " for head in range(config.num_attention_heads):\n", + " pos_pairs = []\n", + " for from_pos in range(1, len(tokens) - 1): # skip [CLS] and [SEP]\n", + " top_attn, to_pos = attns[layer][head][from_pos].max(0)\n", + " top_attn, to_pos = top_attn.item(), to_pos.item()\n", + " from_word, to_word = tokens[from_pos], tokens[to_pos]\n", + "# if from_word.isalpha() and to_word.isalpha() and top_attn >= attn_thld:\n", + " if abs(from_pos - to_pos) <= 1:\n", + "# print('Layer %d, head %d: %s @ %d -> %s @ %d' % (layer, head, from_word, from_pos, to_word, to_pos), end='\\t')\n", + "# print(attns[layer][head][from_pos].topk(5)[0].data)\n", + " pos_pairs.append((from_pos, to_pos))\n", + " \n", + " ratio = len(pos_pairs) / (len(tokens) - 2)\n", + " if ratio > 0.5:\n", + " print(ratio)\n", + " for from_pos, to_pos in pos_pairs:\n", + " print('Layer %d, head %d: %s @ %d -> %s @ %d' % (layer, head, tokens[from_pos], from_pos, tokens[to_pos], to_pos), end='\\t')\n", + " print(attns[layer][head][from_pos].topk(5)[0].data)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "01/10/2019 21:46:20 - INFO - examples.extract_features - tokens: [CLS] jim laughed because he was so happy . [SEP]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "jim @ 1 -> jim @ 1\ttensor([0.7248, 0.0842, 0.0656, 0.0407, 0.0319], device='cuda:0')\n" + ] + } + ], + "source": [ + "# text, words = [\"The trophy doesn't fit into the brown suitcase because the it is too large.\"], ['fit', 'large']\n", + "# text, words = [\"Mary couldn't beat John in the match because he was too strong.\"], ['beat', 'strong']\n", + "text, words = [\"John is taller than Mary because he is older.\"], ['taller', 'older']\n", + "# text, words = [\"The red ball is heavier than the blue ball because the red ball is bigger.\"], ['heavier', 'bigger']\n", + "text, words = [\"Jim laughed because he was so happy.\"], ['cried', 'sad']\n", + "# text, words = [\"Jim ate the cake quickly because he was so hungry.\"], ['ate', 'hungry']\n", + "# text, words = [\"Jim drank the juice quickly because he was so thirsty.\"], ['drank', 'thirsty']\n", + "# text, words = [\"Tom's drawing hangs high. It is above Susan's drawing\"], ['high', 'above']\n", + "# text, words = [\"Tom's drawing hangs low. It is below Susan's drawing\"], ['low', 'below']\n", + "# text, words = [\"John is taller than Mary . Mary is shorter than John.\"], ['taller', 'shorter']\n", + "# text, words = [\"The drawing is above the cabinet. The cabinet is below the drawing\"], ['above', 'below']\n", + "# text, words = [\"Jim is very thin . He is not fat.\"], ['thin', 'fat']\n", + "\n", + "features = convert_examples_to_features(convert_text_to_examples(text), tokenizer, print_info=False)\n", + "input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long).to(device)\n", + "input_type_ids = torch.tensor([f.input_type_ids for f in features], dtype=torch.long).to(device)\n", + "mlm_logits, _ = model(input_ids, input_type_ids)\n", + "mlm_probs = F.softmax(mlm_logits, dim=-1)\n", + "tokens = features[0].tokens\n", + "# top_pairs = show_lm_probs(tokens, None, mlm_probs[0], firstk=100)\n", + "\n", + "attn_name = 'enc_self_attns'\n", + "hypo = {attn_name: [model.bert.encoder.layer[i].attention.self.attention_probs[0] for i in range(config.num_hidden_layers)]}\n", + "key_labels = query_labels = tokens\n", + "labels_dict = {attn_name: (key_labels, query_labels)}\n", + "result_tuple = (hypo, config.num_attention_heads, labels_dict)\n", + "# plot_layer_attn(result_tuple, attn_name=attn_name, layer=10, heads=None)\n", + "\n", + "attns = hypo[attn_name]\n", + " \n", + "# words2heads(attns, tokens, words)\n", + "head2words(attns, tokens, 2, 10)\n", + "# get_salient_heads(attns, tokens, attn_thld=0.0)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "0，2\t-1\n", + "0，3\t-1\n", + "0，10\t+1 动宾\n", + "1，1\t+1 动介\n", + "1，4\t-1\n", + "1，11\t0\n", + "2，0\t+1**\n", + "2，6\t0**\n", + "2，9\t+1**\n", + "3，5\t-1\n", + "7，4\t-1\n", + "11，8\t0\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head_size = config.hidden_size // config.num_attention_heads\n", + "layer = 1\n", + "head = 1 # 2, 3, 10\n", + "wq = model.bert.encoder.layer[layer].attention.self.query.weight.data.view(-1, config.num_attention_heads, head_size).permute(1, 0, 2)\n", + "wk = model.bert.encoder.layer[layer].attention.self.key.weight.data.view(-1, config.num_attention_heads, head_size).permute(1, 0, 2)\n", + "\n", + "wqk = torch.bmm(wq, wk.transpose(-1, -2))\n", + "# (wqk * wqk.transpose(-1, -2)).sum((1, 2)) / (wqk * wqk).sum((1, 2))\n", + "plt.imshow(wqk[head]*wqk[head])\n", + "plt.show()\n", + "\n", + "# q = torch.matmul(pos_emb, wq)\n", + "# k = torch.matmul(pos_emb_prev, wk)\n", + "# (q * k).sum((-2, -1))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "pos_emb = model.bert.embeddings.position_embeddings.weight.data\n", + "pos_emb_prev = torch.zeros_like(pos_emb)\n", + "pos_emb_next = torch.zeros_like(pos_emb)\n", + "pos_emb_prev[1:] = pos_emb[:-1]\n", + "pos_emb_next[:-1] = pos_emb[1:]\n", + "pos_emb, pos_emb_prev, pos_emb_next = pos_emb[1:-1], pos_emb_prev[1:-1], pos_emb_next[1:-1]\n", + "\n", + "# pos_q = torch.matmul(pos_emb, wk[head])\n", + "# plt.imshow(pos_q[:32])\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{\n", + " \"attention_probs_dropout_prob\": 0.1,\n", + " \"hidden_act\": \"gelu\",\n", + " \"hidden_dropout_prob\": 0.1,\n", + " \"hidden_size\": 768,\n", + " \"initializer_range\": 0.02,\n", + " \"intermediate_size\": 3072,\n", + " \"max_position_embeddings\": 512,\n", + " \"num_attention_heads\": 12,\n", + " \"num_hidden_layers\": 12,\n", + " \"type_vocab_size\": 2,\n", + " \"vocab_size\": 30522\n", + "}" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have the same hair color.',\n", + " 'Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have different hair colors.']" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "text = [\n", + " # same / different\n", + " \"Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have the same hair color.\",\n", + " \"Tom has black hair. Mary has black hair. John has yellow hair. _ and Mary have different hair colors.\",\n", + " \"Tom has yellow hair. Mary has black hair. John has black hair. Mary and _ have the same hair color.\",\n", + " # because / although\n", + " \"John is taller/shorter than Mary because/although _ is older/younger.\",\n", + " \"The red ball is heavier/lighter than the blue ball because/although the _ ball is bigger/smaller.\",\n", + " \"Charles did a lot better/worse than his good friend Nancy on the test because/although _ had/hadn't studied so hard.\",\n", + " \"The trophy doesn't fit into the brown suitcase because/although the _ is too small/large.\",\n", + " \"John thought that he would arrive earlier than Susan, but/and indeed _ was the first to arrive.\",\n", + " # reverse\n", + " \"John came then Mary came. They left in reverse order. _ left then _ left.\",\n", + " \"John came after Mary. They left in reverse order. _ left after _ .\",\n", + " \"John came first, then came Mary. They left in reverse order: _ left first, then left _ .\",\n", + " # compare sentences with same / opposite meaning, 2nd order\n", + " \"Though John is tall, Tom is taller than John. So John is _ than Tom.\",\n", + " \"Tom is taller than John. So _ is shorter than _.\",\n", + " # WSC-style: before /after\n", + " # \"Mary came before/after John. _ was late/early .\",\n", + " # yes / no, 2nd order\n", + " \"Was Tom taller than Susan? Yes, _ was taller.\",\n", + " # right / wrong, epistemic modality, 2nd order\n", + " \"John said/thought that the red ball was heavier than the blue ball. He was wrong. The _ ball was heavier\",\n", + " \"John was wrong in saying/thinking that the red ball was heavier than the blue ball. The _ ball was heavier\",\n", + " \"John said the rain was about to stop. Mary said the rain would continue. Later the rain stopped. _ was wrong/right.\",\n", + " \n", + " \"The trophy doesn't fit into the brown suitcase because/although the _ is too small/large.\",\n", + " \"John thanked Mary because _ had given help to _ . \",\n", + " \"John felt vindicated/crushed when his longtime rival Mary revealed that _ was the winner of the competition.\",\n", + " \"John couldn't see the stage with Mary in front of him because _ is so short/tall.\",\n", + " \"Although they ran at about the same speed, John beat Sally because _ had such a bad start.\",\n", + " \"The fish ate the worm. The _ was hungry/tasty.\",\n", + " \n", + " \"John beat Mary. _ won the game/e winner.\",\n", + "]\n", + "text" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "with open('WSC_switched_label.json') as f:\n", + " examples = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "with open('WSC_child_problem.json') as f:\n", + " cexamples = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "for ce in cexamples:\n", + " for s in ce['sentences']:\n", + " for a in s['answer0'] + s['answer1']:\n", + " a = a.lower()\n", + " if a not in tokenizer.vocab:\n", + " ce\n", + " print(a, 'not in vocab!!!')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "for ce in cexamples:\n", + " if len(ce['sentences']) > 0:\n", + " e = examples[ce['index']]\n", + " assert ce['index'] == e['index']\n", + " e['score'] = all([s['score'] for s in ce['sentences']])\n", + " assert len(set([s['adjacent_ref'] for s in ce['sentences']])) == 1, 'adjcent_refs are different!'\n", + " e['adjacent_ref'] = ce['sentences'][0]['adjacent_ref']" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "\n", + "groups = defaultdict(list)\n", + "for e in examples:\n", + " if 'score' in e:\n", + " index = e['index']\n", + " if index < 252:\n", + " if index % 2 == 1:\n", + " index -= 1\n", + " elif index in [252, 253, 254]:\n", + " index = 252\n", + " else:\n", + " if index % 2 == 0:\n", + " index -= 1\n", + " groups[index].append(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(\"The trophy doesn't fit into the brown suitcase because [it] is too large.\",\n", + " 'fit into:large/small'),\n", + " ('Joan made sure to thank Susan for all the help [she] had recieved.',\n", + " 'thank:receive/give'),\n", + " ('The delivery truck zoomed by the school bus because [it] was going so fast.',\n", + " 'zoom by:fast/slow'),\n", + " ('Frank felt vindicated when his longtime rival Bill revealed that [he] was the winner of the competition.',\n", + " 'vindicated/crushed:be the winner'),\n", + " ('The large ball crashed right through the table because [it] was made of steel.',\n", + " 'crash through:[hard]/[soft]'),\n", + " (\"John couldn't see the stage with Billy in front of him because [he] is so short.\",\n", + " '[block]:short/tall'),\n", + " ('Tom threw his schoolbag down to Ray after [he] reached the top of the stairs.',\n", + " 'down to:top/bottom'),\n", + " ('Although they ran at about the same speed, Sue beat Sally because [she] had such a good start.',\n", + " 'beat:good/bad'),\n", + " (\"Sam's drawing was hung just above Tina's and [it] did look much better with another one below it.\",\n", + " 'above/below'),\n", + " ('Anna did a lot better than her good friend Lucy on the test because [she] had studied so hard.',\n", + " 'better/worse:study hard'),\n", + " ('The firemen arrived after the police because [they] were coming from so far away.',\n", + " 'after/before:far away'),\n", + " (\"Frank was upset with Tom because the toaster [he] had bought from him didn't work.\",\n", + " 'be upset with:buy from not work/sell not work'),\n", + " ('The sack of potatoes had been placed above the bag of flour, so [it] had to be moved first.',\n", + " 'above/below:moved first'),\n", + " ('Pete envies Martin although [he] is very successful.', 'although/because'),\n", + " ('I poured water from the bottle into the cup until [it] was empty.',\n", + " 'pour:empty/full'),\n", + " (\"Sid explained his theory to Mark but [he] couldn't convince him.\",\n", + " 'explain:convince/understand'),\n", + " (\"Susan knew that Ann's son had been in a car accident, so [she] told her about it.\",\n", + " '?know tell:so/because'),\n", + " (\"Joe's uncle can still beat him at tennis, even though [he] is 30 years younger.\",\n", + " 'beat:younger/older'),\n", + " ('In the middle of the outdoor concert, the rain started falling, but [it] continued until 10.',\n", + " 'but/and'),\n", + " ('Ann asked Mary what time the library closes, because [she] had forgotten.',\n", + " 'because/but'),\n", + " ('If the con artist has succeeded in fooling Sam, [he] would have gotten a lot of money.',\n", + " 'fool:get/lose'),\n", + " ('Alice tried frantically to stop her daughter from chatting at the party, leaving us to wonder why [she] was behaving so strangely.',\n", + " '?stop normal/stop abnormal:strange'),\n", + " (\"I was trying to open the lock with the key, but someone had filled the keyhole with chewing gum, and I couldn't get [it] in.\",\n", + " 'put ... into filled with ... :get in/get out'),\n", + " ('The dog chased the cat, which ran up a tree. [It] waited at the bottom.',\n", + " 'up:at the bottom/at the top'),\n", + " ('John was doing research in the library when he heard a man humming and whistling. [He] was very annoyed.',\n", + " 'hear ... humming and whistling:annoyed/annoying'),\n", + " ('John was jogging through the park when he saw a man juggling watermelons. [He] was very impressed.',\n", + " 'see ... juggling watermelons:impressed/impressive'),\n", + " ('Jane knocked on the door, and Susan answered it. [She] invited her to come out.',\n", + " 'visit:invite come out/invite come in'),\n", + " ('Jackson was greatly influenced by Arnold, though [he] lived two centuries later.',\n", + " 'influence:later/earlier'),\n", + " ('The actress used to be named Terpsichore, but she changed it to Tina a few years ago, because she figured [it] was too hard to pronounce.',\n", + " 'change:hard/easy'),\n", + " ('Fred is the only man still alive who remembers my great-grandfather. [He] is a remarkable man.',\n", + " 'alive:is/was'),\n", + " (\"In July, Kamtchatka declared war on Yakutsk. Since Yakutsk's army was much better equipped and ten times larger, [they] were defeated within weeks.\",\n", + " 'better equipped and large:defeated/victorious'),\n", + " ('When the sponsors of the bill got to the town hall, they were surprised to find that the room was full of opponents. [They] were very much in the minority.',\n", + " 'be full of:minority/majority'),\n", + " ('Everyone really loved the oatmeal cookies; only a few people liked the chocolate chip cookies. Next time, we should make more of [them] .',\n", + " 'like over:more/fewer'),\n", + " ('We had hoped to place copies of our newsletter on all the chairs in the auditorium, but there were simply not enough of [them] .',\n", + " 'place on all:not enough/too many'),\n", + " (\"Steve follows Fred's example in everything. [He] admires him hugely.\",\n", + " 'follow:admire/influence'),\n", + " (\"The table won't fit through the doorway because [it] is too wide.\",\n", + " 'fit through:wide/narrow'),\n", + " ('Grace was happy to trade me her sweater for my jacket. She thinks [it] looks dowdy on her.',\n", + " 'trade:dowdy/great'),\n", + " ('John hired Bill to take care of [him] .',\n", + " 'hire/hire oneself to:take care of'),\n", + " ('John promised Bill to leave, so an hour later [he] left.', 'promise/order'),\n", + " (\"Jane knocked on Susan's door but [she] did not get an answer.\",\n", + " 'knock:get an answer/answer'),\n", + " ('Joe paid the detective after [he] received the final report on the case.',\n", + " 'pay:receive/deliver'),\n", + " ('Bill passed the half-empty plate to John because [he] was full.',\n", + " 'pass the plate:full/hungry'),\n", + " ('George got free tickets to the play, but he gave them to Eric, even though [he] was particularly eager to see it.',\n", + " 'even though/because/not'),\n", + " (\"Jane gave Joan candy because [she] wasn't hungry.\",\n", + " 'give:not hungry/hungry'),\n", + " ('James asked Robert for a favor but [he] was refused.',\n", + " 'ask for a favor:refuse/be refused`'),\n", + " ('Kirilov ceded the presidency to Shatov because [he] was less popular.',\n", + " 'cede:less popular/more popular'),\n", + " ('Emma did not pass the ball to Janie although [she] saw that she was open.',\n", + " 'not pass although:see open/open')]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def filter_dict(d, keys=['index', 'sentence', 'correct_answer', 'relational_word', 'is_associative', 'score']):\n", + " return {k: d[k] for k in d if k in keys}\n", + "\n", + "# ([[filter_dict(e) for e in eg] for eg in groups.values() if eg[0]['relational_word'] != 'none' and all([e['score'] for e in eg])])# / len([eg for eg in groups.values() if eg[0]['relational_word'] != 'none'])\n", + "# [(index, eg[0]['relational_word'], all([e['score'] for e in eg])) for index, eg in groups.items() if eg[0]['relational_word'] != 'none']\n", + "# len([filter_dict(e) for e in examples if 'score' in e and not e['score'] and e['adjacent_ref']])\n", + "# for e in examples:\n", + "# if e['index'] % 2 == 0:\n", + "# print(e['sentence'])\n", + "[(eg[0]['sentence'], eg[0]['relational_word']) for index, eg in groups.items() if '/' in eg[0]['relational_word']]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "179" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(['because' in e['sentence'] for e in examples]) + \\\n", + "sum(['so ' in e['sentence'] for e in examples]) + \\\n", + "sum(['but ' in e['sentence'] for e in examples]) + \\\n", + "sum(['though' in e['sentence'] for e in examples])" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "# with open('WSC_switched_label.json', 'w') as f:\n", + "# json.dump(examples, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "vis_attn_topk = 3\n", + "\n", + "def has_chinese_label(labels):\n", + " labels = [label.split('->')[0].strip() for label in labels]\n", + " r = sum([len(label) > 1 for label in labels if label not in ['BOS', 'EOS']]) * 1. / (len(labels) - 1)\n", + " return 0 < r < 0.5 # r == 0 means empty query labels used in self attention\n", + "\n", + "def _plot_attn(ax1, attn_name, attn, key_labels, query_labels, col, color='b'):\n", + " assert len(query_labels) == attn.size(0)\n", + " assert len(key_labels) == attn.size(1)\n", + "\n", + " ax1.set_xlim([-1, 1])\n", + " ax1.set_xticks([])\n", + " ax2 = ax1.twinx()\n", + " nlabels = max(len(key_labels), len(query_labels))\n", + " pos = range(nlabels)\n", + " \n", + " if 'self' in attn_name and col < ncols - 1:\n", + " query_labels = ['' for _ in query_labels]\n", + "\n", + " for ax, labels in [(ax1, key_labels), (ax2, query_labels)]:\n", + " ax.set_yticks(pos)\n", + " if has_chinese_label(labels):\n", + " ax.set_yticklabels(labels, fontproperties=zhfont)\n", + " else:\n", + " ax.set_yticklabels(labels)\n", + " ax.set_ylim([nlabels - 1, 0])\n", + " ax.tick_params(width=0, labelsize='xx-large')\n", + "\n", + " for spine in ax.spines.values():\n", + " spine.set_visible(False)\n", + "\n", + "# mask, attn = filter_attn(attn)\n", + " for qi in range(attn.size(0)):\n", + "# if not mask[qi]:\n", + "# continue\n", + "# for ki in range(attn.size(1)):\n", + " for ki in attn[qi].topk(vis_attn_topk)[1]:\n", + " a = attn[qi, ki]\n", + " ax1.plot((-1, 1), (ki, qi), color, alpha=a)\n", + "# print(attn.mean(dim=0).topk(5)[0])\n", + "# ax1.barh(pos, attn.mean(dim=0).data.cpu().numpy())\n", + "\n", + "def plot_layer_attn(result_tuple, attn_name='dec_self_attns', layer=0, heads=None):\n", + " hypo, nheads, labels_dict = result_tuple\n", + " key_labels, query_labels = labels_dict[attn_name]\n", + " if heads is None:\n", + " heads = range(nheads)\n", + " else:\n", + " nheads = len(heads)\n", + " \n", + " stride = 2 if attn_name == 'dec_enc_attns' else 1\n", + " nlabels = max(len(key_labels), len(query_labels))\n", + " rcParams['figure.figsize'] = 20, int(round(nlabels * stride * nheads / 8 * 1.0))\n", + " \n", + " rows = nheads // ncols * stride\n", + " fig, axes = plt.subplots(rows, ncols)\n", + " \n", + " # for head in range(nheads):\n", + " for head_i, head in enumerate(heads):\n", + " row, col = head_i * stride // ncols, head_i * stride % ncols\n", + " ax1 = axes[row, col]\n", + " attn = hypo[attn_name][layer][head]\n", + " _plot_attn(ax1, attn_name, attn, key_labels, query_labels, col)\n", + " if attn_name == 'dec_enc_attns':\n", + " col = col + 1\n", + " axes[row, col].axis('off') # next subfig acts as blank place holder\n", + " # plt.suptitle('%s with %d heads, Layer %d' % (attn_name, nheads, layer), fontsize=20)\n", + " plt.show() \n", + " \n", + "ncols = 4" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{\n", + " \"attention_probs_dropout_prob\": 0.1,\n", + " \"hidden_act\": \"gelu\",\n", + " \"hidden_dropout_prob\": 0.1,\n", + " \"hidden_size\": 768,\n", + " \"initializer_range\": 0.02,\n", + " \"intermediate_size\": 3072,\n", + " \"max_position_embeddings\": 512,\n", + " \"num_attention_heads\": 12,\n", + " \"num_hidden_layers\": 12,\n", + " \"type_vocab_size\": 2,\n", + " \"vocab_size\": 30522\n", + "}" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config.num" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Untitled_zeoliao.ipynb b/Untitled_zeoliao.ipynb new file mode 100644 index 000000000000..4f63f139e11e --- /dev/null +++ b/Untitled_zeoliao.ipynb @@ -0,0 +1,1174 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from IPython.core.interactiveshell import InteractiveShell\n", + "InteractiveShell.ast_node_interactivity = 'all'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex.\n" + ] + } + ], + "source": [ + "import os\n", + "import json\n", + "import itertools\n", + "from itertools import product, chain\n", + "\n", + "from pytorch_pretrained_bert import tokenization, BertTokenizer, BertModel, BertForMaskedLM, BertForPreTraining, BertConfig" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "CONFIG_NAME = 'bert_config.json'\n", + "BERT_DIR = '/nas/pretrain-bert/pretrain-tensorflow/uncased_L-12_H-768_A-12/'\n", + "tokenizer = BertTokenizer.from_pretrained(os.path.join(BERT_DIR, 'vocab.txt'))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def reverse(l):\n", + " return list(reversed(l))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def mask(ent_str):\n", + " tokens = ent_str.strip().split()\n", + " if len(tokens) == 1:\n", + " return '[%s]' % tokens[0]\n", + " elif len(tokens) == 2:\n", + " assert tokens[0] == 'the', ent_str\n", + " return '%s [%s]' % (tokens[0], tokens[1])\n", + " else:\n", + " assert False, ent_str" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "A_template = \"{dt} {ent0} {rel} {dt} {ent1} {rel_suffix}\"\n", + "B_template = \"{dt} {ent} {pred}\"\n", + "\n", + "causal_templates = [[\"{A} because {B}.\"],# \"{B} so {A}.\"], \n", + " [\"{A} so {B}.\"],# \"{B} because {A}.\"]\n", + " ]\n", + "turning_templates = [[\"{A} although {B}.\"],# \"{B} but {A}.\"], \n", + " [\"{A} but {B}.\"],# \"{B} although {A}.\"]\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def make_sentences(A_template, B_template, causal_templates, turning_templates,\n", + " index=-1, orig_sentence='', entities=[\"John\", \"Mary\"], entity_substitutes=None, determiner=\"\", \n", + " packed_relations=[\"rel/~rel\", \"rev_rel/~rev_rel\"], packed_relation_substitutes=None, relation_suffix=\"\",\n", + " packed_predicates=[\"pred0/~pred0\", \"pred1/~pred1\"], predicate_substitutes=None,\n", + " predicate_dichotomy=True, reverse_causal=False):\n", + " assert entities[0].lower() in tokenizer.vocab , entities[0]\n", + " assert entities[1].lower() in tokenizer.vocab , entities[1]\n", + " \n", + " relations, neg_relations = zip(*[rel.split(\"/\") for rel in packed_relations])\n", + " relations, neg_relations = list(relations), list(neg_relations)\n", + " predicates, neg_predicates = zip(*[pred.split(\"/\") for pred in packed_predicates])\n", + " predicates, neg_predicates = list(predicates), list(neg_predicates)\n", + " \n", + " As = [A_template.format(dt=determiner, ent0=ent0, ent1=ent1, rel=rel, rel_suffix=relation_suffix) \n", + " for ent0, ent1, rel in [entities + relations[:1], reverse(entities) + reverse(relations)[:1]]]\n", + " negAs = [A_template.format(dt=determiner, ent0=ent0, ent1=ent1, rel=rel, rel_suffix=relation_suffix) \n", + " for ent0, ent1, rel in [entities + neg_relations[:1], reverse(entities) + reverse(neg_relations)[:1]]]\n", + "\n", + " Bs = [B_template.format(dt=determiner, ent=mask(ent), pred=pred) for ent, pred in zip(entities, predicates)]\n", + " negBs = [B_template.format(dt=determiner, ent=mask(ent), pred=pred) for ent, pred in zip(entities, neg_predicates)]\n", + " if predicate_dichotomy:\n", + " Bs += [B_template.format(dt=determiner, ent=mask(ent), pred=pred) for ent, pred in zip(entities, reversed(neg_predicates))]\n", + " negBs += [B_template.format(dt=determiner, ent=mask(ent), pred=pred) for ent, pred in zip(entities, reversed(predicates))]\n", + "\n", + " def form_sentences(sentence_template, As, Bs):\n", + " return [\" \".join(sentence_template.format(A=A, B=B).split()) for A, B in product(As, Bs)]\n", + "\n", + " causal_sentences = []\n", + " for causal_template in causal_templates[int(reverse_causal)]:\n", + " for A, B in [(As, Bs), (negAs, negBs)]:\n", + " causal_sentences.extend(form_sentences(causal_template, A, B))\n", + "\n", + " turning_sentences = []\n", + " for turning_template in turning_templates[int(reverse_causal)]:\n", + " for A, B in [(As, negBs), (negAs, Bs)]:\n", + " turning_sentences.extend(form_sentences(turning_template, A, B))\n", + " \n", + " sentences = causal_sentences + turning_sentences\n", + " substituted_sentences = sentences\n", + " \n", + " if packed_relation_substitutes is not None:\n", + " packed_relation_substitutes = list(itertools.product(packed_relations[:1] + packed_relation_substitutes[0], \n", + " packed_relations[1:] + packed_relation_substitutes[1]))\n", + " substituted_sentences = []\n", + " for packed_sub_relations in packed_relation_substitutes:\n", + " sub_relations, sub_neg_relations = zip(*[rel.split(\"/\") for rel in packed_sub_relations])\n", + " substituted_sentences += [sent.replace(relations[0], sub_relations[0]).replace(relations[1], sub_relations[1])\n", + " .replace(neg_relations[0], sub_neg_relations[0]).replace(neg_relations[1], sub_neg_relations[1]) \n", + " for sent in sentences]\n", + " substituted_sentences = list(set(substituted_sentences))\n", + " \n", + " if entity_substitutes is not None:\n", + " for sub in entity_substitutes:\n", + " for ent in sub:\n", + " assert ent.lower() in tokenizer.vocab , ent + \" not in BERT vocab\"\n", + " assert len(set(chain.from_iterable(entity_substitutes))) == 4, entity_substitutes\n", + " assert len(set(chain.from_iterable(entity_substitutes)).union(set(entities))) == 6 \n", + " \n", + " entity_substitutes = list(itertools.product(entities[:1] + entity_substitutes[0], entities[1:] + entity_substitutes[1]))\n", + " substituted_sentences = [sent.replace(entities[0], sub[0]).replace(entities[1], sub[1]) \n", + " for sent in substituted_sentences for sub in entity_substitutes]\n", + " return causal_sentences, turning_sentences, substituted_sentences" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "frames = \\\n", + "[\n", + " {\n", + " \"index\": 2,\n", + " \"orig_sentence\": \"The trophy doesn't fit into the brown suitcase because [it] is too large/small.\",\n", + " \"entities\": [\"trophy\", \"suitcase\"],\n", + " \"entity_substitutes\": [[\"ball\", \"toy\"], [\"bag\", \"box\"]],\n", + " \"determiner\": \"the\",\n", + " \"packed_relations\": [\"doesn't fit into/can fit into\", \"doesn't hold/can hold\"],\n", + " \"packed_relation_substitutes\": [[\"can't be put into/can be put into\"], [\"doesn't have enough room for/has enough room for\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"is large/isn't large\", \"is small/isn't small\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": False\n", + " },\n", + " {\n", + " \"index\": 4,\n", + " \"orig_sentence\": \"Joan made sure to thank Susan for all the help [she] had recieved/given.\",\n", + " \"entities\": [\"John\", \"Susan\"],\n", + " \"entity_substitutes\": [[\"David\", \"Michael\"], [\"Mary\", \"Linda\"]],\n", + " \"determiner\": \"\",\n", + " \"packed_relations\": [\"thanked/didn't thank\", \"took good care of/didn't good care of\"],\n", + " \"packed_relation_substitutes\": [[\"felt grateful to/didn't feel grateful to\"], [\"was appreciated by/wasn't appreciated by\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"had received a lot of help/hadn't received a lot of help\", \"had given a lot of help/hadn't given a lot of help\"],\n", + " \"predicate_dichotomy\": False,\n", + " \"reverse_causal\": False\n", + " },\n", + " {\n", + " \"index\": 4000,\n", + " \"orig_sentence\": \"John gave a lot of money to Susan because [he] was very rich/poor.\",\n", + " \"entities\": [\"John\", \"Susan\"],\n", + " \"entity_substitutes\": [[\"David\", \"Michael\"], [\"Mary\", \"Linda\"]],\n", + " \"determiner\": \"\",\n", + " \"packed_relations\": [\"gave a lot of money to/didn't give a lot of money to\", \"received a lot of money from/didn't receive a lot of money from\"],\n", + " \"packed_relation_substitutes\": [[\"subsidized/didn't subsidize\"], [\"borrowed a lot of money from/didn't borrow any money from\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"was rich/wasn't rich\", \"was poor/wasn't poor\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": False\n", + " },\n", + " {\n", + " \"index\": 10,\n", + " \"orig_sentence\": \"The delivery truck zoomed by the school bus because [it] was going so fast/slow.\",\n", + " \"entities\": [\"truck\", \"bus\"],\n", + " \"entity_substitutes\": [[\"car\", \"ambulance\"], [\"bicycle\", \"tram\"]],\n", + " \"determiner\": \"the\",\n", + " \"packed_relations\": [\"overtook/couldn't overtake\", \"fell far behind/didn't fall far behind\"],\n", + " \"packed_relation_substitutes\": [[\"zoomed by/didn't pass\"], [\"was left behind/wasn't left far behind\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"was going fast/wasn't going fast\", \"was going slow/wasn't going slow\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": False\n", + " },\n", + " {\n", + " \"index\": 12,\n", + " \"orig_sentence\": \"Frank felt vindicated/crushed when his longtime rival Bill revealed that [he] was the winner of the competition.\",\n", + " \"entities\": [\"John\", \"Susan\"],\n", + " \"entity_substitutes\": [[\"David\", \"Michael\"], [\"Mary\", \"Linda\"]],\n", + " \"determiner\": \"\",\n", + " \"packed_relations\": [\"beat/didn't beat\", \"lost to/didn't lose to\"],\n", + " \"packed_relation_substitutes\": [[\"defeated/didn't defeat\"], [\"was defeated by/didn't be defeated by\"]],\n", + " \"relation_suffix\": \"in the game\",\n", + " \"packed_predicates\": [\"was happy/wasn't happy\", \"was sad/wasn't sad\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": True\n", + " },\n", + " {\n", + " \"index\": 16,\n", + " \"orig_sentence\": \"The large ball crashed right through the table because [it] was made of steel/styrofoam.\",\n", + " \"entities\": [\"ball\", \"board\"],\n", + " \"entity_substitutes\": [[\"bullet\", \"arrow\"], [\"shield\", \"disk\"]],\n", + " \"determiner\": \"the\",\n", + " \"packed_relations\": [\"crashed right through/didn't crash through\", \"failed to block/blocked\"],\n", + " \"packed_relation_substitutes\": [[\"penetrated through/didn't penetrate through\"], [\"failed to stop/stopped\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"was hard/wasn't hard\", \"was soft/wasn't soft\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": True\n", + " },\n", + " {\n", + " \"index\": 18,\n", + " \"orig_sentence\": \"John couldn't see the stage with Billy in front of him because [he] is so short.\",\n", + " \"entities\": [\"John\", \"Susan\"],\n", + " \"entity_substitutes\": [[\"David\", \"Edward\"], [\"Betty\", \"Donna\"]],\n", + " \"determiner\": \"\",\n", + " \"packed_relations\": [\"couldn't see the stage which behind/could see the stage which behind\", \"blocked the view of/couldn't block the view of\"],\n", + " \"packed_relation_substitutes\": [[\"couldn't find the stage which behind/could find the stage which behind\"], [\"obstructed the view of/couldn't obstruct the view of\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"is short/isn't short\", \"is tall/isn't tall\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": True\n", + " },\n", + " {\n", + " \"index\": 20,\n", + " \"orig_sentence\": \"Tom threw his schoolbag down to Ray after [he] reached the top of the stairs.\",\n", + " \"entities\": [\"Brian\", \"Amy\"],\n", + " \"entity_substitutes\": [[\"Charles\", \"Paul\"], [\"Emma\", \"Linda\"]],\n", + " \"determiner\": \"\",\n", + " \"packed_relations\": [\"through the schoolbag down to/through the schoolbag up to\", \"caught the schoolbag thrown down by/caught the schoolbag thrown up by\"],\n", + " \"packed_relation_substitutes\": [[\"cast the schoolbag down to/cast the schoolbag up to\"], [\"took the schoolbag thrown down by/took the schoolbag thrown up by\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"is on the top/isn't on the top\", \"is at the buttom/isn't at the buttom\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": True\n", + " },\n", + " {\n", + " \"index\": 22,\n", + " \"orig_sentence\": \"Although they ran at about the same speed, Sue beat Sally because [she] had such a good start.\",\n", + " \"entities\": [\"Tom\", \"Sue\"],\n", + " \"entity_substitutes\": [[\"John\", \"David\"], [\"Sally\", \"Susan\"]],\n", + " \"determiner\": \"\",\n", + " \"packed_relations\": [\"beat/didn't beat\", \"lost to/didn't lose to\"],\n", + " \"packed_relation_substitutes\": [[\"defeated/didn't defeat\"], [\"was defeated by/didn't be defeated by\"]],\n", + " \"relation_suffix\": \"in the running race\",\n", + " \"packed_predicates\": [\"has a good start/didn't has a good start\", \"has a bad start/didn't has a bad start\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": False\n", + " },\n", + " {\n", + " \"index\": 26000,\n", + " \"orig_sentence\": \"Sam's drawing was hung just above Tina's and [it] did look much better with another one below it\",\n", + " \"entities\": [\"Bob\", \"Wendy\"],\n", + " \"entity_substitutes\": [[\"Bush\", \"Tim\"], [\"Sandy\", \"Helen\"]],\n", + " \"determiner\": \"\",\n", + " \"packed_relations\": [\"could reach higher than/couldn't reach higher than\", \"reached lower than/didn't reach lower than\"],\n", + " \"packed_relation_substitutes\": [[\"could jump higher than/couldn't jump higher than\"], [\"jumped lower than/didn't jump lower than\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"is tall/is not tall\", \"is short/is not short\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": False\n", + " },\n", + " {\n", + " \"index\": 28,\n", + " \"orig_sentence\": \"Anna did a lot better than her good friend Lucy on the test because [she] had studied so hard.\",\n", + " \"entities\": [\"Anna\", \"Andy\"],\n", + " \"entity_substitutes\": [[\"Lucy\", \"Nancy\"], [\"George\", \"Frank\"]],\n", + " \"determiner\": \"\",\n", + " \"packed_relations\": [\"did better than her good friend/didn't do better than her good friend\", \"did worse than her good friend/didn't do worse than her good friend\"],\n", + " \"packed_relation_substitutes\": [[\"performed better than her good friend/didn't perform better than her good friend\"], [\"performed worse than her good friend/didn't perform worse than her good friend\"]],\n", + " \"relation_suffix\": \"on the test\",\n", + " \"packed_predicates\": [\"had studied hard./hadn't studied hard.\", \"had studied less./hadn't studied less\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": False\n", + " },\n", + " {\n", + " \"index\": 30,\n", + " \"orig_sentence\": \"The firemen arrived after the police because [they] were coming from so far away.\",\n", + " \"entities\": [\"firemen\", \"police\"],\n", + " \"entity_substitutes\": [[\"worker\", \"employee\"], [\"boss\", \"administer\"]],\n", + " \"determiner\": \"the\",\n", + " \"packed_relations\": [\"arrived after/didn't arrive after\", \"arrived before/didn't arrive before\"],\n", + " \"packed_relation_substitutes\": [[\"reached here after/didn't reach here after\"], [\"reached here before/didn't reach here before\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"came from the farther place./didn't come from the farther place.\", \"came from the closer place./didn't come from the closer place.\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": True\n", + " },\n", + " {\n", + " \"index\": 32000,\n", + " \"orig_sentence\": \"Frank was upset with Tom because the toaster [he] had bought from him didn't work.\",\n", + " \"entities\": [\"Betty\", \"Henry\"],\n", + " \"entity_substitutes\": [[\"Amy\", \"Linda\"], [\"Bush\", \"Frank\"]],\n", + " \"determiner\": \"\",\n", + " \"packed_relations\": [\"was hate by/wasn't hate by\", \"was liked by/wasn't liked by\"],\n", + " \"packed_relation_substitutes\": [[\"was dislike by/wasn't dislike by\"], [\"was appreciated by/wasn't appreciate by\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"broken down the toaster./didn't broke down the toaster.\", \"repaired the toaster./didn't repaire the toaster.\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": True\n", + " },\n", + " {\n", + " \"index\": 36,\n", + " \"orig_sentence\": \"The sack of potatoes had been placed above the bag of flour, so [it] had to be moved first.\",\n", + " \"entities\": [\"potatoes\", \"Henry\"],\n", + " \"entity_substitutes\": [[\"Amy\", \"Linda\"], [\"Bush\", \"Frank\"]],\n", + " \"determiner\": \"\",\n", + " \"packed_relations\": [\"was hate by/wasn't hate by\", \"was liked by/wasn't liked by\"],\n", + " \"packed_relation_substitutes\": [[\"was dislike by/wasn't dislike by\"], [\"was appreciated by/wasn't appreciate by\"]],\n", + " \"relation_suffix\": \"\",\n", + " \"packed_predicates\": [\"broken down the toaster./didn't broke down the toaster.\", \"repaired the toaster./didn't repaire the toaster.\"],\n", + " \"predicate_dichotomy\": True,\n", + " \"reverse_causal\": True\n", + " },\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "causal_sentences, turning_sentences, substituted_sentences = \\\n", + " make_sentences(A_template, B_template, causal_templates, turning_templates, **frames[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['potatoes was hate by Henry so [potatoes] broken down the toaster..',\n", + " 'potatoes was hate by Henry so [Henry] repaired the toaster..',\n", + " \"potatoes was hate by Henry so [potatoes] didn't repaire the toaster..\",\n", + " \"potatoes was hate by Henry so [Henry] didn't broke down the toaster..\",\n", + " 'Henry was liked by potatoes so [potatoes] 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"output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "[\"potatoes was hate by Henry but [potatoes] didn't broke down the toaster..\",\n", + " \"potatoes was hate by Henry but [Henry] didn't repaire the toaster..\",\n", + " 'potatoes was hate by Henry but [potatoes] repaired the toaster..',\n", + " 'potatoes was hate by Henry but [Henry] broken down the toaster..',\n", + " \"Henry was liked by potatoes but [potatoes] didn't broke down the toaster..\",\n", + " \"Henry was liked by potatoes but [Henry] didn't repaire the toaster..\",\n", + " 'Henry was liked by potatoes but [potatoes] repaired the toaster..',\n", + " 'Henry was liked by potatoes but [Henry] broken down the toaster..',\n", + " \"potatoes wasn't hate by Henry but [potatoes] broken down the toaster..\",\n", + " \"potatoes wasn't hate by Henry but [Henry] repaired the toaster..\",\n", + " \"potatoes wasn't hate by Henry but [potatoes] didn't repaire the toaster..\",\n", + " \"potatoes wasn't hate by Henry 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wasn't appreciate by Amy but [Amy] didn't repaire the toaster..\",\n", + " \"Bush wasn't appreciate by Amy but [Amy] didn't repaire the toaster..\",\n", + " \"Frank wasn't appreciate by Amy but [Amy] didn't repaire the toaster..\",\n", + " \"Henry wasn't appreciate by Linda but [Linda] didn't repaire the toaster..\",\n", + " \"Bush wasn't appreciate by Linda but [Linda] didn't repaire the toaster..\",\n", + " \"Frank wasn't appreciate by Linda but [Linda] didn't repaire the toaster..\",\n", + " \"potatoes wasn't hate by Henry so [potatoes] didn't broke down the toaster..\",\n", + " \"potatoes wasn't hate by Bush so [potatoes] didn't broke down the toaster..\",\n", + " \"potatoes wasn't hate by Frank so [potatoes] didn't broke down the toaster..\",\n", + " \"Amy wasn't hate by Henry so [Amy] didn't broke down the toaster..\",\n", + " \"Amy wasn't hate by Bush so [Amy] didn't broke down the toaster..\",\n", + " \"Amy wasn't hate by Frank so [Amy] didn't broke down the toaster..\",\n", + " \"Linda wasn't hate by Henry so [Linda] didn't broke down the toaster..\",\n", + " \"Linda wasn't hate by Bush so [Linda] didn't broke down the toaster..\",\n", + " \"Linda wasn't hate by Frank so [Linda] didn't broke down the toaster..\",\n", + " \"potatoes was dislike by Henry but [potatoes] didn't broke down the toaster..\",\n", + " \"potatoes was dislike by Bush but [potatoes] didn't broke down the toaster..\",\n", + " \"potatoes was dislike by Frank but [potatoes] didn't broke down the toaster..\",\n", + " \"Amy was dislike by Henry but [Amy] didn't broke down the toaster..\",\n", + " \"Amy was dislike by Bush but [Amy] didn't broke down the toaster..\",\n", + " \"Amy was dislike by Frank but [Amy] didn't broke down the toaster..\",\n", + " \"Linda was dislike by Henry but [Linda] didn't broke down the toaster..\",\n", + " \"Linda was dislike by Bush but [Linda] didn't broke down the toaster..\",\n", + " \"Linda was dislike by Frank but [Linda] didn't broke down the toaster..\",\n", + " 'Henry was liked by potatoes so [Henry] repaired the toaster..',\n", + " 'Bush was liked by potatoes so [Bush] repaired the toaster..',\n", + " 'Frank was liked by potatoes so [Frank] repaired the toaster..',\n", + " 'Henry was liked by Amy so [Henry] repaired the toaster..',\n", + " 'Bush was liked by Amy so [Bush] repaired the toaster..',\n", + " 'Frank was liked by Amy so [Frank] repaired the toaster..',\n", + " 'Henry was liked by Linda so [Henry] repaired the toaster..',\n", + " 'Bush was liked by Linda so [Bush] repaired the toaster..',\n", + " 'Frank was liked by Linda so [Frank] repaired the toaster..',\n", + " 'potatoes was hate by Henry but [Henry] broken down the toaster..',\n", + " 'potatoes was hate by Bush but [Bush] broken down the toaster..',\n", + " 'potatoes was hate by Frank but [Frank] broken down the toaster..',\n", + " 'Amy was hate by Henry but [Henry] broken down the toaster..',\n", + " 'Amy was hate by Bush but [Bush] broken down the toaster..',\n", + " 'Amy was hate by Frank but [Frank] broken down the toaster..',\n", + " 'Linda was hate by Henry but [Henry] broken down the toaster..',\n", + " 'Linda was hate by Bush but [Bush] broken down the toaster..',\n", + " 'Linda was hate by Frank but [Frank] broken down the toaster..',\n", + " \"potatoes was dislike by Henry so [potatoes] didn't repaire the toaster..\",\n", + " \"potatoes was dislike by Bush so [potatoes] didn't repaire the toaster..\",\n", + " \"potatoes was dislike by Frank so [potatoes] didn't repaire the toaster..\",\n", + " \"Amy was dislike by Henry so [Amy] didn't repaire the toaster..\",\n", + " \"Amy was dislike by Bush so [Amy] didn't repaire the toaster..\",\n", + " \"Amy was dislike by Frank so [Amy] didn't repaire the toaster..\",\n", + " \"Linda was dislike by Henry so [Linda] didn't repaire the toaster..\",\n", + " \"Linda was dislike by Bush so [Linda] didn't repaire the toaster..\",\n", + " \"Linda was dislike by Frank so [Linda] didn't repaire the toaster..\",\n", + " 'Henry was appreciated by potatoes but [Henry] broken down the toaster..',\n", + " 'Bush was appreciated by potatoes but [Bush] broken down the toaster..',\n", + " 'Frank was appreciated by potatoes but [Frank] broken down the toaster..',\n", + " 'Henry was appreciated by Amy but [Henry] broken down the toaster..',\n", + " 'Bush was appreciated by Amy but [Bush] broken down the toaster..',\n", + " 'Frank was appreciated by Amy but [Frank] broken down the toaster..',\n", + " 'Henry was appreciated by Linda but [Henry] broken down the toaster..',\n", + " 'Bush was appreciated by Linda but [Bush] broken down the toaster..',\n", + " 'Frank was appreciated by Linda but [Frank] broken down the toaster..',\n", + " \"potatoes wasn't dislike by Henry but [Henry] repaired the toaster..\",\n", + " \"potatoes wasn't dislike by Bush but [Bush] repaired the toaster..\",\n", + " \"potatoes wasn't dislike by Frank but [Frank] repaired the toaster..\",\n", + " \"Amy wasn't dislike by Henry but [Henry] repaired the toaster..\",\n", + " \"Amy wasn't dislike by Bush but [Bush] repaired the toaster..\",\n", + " \"Amy wasn't dislike by Frank but [Frank] repaired the toaster..\",\n", + " \"Linda wasn't dislike by Henry but [Henry] repaired the toaster..\",\n", + " \"Linda wasn't dislike by Bush but [Bush] repaired the toaster..\",\n", + " \"Linda wasn't dislike by Frank but [Frank] repaired the toaster..\",\n", + " 'potatoes was dislike by Henry but [Henry] broken down the toaster..',\n", + " 'potatoes was dislike by Bush but [Bush] broken down the toaster..',\n", + " 'potatoes was dislike by Frank but [Frank] broken down the toaster..',\n", + " 'Amy was dislike by Henry but [Henry] broken down the toaster..',\n", + " 'Amy was dislike by Bush but [Bush] broken down the toaster..',\n", + " 'Amy was dislike by Frank but [Frank] broken down the toaster..',\n", + " 'Linda was dislike by Henry but [Henry] broken down the toaster..',\n", + " 'Linda was dislike by Bush but [Bush] broken down the toaster..',\n", + " 'Linda was dislike by Frank but [Frank] broken down the toaster..',\n", + " \"Henry wasn't appreciate by potatoes so [Henry] didn't repaire the toaster..\",\n", + " \"Bush wasn't appreciate by potatoes so [Bush] didn't repaire the toaster..\",\n", + " \"Frank wasn't appreciate by potatoes so [Frank] didn't repaire the toaster..\",\n", + " \"Henry wasn't appreciate by Amy so [Henry] didn't repaire the toaster..\",\n", + " \"Bush wasn't appreciate by Amy so [Bush] didn't repaire the toaster..\",\n", + " \"Frank wasn't appreciate by Amy so [Frank] didn't repaire the toaster..\",\n", + " \"Henry wasn't appreciate by Linda so [Henry] didn't repaire the toaster..\",\n", + " \"Bush wasn't appreciate by Linda so [Bush] didn't repaire the toaster..\",\n", + " \"Frank wasn't appreciate by Linda so [Frank] didn't repaire the toaster..\",\n", + " \"Henry was liked by potatoes but [Henry] didn't repaire the toaster..\",\n", + " \"Bush was liked by potatoes but [Bush] didn't repaire the toaster..\",\n", + " \"Frank was liked by potatoes but [Frank] didn't repaire the toaster..\",\n", + " \"Henry was liked by Amy but [Henry] didn't repaire the toaster..\",\n", + " \"Bush was liked by Amy but [Bush] didn't repaire the toaster..\",\n", + " \"Frank was liked by Amy but [Frank] didn't repaire the toaster..\",\n", + " \"Henry was liked by Linda but [Henry] didn't repaire the toaster..\",\n", + " \"Bush was liked by Linda but [Bush] didn't repaire the toaster..\",\n", + " \"Frank was liked by Linda but [Frank] didn't repaire the toaster..\",\n", + " \"potatoes wasn't dislike by Henry so [potatoes] didn't broke down the toaster..\",\n", + " \"potatoes wasn't dislike by Bush so [potatoes] didn't broke down the toaster..\",\n", + " \"potatoes wasn't dislike by Frank so [potatoes] didn't broke down the toaster..\",\n", + " \"Amy wasn't dislike by Henry so [Amy] didn't broke down the toaster..\",\n", + " \"Amy wasn't dislike by Bush so [Amy] didn't broke down the toaster..\",\n", + " \"Amy wasn't dislike by Frank so [Amy] didn't broke down the toaster..\",\n", + " \"Linda wasn't dislike by Henry so [Linda] didn't broke down the toaster..\",\n", + " \"Linda wasn't dislike by Bush so [Linda] didn't broke down the toaster..\",\n", + " \"Linda wasn't dislike by Frank so [Linda] didn't broke down the toaster..\",\n", + " 'Henry was liked by potatoes but [Henry] broken down the toaster..',\n", + " 'Bush was liked by potatoes but [Bush] broken down the toaster..',\n", + " 'Frank was liked by potatoes but [Frank] broken down the toaster..',\n", + " 'Henry was liked by Amy but [Henry] broken down the toaster..',\n", + " 'Bush was liked by Amy but [Bush] broken down the toaster..',\n", + " 'Frank was liked by Amy but [Frank] broken down the toaster..',\n", + " 'Henry was liked by Linda but [Henry] broken down the toaster..',\n", + " 'Bush was liked by Linda but [Bush] broken down the toaster..',\n", + " 'Frank was liked by Linda but [Frank] broken down the toaster..',\n", + " \"Henry wasn't liked by potatoes but [Henry] didn't broke down the toaster..\",\n", + " \"Bush wasn't liked by potatoes but [Bush] didn't broke down the toaster..\",\n", + " \"Frank wasn't liked by potatoes but [Frank] didn't broke down the toaster..\",\n", + " \"Henry wasn't liked by Amy but [Henry] didn't broke down the toaster..\",\n", + " \"Bush wasn't liked by Amy but [Bush] didn't broke down the toaster..\",\n", + " \"Frank wasn't liked by Amy but [Frank] didn't broke down the toaster..\",\n", + " \"Henry wasn't liked by Linda but [Henry] didn't broke down the toaster..\",\n", + " \"Bush wasn't liked by Linda but [Bush] didn't broke down the toaster..\",\n", + " \"Frank wasn't liked by Linda but [Frank] didn't broke down the toaster..\",\n", + " \"Henry was liked by potatoes so [potatoes] didn't repaire the toaster..\",\n", + " \"Bush was liked by potatoes so [potatoes] didn't repaire the toaster..\",\n", + " \"Frank was liked by potatoes so [potatoes] didn't repaire the toaster..\",\n", + " \"Henry was liked by Amy so [Amy] didn't repaire the toaster..\",\n", + " \"Bush was liked by Amy so [Amy] didn't repaire the toaster..\",\n", + " \"Frank was liked by Amy so [Amy] didn't repaire the toaster..\",\n", + " \"Henry was liked by Linda so [Linda] didn't repaire the toaster..\",\n", + " \"Bush was liked by Linda so [Linda] didn't repaire the toaster..\",\n", + " \"Frank was liked by Linda so [Linda] didn't repaire the toaster..\",\n", + " \"Henry wasn't liked by potatoes so [potatoes] repaired the toaster..\",\n", + " \"Bush wasn't liked by potatoes so [potatoes] repaired the toaster..\",\n", + " \"Frank wasn't liked by potatoes so [potatoes] repaired the toaster..\",\n", + " \"Henry wasn't liked by Amy so [Amy] repaired the toaster..\",\n", + " \"Bush wasn't liked by Amy so [Amy] repaired the toaster..\",\n", + " \"Frank wasn't liked by Amy so [Amy] repaired the toaster..\",\n", + " \"Henry wasn't liked by Linda so [Linda] repaired the toaster..\",\n", + " \"Bush wasn't liked by Linda so [Linda] repaired the toaster..\",\n", + " \"Frank wasn't liked by Linda so [Linda] repaired the toaster..\",\n", + " \"potatoes wasn't dislike by Henry so [Henry] didn't repaire the toaster..\",\n", + " \"potatoes wasn't dislike by Bush so [Bush] didn't repaire the toaster..\",\n", + " \"potatoes wasn't dislike by Frank so [Frank] didn't repaire the toaster..\",\n", + " \"Amy wasn't dislike by Henry so [Henry] didn't repaire the toaster..\",\n", + " \"Amy wasn't dislike by Bush so [Bush] didn't repaire the toaster..\",\n", + " \"Amy wasn't dislike by Frank so [Frank] didn't repaire the toaster..\",\n", + " \"Linda wasn't dislike by Henry so [Henry] didn't repaire the toaster..\",\n", + " \"Linda wasn't dislike by Bush so [Bush] didn't repaire the toaster..\",\n", + " \"Linda wasn't dislike by Frank so [Frank] didn't repaire the toaster..\",\n", + " \"Henry wasn't appreciate by potatoes so [potatoes] didn't broke down the toaster..\",\n", + " \"Bush wasn't appreciate by potatoes so [potatoes] didn't broke down the toaster..\",\n", + " \"Frank wasn't appreciate by potatoes so [potatoes] didn't broke down the toaster..\",\n", + " \"Henry wasn't appreciate by Amy so [Amy] didn't broke down the toaster..\",\n", + " \"Bush wasn't appreciate by Amy so [Amy] didn't broke down the toaster..\",\n", + " \"Frank wasn't appreciate by Amy so [Amy] didn't broke down the toaster..\",\n", + " \"Henry wasn't appreciate by Linda so [Linda] didn't broke down the toaster..\",\n", + " \"Bush wasn't appreciate by Linda so [Linda] didn't broke down the toaster..\",\n", + " \"Frank wasn't appreciate by Linda so [Linda] didn't broke down the toaster..\",\n", + " \"Henry wasn't appreciate by potatoes but [potatoes] broken down the toaster..\",\n", + " \"Bush wasn't appreciate by potatoes but [potatoes] broken down the toaster..\",\n", + " \"Frank wasn't appreciate by potatoes but [potatoes] broken down the toaster..\",\n", + " \"Henry wasn't appreciate by Amy but [Amy] broken down the toaster..\",\n", + " \"Bush wasn't appreciate by Amy but [Amy] broken down the toaster..\",\n", + " \"Frank wasn't appreciate by Amy but [Amy] broken down the toaster..\",\n", + " \"Henry wasn't appreciate by Linda but [Linda] broken down the toaster..\",\n", + " \"Bush wasn't appreciate by Linda but [Linda] broken down the toaster..\",\n", + " \"Frank wasn't appreciate by Linda but [Linda] broken down the toaster..\",\n", + " \"Henry was liked by potatoes but [potatoes] didn't broke down the toaster..\",\n", + " \"Bush was liked by potatoes but [potatoes] didn't broke down the toaster..\",\n", + " \"Frank was liked by potatoes but [potatoes] didn't broke down the toaster..\",\n", + " \"Henry was liked by Amy but [Amy] didn't broke down the toaster..\",\n", + " \"Bush was liked by Amy but [Amy] didn't broke down the toaster..\",\n", + " \"Frank was liked by Amy but [Amy] didn't broke down the toaster..\",\n", + " \"Henry was liked by Linda but [Linda] didn't broke down the toaster..\",\n", + " \"Bush was liked by Linda but [Linda] didn't broke down the toaster..\",\n", + " \"Frank was liked by Linda but [Linda] didn't broke down the toaster..\",\n", + " \"potatoes was hate by Henry so [Henry] didn't broke down the toaster..\",\n", + " \"potatoes was hate by Bush so [Bush] didn't broke down the toaster..\",\n", + " \"potatoes was hate by Frank so [Frank] didn't broke down the toaster..\",\n", + " \"Amy was hate by Henry so [Henry] didn't broke down the toaster..\",\n", + " \"Amy was hate by Bush so [Bush] didn't broke down the toaster..\",\n", + " \"Amy was hate by Frank so [Frank] didn't broke down the toaster..\",\n", + " \"Linda was hate by Henry so [Henry] didn't broke down the toaster..\",\n", + " \"Linda was hate by Bush so [Bush] didn't broke down the toaster..\",\n", + " \"Linda was hate by Frank so [Frank] didn't broke down the toaster..\",\n", + " \"Henry wasn't liked by potatoes but [Henry] repaired the toaster..\",\n", + " \"Bush wasn't liked by potatoes but [Bush] repaired the toaster..\",\n", + " \"Frank wasn't liked by potatoes but [Frank] repaired the toaster..\",\n", + " \"Henry wasn't liked by Amy but [Henry] repaired the toaster..\",\n", + " \"Bush wasn't liked by Amy but [Bush] repaired the toaster..\",\n", + " \"Frank wasn't liked by Amy but [Frank] repaired the toaster..\",\n", + " \"Henry wasn't liked by Linda but [Henry] repaired the toaster..\",\n", + " \"Bush wasn't liked by Linda but [Bush] repaired the toaster..\",\n", + " \"Frank wasn't liked by Linda but [Frank] repaired the toaster..\",\n", + " \"potatoes was hate by Henry but [potatoes] didn't broke down the toaster..\",\n", + " \"potatoes was hate by Bush but [potatoes] didn't broke down the toaster..\",\n", + " \"potatoes was hate by Frank but [potatoes] didn't broke down the toaster..\",\n", + " \"Amy was hate by Henry but [Amy] didn't broke down the toaster..\",\n", + " \"Amy was hate by Bush but [Amy] didn't broke down the toaster..\",\n", + " \"Amy was hate by Frank but [Amy] didn't broke down the toaster..\",\n", + " \"Linda was hate by Henry but [Linda] didn't broke down the toaster..\",\n", + " \"Linda was hate by Bush but [Linda] didn't broke down the toaster..\",\n", + " \"Linda was hate by Frank but [Linda] didn't broke down the toaster..\",\n", + " \"potatoes was dislike by Henry but [Henry] didn't repaire the toaster..\",\n", + " \"potatoes was dislike by Bush but [Bush] didn't repaire the toaster..\",\n", + " \"potatoes was dislike by Frank but [Frank] didn't repaire the toaster..\",\n", + " \"Amy was dislike by Henry but [Henry] didn't repaire the toaster..\",\n", + " \"Amy was dislike by Bush but [Bush] didn't repaire the toaster..\",\n", + " \"Amy was dislike by Frank but [Frank] didn't repaire the toaster..\",\n", + " \"Linda was dislike by Henry but [Henry] didn't repaire the toaster..\",\n", + " \"Linda was dislike by Bush but [Bush] didn't repaire the toaster..\",\n", + " \"Linda was dislike by Frank but [Frank] didn't repaire the toaster..\",\n", + " \"Henry wasn't liked by potatoes so [Henry] didn't repaire the toaster..\",\n", + " \"Bush wasn't liked by potatoes so [Bush] didn't repaire the toaster..\",\n", + " \"Frank wasn't liked by potatoes so [Frank] didn't repaire the toaster..\",\n", + " \"Henry wasn't liked by Amy so [Henry] didn't repaire the toaster..\",\n", + " \"Bush wasn't liked by Amy so [Bush] didn't repaire the toaster..\",\n", + " \"Frank wasn't liked by Amy so [Frank] didn't repaire the toaster..\",\n", + " \"Henry wasn't liked by Linda so [Henry] didn't repaire the toaster..\",\n", + " \"Bush wasn't liked by Linda so [Bush] didn't repaire the toaster..\",\n", + " \"Frank wasn't liked by Linda so [Frank] didn't repaire the toaster..\",\n", + " \"Henry wasn't appreciate by potatoes but [Henry] repaired the toaster..\",\n", + " \"Bush wasn't appreciate by potatoes but [Bush] repaired the toaster..\",\n", + " \"Frank wasn't appreciate by potatoes but [Frank] repaired the toaster..\",\n", + " \"Henry wasn't appreciate by Amy but [Henry] repaired the toaster..\",\n", + " \"Bush wasn't appreciate by Amy but [Bush] repaired the toaster..\",\n", + " \"Frank wasn't appreciate by Amy but [Frank] repaired the toaster..\",\n", + " \"Henry wasn't appreciate by Linda but [Henry] repaired the toaster..\",\n", + " \"Bush wasn't appreciate by Linda but [Bush] repaired the toaster..\",\n", + " \"Frank wasn't appreciate by Linda but [Frank] repaired the toaster..\",\n", + " \"potatoes wasn't dislike by Henry so [potatoes] repaired the toaster..\",\n", + " \"potatoes wasn't dislike by Bush so [potatoes] repaired the toaster..\",\n", + " \"potatoes wasn't dislike by Frank so [potatoes] repaired the toaster..\",\n", + " \"Amy wasn't dislike by Henry so [Amy] repaired the toaster..\",\n", + " \"Amy wasn't dislike by Bush so [Amy] repaired the toaster..\",\n", + " \"Amy wasn't dislike by Frank so [Amy] repaired the toaster..\",\n", + " \"Linda wasn't dislike by Henry so [Linda] repaired the toaster..\",\n", + " \"Linda wasn't dislike by Bush so [Linda] repaired the toaster..\",\n", + " \"Linda wasn't dislike by Frank so [Linda] repaired the toaster..\",\n", + " \"potatoes wasn't hate by Henry so [potatoes] repaired the toaster..\",\n", + " \"potatoes wasn't hate by Bush so [potatoes] repaired the toaster..\",\n", + " \"potatoes wasn't hate by Frank so [potatoes] repaired the toaster..\",\n", + " \"Amy wasn't hate by Henry so [Amy] repaired the toaster..\",\n", + " \"Amy wasn't hate by Bush so [Amy] repaired the toaster..\",\n", + " \"Amy wasn't hate by Frank so [Amy] repaired the toaster..\",\n", + " \"Linda wasn't hate by Henry so [Linda] repaired the toaster..\",\n", + " \"Linda wasn't hate by Bush so [Linda] repaired the toaster..\",\n", + " \"Linda wasn't hate by Frank so [Linda] repaired the toaster..\",\n", + " 'potatoes was dislike by Henry so [potatoes] broken down the toaster..',\n", + " 'potatoes was dislike by Bush so [potatoes] broken down the toaster..',\n", + " 'potatoes was dislike by Frank so [potatoes] broken down the toaster..',\n", + " 'Amy was dislike by Henry so [Amy] broken down the toaster..',\n", + " 'Amy was dislike by Bush so [Amy] broken down the toaster..',\n", + " 'Amy was dislike by Frank so [Amy] broken down the toaster..',\n", + " 'Linda was dislike by Henry so [Linda] broken down the toaster..',\n", + " 'Linda was dislike by Bush so [Linda] broken down the toaster..',\n", + " 'Linda was dislike by Frank so [Linda] broken down the toaster..',\n", + " \"potatoes wasn't hate by Henry but [Henry] didn't broke down the toaster..\",\n", + " \"potatoes wasn't hate by Bush but [Bush] didn't broke down the toaster..\",\n", + " \"potatoes wasn't hate by Frank but [Frank] didn't broke down the toaster..\",\n", + " \"Amy wasn't hate by Henry but [Henry] didn't broke down the toaster..\",\n", + " \"Amy wasn't hate by Bush but [Bush] didn't broke down the toaster..\",\n", + " \"Amy wasn't hate by Frank but [Frank] didn't broke down the toaster..\",\n", + " \"Linda wasn't hate by Henry but [Henry] didn't broke down the toaster..\",\n", + " \"Linda wasn't hate by Bush but [Bush] didn't broke down the toaster..\",\n", + " \"Linda wasn't hate by Frank but [Frank] didn't broke down the toaster..\",\n", + " \"Henry wasn't appreciate by potatoes but [Henry] didn't broke down the toaster..\",\n", + " \"Bush wasn't appreciate by potatoes but [Bush] didn't broke down the toaster..\",\n", + " \"Frank wasn't appreciate by potatoes but [Frank] didn't broke down the toaster..\",\n", + " \"Henry wasn't appreciate by Amy but [Henry] didn't broke down the toaster..\",\n", + " \"Bush wasn't appreciate by Amy but [Bush] didn't broke down the toaster..\",\n", + " \"Frank wasn't appreciate by Amy but [Frank] didn't broke down the toaster..\",\n", + " \"Henry wasn't appreciate by Linda but [Henry] didn't broke down the toaster..\",\n", + " \"Bush wasn't appreciate by Linda but [Bush] didn't broke down the toaster..\",\n", + " \"Frank wasn't appreciate by Linda but [Frank] didn't broke down the toaster..\",\n", + " 'Henry was appreciated by potatoes so [Henry] repaired the toaster..',\n", + " 'Bush was appreciated by potatoes so [Bush] repaired the toaster..',\n", + " 'Frank was appreciated by potatoes so [Frank] repaired the toaster..',\n", + " 'Henry was appreciated by Amy so [Henry] repaired the toaster..',\n", + " 'Bush was appreciated by Amy so [Bush] repaired the toaster..',\n", + " 'Frank was appreciated by Amy so [Frank] repaired the toaster..',\n", + " 'Henry was appreciated by Linda so [Henry] repaired the toaster..',\n", + " 'Bush was appreciated by Linda so [Bush] repaired the toaster..',\n", + " 'Frank was appreciated by Linda so [Frank] repaired the toaster..',\n", + " 'potatoes was hate by Henry but [potatoes] repaired the toaster..',\n", + " 'potatoes was hate by Bush but [potatoes] repaired the toaster..',\n", + " 'potatoes was hate by Frank but [potatoes] repaired the toaster..',\n", + " 'Amy was hate by Henry but [Amy] repaired the toaster..',\n", + " 'Amy was hate by Bush but [Amy] repaired the toaster..',\n", + " 'Amy was hate by Frank but [Amy] repaired the toaster..',\n", + " 'Linda was hate by Henry but [Linda] repaired the toaster..',\n", + " 'Linda was hate by Bush but [Linda] repaired the toaster..',\n", + " 'Linda was hate by Frank but [Linda] repaired the toaster..',\n", + " \"potatoes wasn't dislike by Henry but [Henry] didn't broke down the toaster..\",\n", + " \"potatoes wasn't dislike by Bush but [Bush] didn't broke down the toaster..\",\n", + " \"potatoes wasn't dislike by Frank but [Frank] didn't broke down the toaster..\",\n", + " \"Amy wasn't dislike by Henry but [Henry] didn't broke down the toaster..\",\n", + " \"Amy wasn't dislike by Bush but [Bush] didn't broke down the toaster..\",\n", + " \"Amy wasn't dislike by Frank but [Frank] didn't broke down the toaster..\",\n", + " \"Linda wasn't dislike by Henry but [Henry] didn't broke down the toaster..\",\n", + " \"Linda wasn't dislike by Bush but [Bush] didn't broke down the toaster..\",\n", + " \"Linda wasn't dislike by Frank but [Frank] didn't broke down the toaster..\",\n", + " 'Henry was appreciated by potatoes but [potatoes] repaired the toaster..',\n", + " 'Bush was appreciated by potatoes but [potatoes] repaired the toaster..',\n", + " 'Frank was appreciated by potatoes but [potatoes] repaired the toaster..',\n", + " 'Henry was appreciated by Amy but [Amy] repaired the toaster..',\n", + " 'Bush was appreciated by Amy but [Amy] repaired the toaster..',\n", + " 'Frank was appreciated by Amy but [Amy] repaired the toaster..',\n", + " 'Henry was appreciated by Linda but [Linda] repaired the toaster..',\n", + " 'Bush was appreciated by Linda but [Linda] repaired the toaster..',\n", + " 'Frank was appreciated by Linda but [Linda] repaired the toaster..',\n", + " \"Henry was liked by potatoes so [Henry] didn't broke down the toaster..\",\n", + " \"Bush was liked by potatoes so [Bush] didn't broke down the toaster..\",\n", + " \"Frank was liked by potatoes so [Frank] didn't broke down the toaster..\",\n", + " \"Henry was liked by Amy so [Henry] didn't broke down the toaster..\",\n", + " \"Bush was liked by Amy so [Bush] didn't broke down the toaster..\",\n", + " \"Frank was liked by Amy so [Frank] didn't broke down the toaster..\",\n", + " \"Henry was liked by Linda so [Henry] didn't broke down the toaster..\",\n", + " \"Bush was liked by Linda so [Bush] didn't broke down the toaster..\",\n", + " \"Frank was liked by Linda so [Frank] didn't broke down the toaster..\",\n", + " 'Henry was liked by potatoes but [potatoes] repaired the toaster..',\n", + " 'Bush was liked by potatoes but [potatoes] repaired the toaster..',\n", + " 'Frank was liked by potatoes but [potatoes] repaired the toaster..',\n", + " 'Henry was liked by Amy but [Amy] repaired the toaster..',\n", + " 'Bush was liked by Amy but [Amy] repaired the toaster..',\n", + " 'Frank was liked by Amy but [Amy] repaired the toaster..',\n", + " 'Henry was liked by Linda but [Linda] repaired the toaster..',\n", + " 'Bush was liked by Linda but [Linda] repaired the toaster..',\n", + " 'Frank was liked by Linda but [Linda] repaired the toaster..',\n", + " \"Henry was appreciated by potatoes but [potatoes] didn't broke down the toaster..\",\n", + " \"Bush was appreciated by potatoes but [potatoes] didn't broke down the toaster..\",\n", + " \"Frank was appreciated by potatoes but [potatoes] didn't broke down the toaster..\",\n", + " \"Henry was appreciated by Amy but [Amy] didn't broke down the toaster..\",\n", + " \"Bush was appreciated by Amy but [Amy] didn't broke down the toaster..\",\n", + " \"Frank was appreciated by Amy but [Amy] didn't broke down the toaster..\",\n", + " \"Henry was appreciated by Linda but [Linda] didn't broke down the toaster..\",\n", + " \"Bush was appreciated by Linda but [Linda] didn't broke down the toaster..\",\n", + " \"Frank was appreciated by Linda but [Linda] didn't broke down the toaster..\",\n", + " 'potatoes was hate by Henry so [potatoes] broken down the toaster..',\n", + " 'potatoes was hate by Bush so [potatoes] broken down the toaster..',\n", + " 'potatoes was hate by Frank so [potatoes] broken down the toaster..',\n", + " 'Amy was hate by Henry so [Amy] broken down the toaster..',\n", + " 'Amy was hate by Bush so [Amy] broken down the toaster..',\n", + " 'Amy was hate by Frank so [Amy] broken down the toaster..',\n", + " 'Linda was hate by Henry so [Linda] broken down the toaster..',\n", + " 'Linda was hate by Bush so [Linda] broken down the toaster..',\n", + " 'Linda was hate by Frank so [Linda] broken down the toaster..',\n", + " \"potatoes wasn't hate by Henry so [Henry] didn't repaire the toaster..\",\n", + " \"potatoes wasn't hate by Bush so [Bush] didn't repaire the toaster..\",\n", + " \"potatoes wasn't hate by Frank so [Frank] didn't repaire the toaster..\",\n", + " \"Amy wasn't hate by Henry so [Henry] didn't repaire the toaster..\",\n", + " \"Amy wasn't hate by Bush so [Bush] didn't repaire the toaster..\",\n", + " \"Amy wasn't hate by Frank so [Frank] didn't repaire the toaster..\",\n", + " \"Linda wasn't hate by Henry so [Henry] didn't repaire the toaster..\",\n", + " \"Linda wasn't hate by Bush so [Bush] didn't repaire the toaster..\",\n", + " \"Linda wasn't hate by Frank so [Frank] didn't repaire the toaster..\",\n", + " \"Henry wasn't liked by potatoes so [Henry] broken down the toaster..\",\n", + " \"Bush wasn't liked by potatoes so [Bush] broken down the toaster..\",\n", + " \"Frank wasn't liked by potatoes so [Frank] broken down the toaster..\",\n", + " \"Henry wasn't liked by Amy so [Henry] broken down the toaster..\",\n", + " \"Bush wasn't liked by Amy so [Bush] broken down the toaster..\",\n", + " \"Frank wasn't liked by Amy so [Frank] broken down the toaster..\",\n", + " \"Henry wasn't liked by Linda so [Henry] broken down the toaster..\",\n", + " \"Bush wasn't liked by Linda so [Bush] broken down the toaster..\",\n", + " \"Frank wasn't liked by Linda so [Frank] broken down the toaster..\",\n", + " \"Henry was appreciated by potatoes but [Henry] didn't repaire the toaster..\",\n", + " \"Bush was appreciated by potatoes but [Bush] didn't repaire the toaster..\",\n", + " \"Frank was appreciated by potatoes but [Frank] didn't repaire the toaster..\",\n", + " \"Henry was appreciated by Amy but [Henry] didn't repaire the toaster..\",\n", + " \"Bush was appreciated by Amy but [Bush] didn't repaire the toaster..\",\n", + " \"Frank was appreciated by Amy but [Frank] didn't repaire the toaster..\",\n", + " \"Henry was appreciated by Linda but [Henry] didn't repaire the toaster..\",\n", + " \"Bush was appreciated by Linda but [Bush] didn't repaire the toaster..\",\n", + " \"Frank was appreciated by Linda but [Frank] didn't repaire the toaster..\",\n", + " \"potatoes wasn't dislike by Henry but [potatoes] didn't repaire the toaster..\",\n", + " \"potatoes wasn't dislike by Bush but [potatoes] didn't repaire the toaster..\",\n", + " \"potatoes wasn't dislike by Frank but [potatoes] didn't repaire the toaster..\",\n", + " \"Amy wasn't dislike by Henry but [Amy] didn't repaire the toaster..\",\n", + " \"Amy wasn't dislike by Bush but [Amy] didn't repaire the toaster..\",\n", + " \"Amy wasn't dislike by Frank but [Amy] didn't repaire the toaster..\",\n", + " \"Linda wasn't dislike by Henry but [Linda] didn't repaire the toaster..\",\n", + " \"Linda wasn't dislike by Bush but [Linda] didn't repaire the toaster..\",\n", + " \"Linda wasn't dislike by Frank but [Linda] didn't repaire the toaster..\",\n", + " 'potatoes was hate by Henry so [Henry] repaired the toaster..',\n", + " 'potatoes was hate by Bush so [Bush] repaired the toaster..',\n", + " 'potatoes was hate by Frank so [Frank] repaired the toaster..',\n", + " 'Amy was hate by Henry so [Henry] repaired the toaster..',\n", + " 'Amy was hate by Bush so [Bush] repaired the toaster..',\n", + " 'Amy was hate by Frank so [Frank] repaired the toaster..',\n", + " 'Linda was hate by Henry so [Henry] repaired the toaster..',\n", + " 'Linda was hate by Bush so [Bush] repaired the toaster..',\n", + " 'Linda was hate by Frank so [Frank] repaired the toaster..',\n", + " \"potatoes wasn't hate by Henry so [Henry] broken down the toaster..\",\n", + " \"potatoes wasn't hate by Bush so [Bush] broken down the toaster..\",\n", + " \"potatoes wasn't hate by Frank so [Frank] broken down the toaster..\",\n", + " \"Amy wasn't hate by Henry so [Henry] broken down the toaster..\",\n", + " \"Amy wasn't hate by Bush so [Bush] broken down the toaster..\",\n", + " \"Amy wasn't hate by Frank so [Frank] broken down the toaster..\",\n", + " \"Linda wasn't hate by Henry so [Henry] broken down the toaster..\",\n", + " \"Linda wasn't hate by Bush so [Bush] broken down the toaster..\",\n", + " \"Linda wasn't hate by Frank so [Frank] broken down the toaster..\",\n", + " \"potatoes wasn't hate by Henry but [potatoes] didn't repaire the toaster..\",\n", + " \"potatoes wasn't hate by Bush but [potatoes] didn't repaire the toaster..\",\n", + " \"potatoes wasn't hate by Frank but [potatoes] didn't repaire the toaster..\",\n", + " \"Amy wasn't hate by Henry but [Amy] didn't repaire the toaster..\",\n", + " \"Amy wasn't hate by Bush but [Amy] didn't repaire the toaster..\",\n", + " \"Amy wasn't hate by Frank but [Amy] didn't repaire the toaster..\",\n", + " \"Linda wasn't hate by Henry but [Linda] didn't repaire the toaster..\",\n", + " \"Linda wasn't hate by Bush but [Linda] didn't repaire the toaster..\",\n", + " \"Linda wasn't hate by Frank but [Linda] didn't repaire the toaster..\",\n", + " \"Henry wasn't liked by potatoes but [potatoes] didn't repaire the toaster..\",\n", + " \"Bush wasn't liked by potatoes but [potatoes] didn't repaire the toaster..\",\n", + " \"Frank wasn't liked by potatoes but [potatoes] didn't repaire the toaster..\",\n", + " \"Henry wasn't liked by Amy but [Amy] didn't repaire the toaster..\",\n", + " \"Bush wasn't liked by Amy but [Amy] didn't repaire the toaster..\",\n", + " \"Frank wasn't liked by Amy but [Amy] didn't repaire the toaster..\",\n", + " \"Henry wasn't liked by Linda but [Linda] didn't repaire the toaster..\",\n", + " \"Bush wasn't liked by Linda but [Linda] didn't repaire the toaster..\",\n", + " \"Frank wasn't liked by Linda but [Linda] didn't repaire the toaster..\",\n", + " \"potatoes was dislike by Henry so [Henry] didn't broke down the toaster..\",\n", + " \"potatoes was dislike by Bush so [Bush] didn't broke down the toaster..\",\n", + " \"potatoes was dislike by Frank so [Frank] didn't broke down the toaster..\",\n", + " \"Amy was dislike by Henry so [Henry] didn't broke down the toaster..\",\n", + " \"Amy was dislike by Bush so [Bush] didn't broke down the toaster..\",\n", + " \"Amy was dislike by Frank so [Frank] didn't broke down the toaster..\",\n", + " \"Linda was dislike by Henry so [Henry] didn't broke down the toaster..\",\n", + " \"Linda was dislike by Bush so [Bush] didn't broke down the toaster..\",\n", + " \"Linda was dislike by Frank so [Frank] didn't broke down the toaster..\",\n", + " \"Henry wasn't liked by potatoes but [potatoes] broken down the toaster..\",\n", + " \"Bush wasn't liked by potatoes but [potatoes] broken down the toaster..\",\n", + " \"Frank wasn't liked by potatoes but [potatoes] broken down the toaster..\",\n", + " \"Henry wasn't liked by Amy but [Amy] broken down the toaster..\",\n", + " \"Bush wasn't liked by Amy but [Amy] broken down the toaster..\",\n", + " \"Frank wasn't liked by Amy but [Amy] broken down the toaster..\",\n", + " \"Henry wasn't liked by Linda but [Linda] broken down the toaster..\",\n", + " \"Bush wasn't liked by Linda but [Linda] broken down the toaster..\",\n", + " \"Frank wasn't liked by Linda but [Linda] broken down the toaster..\",\n", + " \"potatoes wasn't hate by Henry but [Henry] repaired the toaster..\",\n", + " \"potatoes wasn't hate by Bush but [Bush] repaired the toaster..\",\n", + " \"potatoes wasn't hate by Frank but [Frank] repaired the toaster..\",\n", + " \"Amy wasn't hate by Henry but [Henry] repaired the toaster..\",\n", + " \"Amy wasn't hate by Bush but [Bush] repaired the toaster..\",\n", + " \"Amy wasn't hate by Frank but [Frank] repaired the toaster..\",\n", + " \"Linda wasn't hate by Henry but [Henry] repaired the toaster..\",\n", + " \"Linda wasn't hate by Bush but [Bush] repaired the toaster..\",\n", + " \"Linda wasn't hate by Frank but [Frank] repaired the toaster..\",\n", + " 'potatoes was dislike by Henry so [Henry] repaired the toaster..',\n", + " 'potatoes was dislike by Bush so [Bush] repaired the toaster..',\n", + " 'potatoes was dislike by Frank so [Frank] repaired the toaster..',\n", + " 'Amy was dislike by Henry so [Henry] repaired the toaster..',\n", + " 'Amy was dislike by Bush so [Bush] repaired the toaster..',\n", + " 'Amy was dislike by Frank so [Frank] repaired the toaster..',\n", + " 'Linda was dislike by Henry so [Henry] repaired the toaster..',\n", + " 'Linda was dislike by Bush so [Bush] repaired the toaster..',\n", + " 'Linda was dislike by Frank so [Frank] repaired the toaster..',\n", + " \"potatoes was hate by Henry but [Henry] didn't repaire the toaster..\",\n", + " \"potatoes was hate by Bush but [Bush] didn't repaire the toaster..\",\n", + " \"potatoes was hate by Frank but [Frank] didn't repaire the toaster..\",\n", + " \"Amy was hate by Henry but [Henry] didn't repaire the toaster..\",\n", + " \"Amy was hate by Bush but [Bush] didn't repaire the toaster..\",\n", + " \"Amy was hate by Frank but [Frank] didn't repaire the toaster..\",\n", + " \"Linda was hate by Henry but [Henry] didn't repaire the toaster..\",\n", + " \"Linda was hate by Bush but [Bush] didn't repaire the toaster..\",\n", + " \"Linda was hate by Frank but [Frank] didn't repaire the toaster..\",\n", + " 'Henry was appreciated by potatoes so [potatoes] broken down the toaster..',\n", + " 'Bush was appreciated by potatoes so [potatoes] broken down the toaster..',\n", + " 'Frank was appreciated by potatoes so [potatoes] broken down the toaster..',\n", + " 'Henry was appreciated by Amy so [Amy] broken down the toaster..',\n", + " 'Bush was appreciated by Amy so [Amy] broken down the toaster..',\n", + " 'Frank was appreciated by Amy so [Amy] broken down the toaster..',\n", + " 'Henry was appreciated by Linda so [Linda] broken down the toaster..',\n", + " 'Bush was appreciated by Linda so [Linda] broken down the toaster..',\n", + " 'Frank was appreciated by Linda so [Linda] broken down the toaster..',\n", + " \"Henry wasn't appreciate by potatoes so [Henry] broken down the toaster..\",\n", + " \"Bush wasn't appreciate by potatoes so [Bush] broken down the toaster..\",\n", + " \"Frank wasn't appreciate by potatoes so [Frank] broken down the toaster..\",\n", + " \"Henry wasn't appreciate by Amy so [Henry] broken down the toaster..\",\n", + " \"Bush wasn't appreciate by Amy so [Bush] broken down the toaster..\",\n", + " \"Frank wasn't appreciate by Amy so [Frank] broken down the toaster..\",\n", + " \"Henry wasn't appreciate by Linda so [Henry] broken down the toaster..\",\n", + " \"Bush wasn't appreciate by Linda so [Bush] broken down the toaster..\",\n", + " \"Frank wasn't appreciate by Linda so [Frank] broken down the toaster..\",\n", + " 'Henry was liked by potatoes so [potatoes] broken down the toaster..',\n", + " 'Bush was liked by potatoes so [potatoes] broken down the toaster..',\n", + " 'Frank was liked by potatoes so [potatoes] broken down the toaster..',\n", + " 'Henry was liked by Amy so [Amy] broken down the toaster..',\n", + " 'Bush was liked by Amy so [Amy] broken down the toaster..',\n", + " 'Frank was liked by Amy so [Amy] broken down the toaster..',\n", + " 'Henry was liked by Linda so [Linda] broken down the toaster..',\n", + " 'Bush was liked by Linda so [Linda] broken down the toaster..',\n", + " 'Frank was liked by Linda so [Linda] broken down the toaster..',\n", + " \"Henry wasn't liked by potatoes so [potatoes] didn't broke down the toaster..\",\n", + " \"Bush wasn't liked by potatoes so [potatoes] didn't broke down the toaster..\",\n", + " \"Frank wasn't liked by potatoes so [potatoes] didn't broke down the toaster..\",\n", + " \"Henry wasn't liked by Amy so [Amy] didn't broke down the toaster..\",\n", + " \"Bush wasn't liked by Amy so [Amy] didn't broke down the toaster..\",\n", + " \"Frank wasn't liked by Amy so [Amy] didn't broke down the toaster..\",\n", + " \"Henry wasn't liked by Linda so [Linda] didn't broke down the toaster..\",\n", + " \"Bush wasn't liked by Linda so [Linda] didn't broke down the toaster..\",\n", + " \"Frank wasn't liked by Linda so [Linda] didn't broke down the toaster..\",\n", + " \"potatoes wasn't dislike by Henry but [potatoes] broken down the toaster..\",\n", + " \"potatoes wasn't dislike by Bush but [potatoes] broken down the toaster..\",\n", + " \"potatoes wasn't dislike by Frank but [potatoes] broken down the toaster..\",\n", + " \"Amy wasn't dislike by Henry but [Amy] broken down the toaster..\",\n", + " \"Amy wasn't dislike by Bush but [Amy] broken down the toaster..\",\n", + " \"Amy wasn't dislike by Frank but [Amy] broken down the toaster..\",\n", + " \"Linda wasn't dislike by Henry but [Linda] broken down the toaster..\",\n", + " \"Linda wasn't dislike by Bush but [Linda] broken down the toaster..\",\n", + " \"Linda wasn't dislike by Frank but [Linda] broken down the toaster..\",\n", + " \"Henry was appreciated by potatoes so [potatoes] didn't repaire the toaster..\",\n", + " \"Bush was appreciated by potatoes so [potatoes] didn't repaire the toaster..\",\n", + " \"Frank was appreciated by potatoes so [potatoes] didn't repaire the toaster..\",\n", + " \"Henry was appreciated by Amy so [Amy] didn't repaire the toaster..\",\n", + " \"Bush was appreciated by Amy so [Amy] didn't repaire the toaster..\",\n", + " \"Frank was appreciated by Amy so [Amy] didn't repaire the toaster..\",\n", + " \"Henry was appreciated by Linda so [Linda] didn't repaire the toaster..\",\n", + " \"Bush was appreciated by Linda so [Linda] didn't repaire the toaster..\",\n", + " \"Frank was appreciated by Linda so [Linda] didn't repaire the toaster..\",\n", + " 'potatoes was dislike by Henry but [potatoes] repaired the toaster..',\n", + " 'potatoes was dislike by Bush but [potatoes] repaired the toaster..',\n", + " 'potatoes was dislike by Frank but [potatoes] repaired the toaster..',\n", + " 'Amy was dislike by Henry but [Amy] repaired the toaster..',\n", + " 'Amy was dislike by Bush but [Amy] repaired the toaster..',\n", + " 'Amy was dislike by Frank but [Amy] repaired the toaster..',\n", + " 'Linda was dislike by Henry but [Linda] repaired the toaster..',\n", + " 'Linda was dislike by Bush but [Linda] repaired the toaster..',\n", + " 'Linda was dislike by Frank but [Linda] repaired the toaster..',\n", + " \"potatoes wasn't hate by Henry but [potatoes] broken down the toaster..\",\n", + " \"potatoes wasn't hate by Bush but [potatoes] broken down the toaster..\",\n", + " \"potatoes wasn't hate by Frank but [potatoes] broken down the toaster..\",\n", + " \"Amy wasn't hate by Henry but [Amy] broken down the toaster..\",\n", + " \"Amy wasn't hate by Bush but [Amy] broken down the toaster..\",\n", + " \"Amy wasn't hate by Frank but [Amy] broken down the toaster..\",\n", + " \"Linda wasn't hate by Henry but [Linda] broken down the toaster..\",\n", + " \"Linda wasn't hate by Bush but [Linda] broken down the toaster..\",\n", + " \"Linda wasn't hate by Frank but [Linda] broken down the toaster..\",\n", + " \"potatoes wasn't dislike by Henry so [Henry] broken down the toaster..\",\n", + " \"potatoes wasn't dislike by Bush so [Bush] broken down the toaster..\",\n", + " \"potatoes wasn't dislike by Frank so [Frank] broken down the toaster..\",\n", + " \"Amy wasn't dislike by Henry so [Henry] broken down the toaster..\",\n", + " \"Amy wasn't dislike by Bush so [Bush] broken down the toaster..\",\n", + " \"Amy wasn't dislike by Frank so [Frank] broken down the toaster..\",\n", + " \"Linda wasn't dislike by Henry so [Henry] broken down the toaster..\",\n", + " \"Linda wasn't dislike by Bush so [Bush] broken down the toaster..\",\n", + " \"Linda wasn't dislike by Frank so [Frank] broken down the toaster..\"]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "causal_sentences\n", + "turning_sentences\n", + "substituted_sentences" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "examples = [\n", + " (28,\n", + " 'Anna did a lot better than her good friend Lucy on the test because [she] had studied so hard.',\n", + " 'better/worse:study hard'),\n", + " (30,\n", + " 'The firemen arrived after the police because [they] were coming from so far away.',\n", + " 'after/before:far away'),\n", + " (32,\n", + " \"Frank was upset with Tom because the toaster [he] had bought from him didn't work.\",\n", + " 'be upset with:buy from not work/sell not work'),\n", + " (36,\n", + " 'The sack of potatoes had been placed above the bag of flour, so [it] had to be moved first.',\n", + " 'above/below:moved first'),\n", + " (38,\n", + " 'Pete envies Martin although [he] is very successful.',\n", + " 'although/because'),\n", + " (42,\n", + " 'I poured water from the bottle into the cup until [it] was empty.',\n", + " 'pour:empty/full'),\n", + " (46,\n", + " \"Sid explained his theory to Mark but [he] couldn't convince him.\",\n", + " 'explain:convince/understand'),\n", + " (48,\n", + " \"Susan knew that Ann's son had been in a car accident, so [she] told her about it.\",\n", + " '?know tell:so/because'),\n", + " (50,\n", + " \"Joe's uncle can still beat him at tennis, even though [he] is 30 years younger.\",\n", + " 'beat:younger/older'),\n", + " (64,\n", + " 'In the middle of the outdoor concert, the rain started falling, but [it] continued until 10.',\n", + " 'but/and'),\n", + " (68,\n", + " 'Ann asked Mary what time the library closes, because [she] had forgotten.',\n", + " 'because/but'),\n", + " (84,\n", + " 'If the con artist has succeeded in fooling Sam, [he] would have gotten a lot of money.',\n", + " 'fool:get/lose'),\n", + " (92,\n", + " 'Alice tried frantically to stop her daughter from chatting at the party, leaving us to wonder why [she] was behaving so strangely.',\n", + " '?stop normal/stop abnormal:strange'),\n", + " (98,\n", + " \"I was trying to open the lock with the key, but someone had filled the keyhole with chewing gum, and I couldn't get [it] in.\",\n", + " 'put ... into filled with ... :get in/get out'),\n", + " (100,\n", + " 'The dog chased the cat, which ran up a tree. [It] waited at the bottom.',\n", + " 'up:at the bottom/at the top'),\n", + " (106,\n", + " 'John was doing research in the library when he heard a man humming and whistling. [He] was very annoyed.',\n", + " 'hear ... humming and whistling:annoyed/annoying'),\n", + " (108,\n", + " 'John was jogging through the park when he saw a man juggling watermelons. [He] was very impressed.',\n", + " 'see ... juggling watermelons:impressed/impressive'),\n", + " (132,\n", + " 'Jane knocked on the door, and Susan answered it. [She] invited her to come out.',\n", + " 'visit:invite come out/invite come in'),\n", + " (150,\n", + " 'Jackson was greatly influenced by Arnold, though [he] lived two centuries later.',\n", + " 'influence:later/earlier'),\n", + " (160,\n", + " 'The actress used to be named Terpsichore, but she changed it to Tina a few years ago, because she figured [it] was too hard to pronounce.',\n", + " 'change:hard/easy'),\n", + " (166,\n", + " 'Fred is the only man still alive who remembers my great-grandfather. [He] is a remarkable man.',\n", + " 'alive:is/was'),\n", + " (170,\n", + " \"In July, Kamtchatka declared war on Yakutsk. Since Yakutsk's army was much better equipped and ten times larger, [they] were defeated within weeks.\",\n", + " 'better equipped and large:defeated/victorious'),\n", + " (186,\n", + " 'When the sponsors of the bill got to the town hall, they were surprised to find that the room was full of opponents. [They] were very much in the minority.',\n", + " 'be full of:minority/majority'),\n", + " (188,\n", + " 'Everyone really loved the oatmeal cookies; only a few people liked the chocolate chip cookies. Next time, we should make more of [them] .',\n", + " 'like over:more/fewer'),\n", + " (190,\n", + " 'We had hoped to place copies of our newsletter on all the chairs in the auditorium, but there were simply not enough of [them] .',\n", + " 'place on all:not enough/too many'),\n", + " (196,\n", + " \"Steve follows Fred's example in everything. [He] admires him hugely.\",\n", + " 'follow:admire/influence'),\n", + " (198,\n", + " \"The table won't fit through the doorway because [it] is too wide.\",\n", + " 'fit through:wide/narrow'),\n", + " (200,\n", + " 'Grace was happy to trade me her sweater for my jacket. She thinks [it] looks dowdy on her.',\n", + " 'trade:dowdy/great'),\n", + " (202,\n", + " 'John hired Bill to take care of [him] .',\n", + " 'hire/hire oneself to:take care of'),\n", + " (204,\n", + " 'John promised Bill to leave, so an hour later [he] left.',\n", + " 'promise/order'),\n", + " (210,\n", + " \"Jane knocked on Susan's door but [she] did not get an answer.\",\n", + " 'knock:get an answer/answer'),\n", + " (212,\n", + " 'Joe paid the detective after [he] received the final report on the case.',\n", + " 'pay:receive/deliver'),\n", + " (226,\n", + " 'Bill passed the half-empty plate to John because [he] was full.',\n", + " 'pass the plate:full/hungry'),\n", + " (252,\n", + " 'George got free tickets to the play, but he gave them to Eric, even though [he] was particularly eager to see it.',\n", + " 'even though/because/not'),\n", + " (255,\n", + " \"Jane gave Joan candy because [she] wasn't hungry.\",\n", + " 'give:not hungry/hungry'),\n", + " (259,\n", + " 'James asked Robert for a favor but [he] was refused.',\n", + " 'ask for a favor:refuse/be refused`'),\n", + " (261,\n", + " 'Kirilov ceded the presidency to Shatov because [he] was less popular.',\n", + " 'cede:less popular/more popular'),\n", + " (263,\n", + " 'Emma did not pass the ball to Janie although [she] saw that she was open.',\n", + " 'not pass although:see open/open')]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(examples)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/WSC_associative_label.json b/WSC_associative_label.json new file mode 100644 index 000000000000..4fd2fd1015ae --- /dev/null +++ b/WSC_associative_label.json @@ -0,0 +1 @@ +[{"index": 0, "sentence": "The city councilmen refused the demonstrators a permit because [they] feared violence.", "answer1": "The demonstrators", "answer0": "The city councilmen", "is_associative": 0, "correct_answer": "The city councilmen"}, {"index": 1, "sentence": "The city councilmen refused the demonstrators a permit because [they] advocated violence.", "answer1": "The demonstrators", "answer0": "The city councilmen", "is_associative": 0, "correct_answer": "The demonstrators"}, {"index": 2, "sentence": "The trophy doesn't fit into the brown suitcase because [it] is too large.", "answer1": "the suitcase", "answer0": "the trophy", "is_associative": 0, "correct_answer": "the trophy"}, {"index": 3, "sentence": "The trophy doesn't fit into the brown suitcase because [it] is too small.", "answer1": "the suitcase", "answer0": "the trophy", "is_associative": 0, "correct_answer": "the suitcase"}, {"index": 4, "sentence": "Joan made sure to thank Susan for all the help [she] had recieved.", "answer1": "Susan", "answer0": "Joan", "is_associative": 0, "correct_answer": "Joan"}, {"index": 5, "sentence": "Joan made sure to thank Susan for all the help [she] had given.", "answer1": "Susan", "answer0": "Joan", "is_associative": 0, "correct_answer": "Susan"}, {"index": 6, "sentence": "Paul tried to call George on the phone, but [he] wasn't successful.", "answer1": "George", "answer0": "Paul", "is_associative": 0, "correct_answer": "Paul"}, {"index": 7, "sentence": "Paul tried to call George on the phone, but [he] wasn't available.", "answer1": "George", "answer0": "Paul", "is_associative": 0, "correct_answer": "George"}, {"index": 8, "sentence": "The lawyer asked the witness a question, but [he] was reluctant to repeat it.", "answer1": "the witness", "answer0": "the lawyer", "is_associative": 0, "correct_answer": "the lawyer"}, {"index": 9, "sentence": "The lawyer asked the witness a question, but [he] was reluctant to answer it.", "answer1": "the witness", "answer0": "the lawyer", "is_associative": 0, "correct_answer": "the witness"}, {"index": 10, "sentence": "The delivery truck zoomed by the school bus because [it] was going so fast.", "answer1": "the school bus", "answer0": "the delivery truck", "is_associative": 0, "correct_answer": "the delivery truck"}, {"index": 11, "sentence": "The delivery truck zoomed by the school bus because [it] was going so slow.", "answer1": "the school bus", "answer0": "the delivery truck", "is_associative": 0, "correct_answer": "the school bus"}, {"index": 12, "sentence": "Frank felt vindicated when his longtime rival Bill revealed that [he] was the winner of the competition.", "answer1": "Bill", "answer0": "Frank", "is_associative": 0, "correct_answer": "Frank"}, {"index": 13, "sentence": "Frank felt crushed when his longtime rival Bill revealed that [he] was the winner of the competition.", "answer1": "Bill", "answer0": "Frank", "is_associative": 0, "correct_answer": "Bill"}, {"index": 14, "sentence": "The man couldn't lift his son because [he] was so weak.", "answer1": "The son", "answer0": "The man", "is_associative": 0, "correct_answer": "The man"}, {"index": 15, "sentence": "The man couldn't lift his son because [he] was so heavy.", "answer1": "The son", "answer0": "The man", "is_associative": 0, "correct_answer": "The son"}, {"index": 16, "sentence": "The large ball crashed right through the table because [it] was made of steel.", "answer1": "The table", "answer0": "The large ball", "is_associative": 0, "correct_answer": "The large ball"}, {"index": 17, "sentence": "The large ball crashed right through the table because [it] was made of styrofoam.", "answer1": "The table", "answer0": "The large ball", "is_associative": 0, "correct_answer": "The table"}, {"index": 18, "sentence": "John couldn't see the stage with Billy in front of him because [he] is so short.", "answer1": "Billy", "answer0": "John", "is_associative": 0, "correct_answer": "John"}, {"index": 19, "sentence": "John couldn't see the stage with Billy in front of him because [he] is so tall.", "answer1": "Billy", "answer0": "John", "is_associative": 0, "correct_answer": "Billy"}, {"index": 20, "sentence": "Tom threw his schoolbag down to Ray after [he] reached the top of the stairs.", "answer1": "Ray", "answer0": "Tom", "is_associative": 0, "correct_answer": "Tom"}, {"index": 21, "sentence": "Tom threw his schoolbag down to Ray after [he] reached the bottom of the stairs.", "answer1": "Ray", "answer0": "Tom", "is_associative": 0, "correct_answer": "Ray"}, {"index": 22, "sentence": "Although they ran at about the same speed, Sue beat Sally because [she] had such a good start.", "answer1": "Sally", "answer0": "Sue", "is_associative": 0, "correct_answer": "Sue"}, {"index": 23, "sentence": "Although they ran at about the same speed, Sue beat Sally because [she] had such a bad start.", "answer1": "Sally", "answer0": "Sue", "is_associative": 0, "correct_answer": "Sally"}, {"index": 24, "sentence": "The sculpture rolled off the shelf because [it] wasn't anchored.", "answer1": "The shelf", "answer0": "The sculpture", "is_associative": 0, "correct_answer": "The sculpture"}, {"index": 25, "sentence": "The sculpture rolled off the shelf because [it] wasn't level.", "answer1": "The shelf", "answer0": "The sculpture", "is_associative": 0, "correct_answer": "The shelf"}, {"index": 26, "sentence": "Sam's drawing was hung just above Tina's and [it] did look much better with another one below it.", "answer1": "Tina's drawing", "answer0": "Sam's drawing", "is_associative": 0, "correct_answer": "Sam's drawing"}, {"index": 27, "sentence": "Sam's drawing was hung just above Tina's and [it] did look much better with another one above it.", "answer1": "Tina's drawing", "answer0": "Sam's 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[It] is to the right of the bookcase.", "answer1": "The oak tree", "answer0": "The painting", "is_associative": 0, "correct_answer": "The painting"}, {"index": 56, "sentence": "The drain is clogged with hair. [It] has to be cleaned.", "answer1": "The hair", "answer0": "The drain", "is_associative": 0, "correct_answer": "The drain"}, {"index": 57, "sentence": "The drain is clogged with hair. [It] has to be removed.", "answer1": "The hair", "answer0": "The drain", "is_associative": 0, "correct_answer": "The hair"}, {"index": 59, "sentence": "My meeting started at 4:00 and I needed to catch the train at 4:30, so there wasn't much time. 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She has loved [it] since she was a child.", "answer1": "The piece", "answer0": "The flute", "is_associative": 0, "correct_answer": "The piece"}, {"index": 122, "sentence": "Sam pulled up a chair to the piano, but [it] was broken, so he had to stand instead.", "answer1": "The piano", "answer0": "The chair", "is_associative": 0, "correct_answer": "The chair"}, {"index": 123, "sentence": "Sam pulled up a chair to the piano, but [it] was broken, so he had to sing instead.", "answer1": "The piano", "answer0": "The chair", "is_associative": 0, "correct_answer": "The piano"}, {"index": 124, "sentence": "Since it was raining, I carried the newspaper in my backpack to keep [it] dry.", "answer1": "The backpack", "answer0": "The newspaper", "is_associative": 0, "correct_answer": "The newspaper"}, {"index": 125, "sentence": "Since it was raining, I carried the newspaper over my backpack to keep [it] dry.", "answer1": "The backpack", "answer0": "The newspaper", "is_associative": 0, "correct_answer": "The backpack"}, {"index": 126, "sentence": "Sara borrowed the book from the library because she needs it for an article she is working on. 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[She] was out.", "answer1": "Susan", "answer0": "Jane", "is_associative": 0, "correct_answer": "Susan"}, {"index": 132, "sentence": "Jane knocked on the door, and Susan answered it. [She] invited her to come out.", "answer1": "Susan", "answer0": "Jane", "is_associative": 0, "correct_answer": "Jane"}, {"index": 133, "sentence": "Jane knocked on the door, and Susan answered it. 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At that date [he] had been travelling for five years.", "answer1": "Cooper", "answer0": "Thomson", "is_associative": 0, "correct_answer": "Thomson"}, {"index": 149, "sentence": "Thomson visited Cooper's grave in 1765. At that date [he] had been dead for five years.", "answer1": "Cooper", "answer0": "Thomson", "is_associative": 0, "correct_answer": "Cooper"}, {"index": 150, "sentence": "Jackson was greatly influenced by Arnold, though [he] lived two centuries later.", "answer1": "Arnold", "answer0": "Jackson", "is_associative": 0, "correct_answer": "Jackson"}, {"index": 151, "sentence": "Jackson was greatly influenced by Arnold, though [he] lived two centuries earlier.", "answer1": "Arnold", "answer0": "Jackson", "is_associative": 0, "correct_answer": "Arnold"}, {"index": 152, "sentence": "I can't cut that tree down with that axe; [it] is too thick.", "answer1": "The axe", "answer0": "The tree", "is_associative": 0, "correct_answer": "The tree"}, {"index": 153, "sentence": "I can't cut that tree down with that axe; [it] is too small.", "answer1": "The axe", "answer0": "The tree", "is_associative": 0, "correct_answer": "The axe"}, {"index": 154, "sentence": "The foxes are getting in at night and attacking the chickens. I shall have to kill [them] .", "answer1": "The chickens", "answer0": "The foxes", "is_associative": 0, "correct_answer": "The foxes"}, {"index": 156, "sentence": "The foxes are getting in at night and attacking the chickens. [They] have gotten very bold.", "answer1": "The chickens", "answer0": "The foxes", "is_associative": 0, "correct_answer": "The foxes"}, {"index": 157, "sentence": "The foxes are getting in at night and attacking the chickens. [They] have gotten very nervous.", "answer1": "The chickens", "answer0": "The foxes", "is_associative": 0, "correct_answer": "The chickens"}, {"index": 159, "sentence": "Fred covered his eyes with his hands, because the wind was blowing sand around. He lowered [them] when the wind stopped.", "answer1": "His hands", "answer0": "His eyes", "is_associative": 0, "correct_answer": "His hands"}, {"index": 160, "sentence": "The actress used to be named Terpsichore, but she changed it to Tina a few years ago, because she figured [it] was too hard to pronounce.", "answer1": "Tina", "answer0": "Terpsichore", "is_associative": 0, "correct_answer": "Terpsichore"}, {"index": 161, "sentence": "The actress used to be named Terpsichore, but she changed it to Tina a few years ago, because she figured [it] was easier to pronounce.", "answer1": "Tina", "answer0": "Terpsichore", "is_associative": 0, "correct_answer": "Tina"}, {"index": 162, "sentence": "Fred watched TV while George went out to buy groceries. After an hour [he] got up.", "answer1": "George", "answer0": "Fred", "is_associative": 0, "correct_answer": "Fred"}, {"index": 163, "sentence": "Fred watched TV while George went out to buy groceries. 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[He] is a remarkable man.", "answer1": "My great-grandfather", "answer0": "Fred", "is_associative": 0, "correct_answer": "Fred"}, {"index": 167, "sentence": "Fred is the only man still alive who remembers my great-grandfather. [He] was a remarkable man.", "answer1": "My great-grandfather", "answer0": "Fred", "is_associative": 0, "correct_answer": "My great-grandfather"}, {"index": 168, "sentence": "Fred is the only man alive who still remembers my father as an infant. When Fred first saw my father, [he] was twelve years old.", "answer1": "My father", "answer0": "Fred", "is_associative": 0, "correct_answer": "Fred"}, {"index": 169, "sentence": "Fred is the only man alive who still remembers my father as an infant. When Fred first saw my father, [he] was twelve months old.", "answer1": "My father", "answer0": "Fred", "is_associative": 0, "correct_answer": "My father"}, {"index": 170, "sentence": "In July, Kamtchatka declared war on Yakutsk. Since Yakutsk's army was much better equipped and ten times larger, [they] were defeated within weeks.", "answer1": "Yakutsk", "answer0": "Kamchatka", "is_associative": 0, "correct_answer": "Kamchatka"}, {"index": 171, "sentence": "In July, Kamtchatka declared war on Yakutsk. Since Yakutsk's army was much better equipped and ten times larger, [they] were victorious within weeks.", "answer1": "Yakutsk", "answer0": "Kamchatka", "is_associative": 0, "correct_answer": "Yakutsk"}, {"index": 172, "sentence": "Look! There is a minnow swimming right below that duck! [It] had better get away to safety fast!", "answer1": "The duck", "answer0": "The minnow", "is_associative": 0, "correct_answer": "The minnow"}, {"index": 173, "sentence": "Look! There is a shark swimming right below that duck! [It] had better get away to safety fast!", "answer1": "The duck", "answer0": "The shark", "is_associative": 0, "correct_answer": "The duck"}, {"index": 178, "sentence": "The journalists interviewed the stars of the new movie. [They] were very persistent, so the interview lasted for a long time.", "answer1": "The stars", "answer0": "The journalists", "is_associative": 0, "correct_answer": "The journalists"}, {"index": 179, "sentence": "The journalists interviewed the stars of the new movie. [They] were very cooperative, so the interview lasted for a long time.", "answer1": "The stars", "answer0": "The journalists", "is_associative": 0, "correct_answer": "The stars"}, {"index": 186, "sentence": "When the sponsors of the bill got to the town hall, they were surprised to find that the room was full of opponents. [They] were very much in the minority.", "answer1": "The opponents", "answer0": "The sponsors", "is_associative": 0, "correct_answer": "The sponsors"}, {"index": 187, "sentence": "When the sponsors of the bill got to the town hall, they were surprised to find that the room was full of opponents. [They] were very much in the majority.", "answer1": "The opponents", "answer0": "The sponsors", "is_associative": 0, "correct_answer": "The opponents"}, {"index": 188, "sentence": "Everyone really loved the oatmeal cookies; only a few people liked the chocolate chip cookies. Next time, we should make more of [them] .", "answer1": "The chocolate chip cookies", "answer0": "The oatmeal cookies", "is_associative": 0, "correct_answer": "The oatmeal cookies"}, {"index": 189, "sentence": "Everyone really loved the oatmeal cookies; only a few people liked the chocolate chip cookies. 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"sentence": "Jane knocked on Susan's door but [she] did not answer.", "answer1": "Susan", "answer0": "Jane", "is_associative": 0, "correct_answer": "Susan"}, {"index": 212, "sentence": "Joe paid the detective after [he] received the final report on the case.", "answer1": "the detective", "answer0": "Joe", "is_associative": 0, "correct_answer": "Joe"}, {"index": 213, "sentence": "Joe paid the detective after [he] delivered the final report on the case.", "answer1": "the detective", "answer0": "Joe", "is_associative": 0, "correct_answer": "the detective"}, {"index": 214, "sentence": "Beth didn't get angry with Sally, who had cut her off, because [she] stopped and counted to ten.", "answer1": "Sally", "answer0": "Beth", "is_associative": 0, "correct_answer": "Beth"}, {"index": 215, "sentence": "Beth didn't get angry with Sally, who had cut her off, because [she] stopped and apologized.", "answer1": "Sally", "answer0": "Beth", "is_associative": 0, "correct_answer": "Sally"}, {"index": 216, "sentence": "Jim signaled the barman and gestured toward [his] empty glass", "answer1": "The barman", "answer0": "Jim", "is_associative": 0, "correct_answer": "Jim"}, {"index": 217, "sentence": "Jim signaled the barman and gestured toward [his] bathroom key.", "answer1": "The barman", "answer0": "Jim", "is_associative": 0, "correct_answer": "The barman"}, {"index": 218, "sentence": "Dan took the rear seat while Bill claimed the front because [his] \"Dibs!\" was slow.", "answer1": "Bill", "answer0": "Dan", "is_associative": 0, "correct_answer": "Dan"}, {"index": 219, "sentence": "Dan took the rear seat while Bill claimed the front because [his] \"Dibs!\" was quicker.", "answer1": "Bill", "answer0": "Dan", "is_associative": 0, "correct_answer": "Bill"}, {"index": 220, "sentence": "Tom said \"Check\" to Ralph as he moved [his] bishop.", "answer1": "Ralph", "answer0": "Tom", "is_associative": 0, "correct_answer": "Tom"}, {"index": 221, "sentence": "Tom said \"Check\" to Ralph as he took [his] bishop.", "answer1": "Ralph", "answer0": "Tom", "is_associative": 0, "correct_answer": "Ralph"}, {"index": 222, "sentence": "As Andrea in the crop duster passed over Susan, [she] could see the landing strip.", "answer1": "Susan", "answer0": "Andrea", "is_associative": 0, "correct_answer": "Andrea"}, {"index": 223, "sentence": "As Andrea in the crop duster passed over Susan, [she] could see the landing gear.", "answer1": "Susan", "answer0": "Andrea", "is_associative": 0, "correct_answer": "Susan"}, {"index": 224, "sentence": "Tom gave Ralph a lift to school so [he] wouldn't have to drive alone.", "answer1": "Ralph", "answer0": "Tom", "is_associative": 0, "correct_answer": "Tom"}, {"index": 225, "sentence": "Tom gave Ralph a lift to school so [he] wouldn't have to walk.", "answer1": "Ralph", "answer0": "Tom", "is_associative": 0, "correct_answer": "Ralph"}, {"index": 226, "sentence": "Bill passed the half-empty plate to John because [he] was full.", "answer1": "John", "answer0": "Bill", "is_associative": 0, "correct_answer": "Bill"}, {"index": 227, "sentence": "Bill passed the half-empty plate to John because [he] was hungry.", "answer1": "John", "answer0": "Bill", "is_associative": 0, "correct_answer": "John"}, {"index": 228, "sentence": "Bill passed the gameboy to John because [his] turn was over.", "answer1": "John", "answer0": "Bill", "is_associative": 0, "correct_answer": "Bill"}, {"index": 229, "sentence": "Bill passed the gameboy to John because [his] turn was next.", "answer1": "John", "answer0": "Bill", "is_associative": 0, "correct_answer": "John"}, {"index": 230, "sentence": "The man lifted the boy onto [his] shoulders.", "answer1": "The boy", "answer0": "The man", "is_associative": 0, "correct_answer": "The man"}, {"index": 232, "sentence": "Stretching [her] back, the woman smiled at the girl.", "answer1": "The girl", "answer0": "The woman", "is_associative": 0, "correct_answer": "The woman"}, {"index": 233, "sentence": "Patting [her] back, the 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"Tommy"}, {"index": 239, "sentence": "When Tommy dropped his ice cream, Timmy giggled, so father gave [him] a stern look.", "answer1": "Timmy", "answer0": "Tommy", "is_associative": 0, "correct_answer": "Timmy"}, {"index": 240, "sentence": "As Ollie carried Tommy up the long winding steps, [his] legs ached.", "answer1": "Tommy", "answer0": "Ollie", "is_associative": 0, "correct_answer": "Ollie"}, {"index": 241, "sentence": "As Ollie carried Tommy up the long winding steps, [his] legs dangled.", "answer1": "Tommy", "answer0": "Ollie", "is_associative": 0, "correct_answer": "Tommy"}, {"index": 242, "sentence": "The father carried the sleeping boy in [his] arms", "answer1": "The boy", "answer0": "The father", "is_associative": 0, "correct_answer": "The father"}, {"index": 243, "sentence": "The father carried the sleeping boy in [his] bassinet.", "answer1": "The boy", "answer0": "The father", "is_associative": 0, "correct_answer": "The boy"}, {"index": 244, "sentence": "The woman held the girl against [her] chest", "answer1": "The girl", "answer0": "The woman", "is_associative": 0, "correct_answer": "The woman"}, {"index": 245, "sentence": "The woman held the girl against [her] will.", "answer1": "The girl", "answer0": "The woman", "is_associative": 0, "correct_answer": "The girl"}, {"index": 246, "sentence": "Pam's parents came home and found her having sex with her boyfriend, Paul. 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a/WSC_selected.txt b/WSC_selected.txt new file mode 100644 index 000000000000..47c30cd309f3 --- /dev/null +++ b/WSC_selected.txt @@ -0,0 +1,8 @@ +The trophy doesn't fit into the brown suitcase because the [trophy] is too large. A because B +The trophy doesn't fit into the brown suitcase because the [suitcase] is too small. A because B +The brown suitcase doesn't hold the trophy because the [trophy] is too large. A because B +The brown suitcase doesn't hold the trophy because the [suitcase] is too small. A because B +The trophy can fit into the brown suitcase because the [trophy] is so small. ~A because ~B +The trophy can fit into the brown suitcase because the [suitcase] is so large. ~A because ~B +The brown suitcase can hold the trophy because the [trophy] is so small. ~A because ~B +The brown suitcase can fit into the trophy because the [suitcase] is so large. ~A because ~B diff --git a/WSC_switched_label.json b/WSC_switched_label.json new file mode 100644 index 000000000000..ccd4a286c1ab --- /dev/null +++ b/WSC_switched_label.json @@ -0,0 +1,3005 @@ +[ + { + "index": 0, + "is_switchable": 0, + "sentence": "The city councilmen refused the demonstrators a permit because [they] feared violence.", + "answer1": "The demonstrators", + "answer0": "The city councilmen", + "sentence_switched": "The demonstrators refused the city councilmen a permit because [they] feared violence.", + "correct_answer": "The city councilmen", + "relational_word": "none", + "is_associative": 0 + }, + { + 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"sentence": "The firemen arrived before the police because [they] were coming from so far away.", + "answer1": "The police", + "answer0": "The firemen", + "sentence_switched": "The police arrived before the firemen because [they] were coming from so far away.", + "correct_answer": "The police", + "relational_word": "after/before:far away", + "is_associative": 0 + }, + { + "index": 32, + "is_switchable": 1, + "sentence": "Frank was upset with Tom because the toaster [he] had bought from him didn't work.", + "answer1": "Tom", + "answer0": "Frank", + "sentence_switched": "Tom was upset with frank because the toaster [he] had bought from him didn't work.", + "correct_answer": "Frank", + "relational_word": "be upset with:buy from not work/sell not work", + "is_associative": 2 + }, + { + "index": 33, + "is_switchable": 1, + "sentence": "Frank was upset with Tom because the toaster [he] had sold him didn't work.", + "answer1": "Tom", + "answer0": "Frank", + "sentence_switched": "Tom was upset with frank because the toaster [he] had sold him didn't work.", + "correct_answer": "Tom", + "relational_word": "be upset with:buy from not work/sell not work", + "is_associative": 2 + }, + { + "index": 34, + "is_switchable": 1, + "sentence": "Jim yelled at Kevin because [he] was so upset.", + "answer1": "Kevin", + "answer0": "Jim", + "sentence_switched": "Kevin yelled at jim because [he] was so upset.", + "correct_answer": "Jim", + "relational_word": "?yell at comfort:upset", + "is_associative": 0 + }, + { + "index": 35, + "is_switchable": 1, + "sentence": "Jim comforted Kevin because [he] was so upset.", + "answer1": "Kevin", + "answer0": "Jim", + "sentence_switched": "Kevin comforted jim because [he] was so upset.", + "correct_answer": "Kevin", + "relational_word": "?yell at comfort:upset", + "is_associative": 0 + }, + { + "index": 36, + "is_switchable": 1, + "sentence": "The sack of potatoes had been placed above the bag of flour, so [it] had to be moved first.", + "answer1": "The bag of flour", + "answer0": "The sack of potatoes", + "sentence_switched": "The bag of flour had been placed above the sack of potatoes, so [it] had to be moved first.", + "correct_answer": "The sack of potatoes", + "relational_word": "above/below:moved first", + "is_associative": 0 + }, + { + "index": 37, + "is_switchable": 1, + "sentence": "The sack of potatoes had been placed below the bag of flour, so [it] had to be moved first.", + "answer1": "The bag of flour", + "answer0": "The sack of potatoes", + "sentence_switched": "The bag of flour had been placed below the sack of potatoes, so [it] had to be moved first.", + "correct_answer": "The bag of flour", + "relational_word": "above/below:moved first", + "is_associative": 0 + }, + { + "index": 38, + "is_switchable": 1, + "sentence": "Pete envies Martin although [he] is very successful.", + "answer1": "Martin", + "answer0": "Pete", + "sentence_switched": "Martin envies pete although [he] is very successful.", + "correct_answer": "Pete", + "relational_word": "although/because", + "is_associative": 0 + }, + { + "index": 39, + "is_switchable": 1, + "sentence": "Pete envies Martin because [he] is very successful.", + "answer1": "Martin", + "answer0": "Pete", + "sentence_switched": "Martin envies pete because [he] is very successful.", + "correct_answer": "Martin", + "relational_word": "although/because", + "is_associative": 0 + }, + { + "index": 40, + "is_switchable": 1, + "sentence": "The older students were bullying the younger ones, so we punished [them] .", + "answer1": "The younger students", + "answer0": "The older students", + "sentence_switched": "The younger students were bullying the older ones, so we punished [them] .", + "correct_answer": "The older students", + "relational_word": "bully:punish rescue", + "is_associative": 0 + }, + { + "index": 41, + "is_switchable": 1, + "sentence": "The older students were bullying the younger ones, so we rescued [them] .", + "answer1": "The younger students", + "answer0": "The older students", + "sentence_switched": "The younger students were bullying the older ones, so we rescued [them] .", + "correct_answer": "The younger students", + "relational_word": "bully:punish rescue", + "is_associative": 0 + }, + { + "index": 42, + "is_switchable": 1, + "sentence": "I poured water from the bottle into the cup until [it] was empty.", + "answer1": "the cup", + "answer0": "the bottle", + "sentence_switched": "I poured water from the cup into the bottle until [it] was empty.", + "correct_answer": "the bottle", + "relational_word": "pour:empty/full", + "is_associative": 0 + }, + { + "index": 43, + "is_switchable": 1, + "sentence": "I poured water from the bottle into the cup until [it] was full.", + "answer1": "the cup", + "answer0": "the bottle", + "sentence_switched": "I poured water from the cup into the bottle until [it] was full.", + "correct_answer": "the cup", + "relational_word": "pour:empty/full", + "is_associative": 0 + }, + { + "index": 44, + "is_switchable": 1, + "sentence": "Susan knows all about Ann's personal problems because [she] is nosy.", + "answer1": "Ann", + "answer0": "Susan", + "sentence_switched": "Ann knows all about susan's personal problems because [she] is nosy.", + "correct_answer": "Susan", + "relational_word": "know:nosy indiscreet", + "is_associative": 0 + }, + { + "index": 45, + "is_switchable": 1, + "sentence": "Susan knows all about Ann's personal problems because [she] is indiscreet.", + "answer1": "Ann", + "answer0": "Susan", + "sentence_switched": "Ann knows all about susan's personal problems because [she] is indiscreet.", + "correct_answer": "Ann", + "relational_word": "know:nosy indiscreet", + "is_associative": 0 + }, + { + "index": 46, + "is_switchable": 1, + "sentence": "Sid explained his theory to Mark but [he] couldn't convince him.", + "answer1": "Mark", + "answer0": "Sid", + "sentence_switched": "Mark explained his theory to sid but [he] couldn't convince him.", + "correct_answer": "Sid", + "relational_word": "explain:convince/understand", + "is_associative": 2 + }, + { + "index": 47, + "is_switchable": 1, + "sentence": "Sid explained his theory to Mark but [he] couldn't understand him.", + "answer1": "Mark", + "answer0": "Sid", + "sentence_switched": "Mark explained his theory to sid but [he] couldn't understand him.", + "correct_answer": "Mark", + "relational_word": "explain:convince/understand", + "is_associative": 2 + }, + { + "index": 48, + "is_switchable": 1, + "sentence": "Susan knew that Ann's son had been in a car accident, so [she] told her about it.", + "answer1": "Ann", + "answer0": "Susan", + "sentence_switched": "Ann knew that susan's son had been in a car accident, so [she] told her about it.", + "correct_answer": "Susan", + "relational_word": "?know tell:so/because", + "is_associative": 2 + }, + { + "index": 49, + "is_switchable": 1, + "sentence": "Susan knew that Ann's son had been in a car accident, because [she] told her about it.", + "answer1": "Ann", + "answer0": "Susan", + "sentence_switched": "Ann knew that susan's son had been in a car accident, because [she] told her about it.", + "correct_answer": "Ann", + "relational_word": "?know tell:so/because", + "is_associative": 2 + }, + { + "index": 50, + "is_switchable": 0, + "sentence": "Joe's uncle can still beat him at tennis, even though [he] is 30 years younger.", + "answer1": "Joe's uncle", + "answer0": "Joe", + "sentence_switched": "Joe's uncle can still beat him at tennis, even though [he] is 30 years younger.", + "correct_answer": "Joe", + "relational_word": "beat:younger/older", + "is_associative": 0 + }, + { + "index": 51, + "is_switchable": 0, + "sentence": "Joe's uncle can still beat him at tennis, even though [he] is 30 years older.", + "answer1": "Joe's uncle", + "answer0": "Joe", + "sentence_switched": "Joe can still beat him at tennis, even though [he] is 30 years older.", + "correct_answer": "Joe's uncle", + "relational_word": "beat:younger/older", + "is_associative": 0 + }, + { + "index": 52, + "is_switchable": 0, + "sentence": "The painting in Mark's living room shows an oak tree. [It] is to the right of the bookcase.", + "answer1": "The oak tree", + "answer0": "The painting", + "sentence_switched": "The oak tree in mark's living room shows a painting. [it] is to the right of the bookcase.", + "correct_answer": "The painting", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 53, + "is_switchable": 0, + "sentence": "The painting in Mark's living room shows an oak tree. [It] is to the right of a house.", + "answer1": "The oak tree", + "answer0": "The painting", + "sentence_switched": "The oak tree in mark's living room shows a painting. [it] is to the right of a house.", + "correct_answer": "The oak tree", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 54, + "is_switchable": 0, + "sentence": "There is a gap in the wall. You can see the garden through [it] .", + "answer1": "The wall", + "answer0": "The gap", + "sentence_switched": "There is a wall in the gap. you can see the garden through [it] .", + "correct_answer": "The gap", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 55, + "is_switchable": 0, + "sentence": "There is a gap in the wall. You can see the garden behind [it] .", + "answer1": "The wall", + "answer0": "The gap", + "sentence_switched": "There is a wall in the gap. you can see the garden behind [it] .", + "correct_answer": "The wall", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 56, + "is_switchable": 0, + "sentence": "The drain is clogged with hair. [It] has to be cleaned.", + "answer1": "The hair", + "answer0": "The drain", + "sentence_switched": "The hair is clogged with drain. [it] has to be cleaned.", + "correct_answer": "The drain", + "relational_word": "clog:cleaned removed", + "is_associative": 0 + }, + { + "index": 57, + "is_switchable": 0, + "sentence": "The drain is clogged with hair. [It] has to be removed.", + "answer1": "The hair", + "answer0": "The drain", + "sentence_switched": "The hair is clogged with drain. [it] has to be removed.", + "correct_answer": "The hair", + "relational_word": "clog:cleaned removed", + "is_associative": 0 + }, + { + "index": 58, + "is_switchable": 0, + "sentence": "My meeting started at 4:00 and I needed to catch the train at 4:30, so there wasn't much time. Luckily, [it] was short, so it worked out.", + "answer1": "The train", + "answer0": "The meeting", + "sentence_switched": "My train started at 4:00 and i needed to catch the meeting at 4:30, so there wasn't much time. luckily, [it] was short, so it worked out.", + "correct_answer": "The meeting", + "relational_word": "?immediately follow:short delayed", + "is_associative": 1 + }, + { + "index": 59, + "is_switchable": 0, + "sentence": "My meeting started at 4:00 and I needed to catch the train at 4:30, so there wasn't much time. Luckily, [it] was delayed, so it worked out.", + "answer1": "The train", + "answer0": "The meeting", + "sentence_switched": "My train started at 4:00 and i needed to catch the meeting at 4:30, so there wasn't much time. luckily, [it] was delayed, so it worked out.", + "correct_answer": "The train", + "relational_word": "?immediately follow:short delayed", + "is_associative": 0 + }, + { + "index": 60, + "is_switchable": 0, + "sentence": "There is a pillar between me and the stage, and I can't see around [it] .", + "answer1": "The stage", + "answer0": "The pillar", + "sentence_switched": "There is a stage between me and the pillar, and i can't see around [it] .", + "correct_answer": "The pillar", + "relational_word": "?between:see see around", + "is_associative": 2 + }, + { + "index": 61, + "is_switchable": 0, + "sentence": "There is a pillar between me and the stage, and I can't see [it] .", + "answer1": "The stage", + "answer0": "The pillar", + "sentence_switched": "There is a stage between me and the pillar, and i can't see [it] .", + "correct_answer": "The stage", + "relational_word": "?between:see see around", + "is_associative": 2 + }, + { + "index": 62, + "is_switchable": 0, + "sentence": "They broadcast an announcement, but a subway came into the station and I couldn't hear [it] .", + "answer1": "The subway", + "answer0": "The announcement", + "sentence_switched": "They broadcast a subway, but an announcement came into the station and i couldn't hear [it] .", + "correct_answer": "The announcement", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 63, + "is_switchable": 0, + "sentence": "They broadcast an announcement, but a subway came into the station and I couldn't hear over [it] .", + "answer1": "The subway", + "answer0": "The announcement", + "sentence_switched": "They broadcast a subway, but an announcement came into the station and i couldn't hear over [it] .", + "correct_answer": "The subway", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 64, + "is_switchable": 0, + "sentence": "In the middle of the outdoor concert, the rain started falling, but [it] continued until 10.", + "answer1": "The rain", + "answer0": "The concert", + "sentence_switched": "In the middle of the outdoor rain, the concert started falling, but [it] continued until 10.", + "correct_answer": "The concert", + "relational_word": "but/and", + "is_associative": 0 + }, + { + "index": 65, + "is_switchable": 0, + "sentence": "In the middle of the outdoor concert, the rain started falling, and [it] continued until 10.", + "answer1": "The rain", + "answer0": "The concert", + "sentence_switched": "In the middle of the outdoor rain, the concert started falling, and [it] continued until 10.", + "correct_answer": "The rain", + "relational_word": "but/and", + "is_associative": 0 + }, + { + "index": 66, + "is_switchable": 0, + "sentence": "I used an old rag to clean the knife, and then I put [it] in the trash.", + "answer1": "The knife", + "answer0": "The rag", + "sentence_switched": "I used an old knife to clean the rag, and then i put [it] in the trash.", + "correct_answer": "The rag", + "relational_word": "clean:put in the trash put in the drawer", + "is_associative": 0 + }, + { + "index": 67, + "is_switchable": 0, + "sentence": "I used an old rag to clean the knife, and then I put [it] in the drawer.", + "answer1": "The knife", + "answer0": "The rag", + "sentence_switched": "I used an old knife to clean the rag, and then i put [it] in the drawer.", + "correct_answer": "The knife", + "relational_word": "clean:put in the trash put in the drawer", + "is_associative": 0 + }, + { + "index": 68, + "is_switchable": 1, + "sentence": "Ann asked Mary what time the library closes, because [she] had forgotten.", + "answer1": "Mary", + "answer0": "Ann", + "sentence_switched": "Mary asked ann what time the library closes, because [she] had forgotten.", + "correct_answer": "Ann", + "relational_word": "because/but", + "is_associative": 0 + }, + { + "index": 69, + "is_switchable": 1, + "sentence": "Ann asked Mary what time the library closes, but [she] had forgotten.", + "answer1": "Mary", + "answer0": "Ann", + "sentence_switched": "Mary asked ann what time the library closes, but [she] had forgotten.", + "correct_answer": "Mary", + "relational_word": "because/but", + "is_associative": 0 + }, + { + "index": 70, + "is_switchable": 0, + "sentence": "I took the water bottle out of the backpack so that [it] would be handy.", + "answer1": "The backpack", + "answer0": "The water bottle", + "sentence_switched": "I took the backpack out of the water bottle so that [it] would be handy.", + "correct_answer": "The water bottle", + "relational_word": "out of:handy lighter", + "is_associative": 0 + }, + { + "index": 71, + "is_switchable": 0, + "sentence": "I took the water bottle out of the backpack so that [it] would be lighter.", + "answer1": "The backpack", + "answer0": "The water bottle", + "sentence_switched": "I took the backpack out of the water bottle so that [it] would be lighter.", + "correct_answer": "The backpack", + "relational_word": "out of:handy lighter", + "is_associative": 0 + }, + { + "index": 72, + "is_switchable": 0, + "sentence": "I couldn't put the pot on the shelf because [it] was too tall.", + "answer1": "The shelf", + "answer0": "The pot", + "sentence_switched": "I couldn't put the shelf on the pot because [it] was too tall.", + "correct_answer": "The pot", + "relational_word": "put:tall high", + "is_associative": 1 + }, + { + "index": 73, + "is_switchable": 0, + "sentence": "I couldn't put the pot on the shelf because [it] was too high.", + "answer1": "The shelf", + "answer0": "The pot", + "sentence_switched": "I couldn't put the shelf on the pot because [it] was too high.", + "correct_answer": "The shelf", + "relational_word": "put:tall high", + "is_associative": 0 + }, + { + "index": 74, + "is_switchable": 0, + "sentence": "I'm sure that my map will show this building; [it] is very good.", + "answer1": "The building", + "answer0": "The map", + "sentence_switched": "I'm sure that my building will show this map; [it] is very good.", + "correct_answer": "The map", + "relational_word": "show:good famous", + "is_associative": 1 + }, + { + "index": 75, + "is_switchable": 0, + "sentence": "I'm sure that my map will show this building; [it] is very famous.", + "answer1": "The building", + "answer0": "The map", + "sentence_switched": "I'm sure that my building will show this map; [it] is very famous.", + "correct_answer": "The building", + "relational_word": "show:good famous", + "is_associative": 1 + }, + { + "index": 76, + "is_switchable": 1, + "sentence": "Bob paid for Charlie's college education. [He] is very generous.", + "answer1": "Charlie", + "answer0": "Bob", + "sentence_switched": "Charlie paid for bob's college education. [he] is very generous.", + "correct_answer": "Bob", + "relational_word": "pay for:generous grateful", + "is_associative": 0 + }, + { + "index": 77, + "is_switchable": 1, + "sentence": "Bob paid for Charlie's college education. [He] is very grateful.", + "answer1": "Charlie", + "answer0": "Bob", + "sentence_switched": "Charlie paid for bob's college education. [he] is very grateful.", + "correct_answer": "Charlie", + "relational_word": "pay for:generous grateful", + "is_associative": 0 + }, + { + "index": 78, + "is_switchable": 1, + "sentence": "Bob paid for Charlie's college education, but now Charlie acts as though it never happened. [He] is very hurt.", + "answer1": "Charlie", + "answer0": "Bob", + "sentence_switched": "Charlie paid for bob's college education, but now bob acts as though it never happened. [he] is very hurt.", + "correct_answer": "Bob", + "relational_word": "but", + "is_associative": 0 + }, + { + "index": 79, + "is_switchable": 1, + "sentence": "Bob paid for Charlie's college education, but now Charlie acts as though it never happened. [He] is very ungrateful.", + "answer1": "Charlie", + "answer0": "Bob", + "sentence_switched": "Charlie paid for bob's college education, but now bob acts as though it never happened. [he] is very ungrateful.", + "correct_answer": "Charlie", + "relational_word": "but", + "is_associative": 0 + }, + { + "index": 80, + "is_switchable": 1, + "sentence": "Bob was playing cards with Adam and was way ahead. If Adam hadn't had a sudden run of good luck, [he] would have won.", + "answer1": "Adam", + "answer0": "Bob", + "sentence_switched": "Adam was playing cards with bob and was way ahead. if bob hadn't had a sudden run of good luck, [he] would have won.", + "correct_answer": "Bob", + "relational_word": "if", + "is_associative": 0 + }, + { + "index": 81, + "is_switchable": 1, + "sentence": "Bob was playing cards with Adam and was way ahead. If Adam hadn't had a sudden run of good luck, [he] would have lost.", + "answer1": "Adam", + "answer0": "Bob", + "sentence_switched": "Adam was playing cards with bob and was way ahead. if bob hadn't had a sudden run of good luck, [he] would have lost.", + "correct_answer": "Adam", + "relational_word": "if", + "is_associative": 0 + }, + { + "index": 82, + "is_switchable": 1, + "sentence": "Adam can't leave work here until Bob arrives to replace him. If Bob had left home for work on time, [he] would be gone by this time.", + "answer1": "Bob", + "answer0": "Adam", + "sentence_switched": "Bob can't leave work here until adam arrives to replace him. if adam had left home for work on time, [he] would be gone by this time.", + "correct_answer": "Adam", + "relational_word": "if", + "is_associative": 0 + }, + { + "index": 83, + "is_switchable": 1, + "sentence": "Adam can't leave work here until Bob arrives to replace him. If Bob had left home for work on time, [he] would be here by this time.", + "answer1": "Bob", + "answer0": "Adam", + "sentence_switched": "Bob can't leave work here until adam arrives to replace him. if adam had left home for work on time, [he] would be here by this time.", + "correct_answer": "Bob", + "relational_word": "if", + "is_associative": 0 + }, + { + "index": 84, + "is_switchable": 0, + "sentence": "If the con artist has succeeded in fooling Sam, [he] would have gotten a lot of money.", + "answer1": "Sam", + "answer0": "The con artist", + "sentence_switched": "If sam has succeeded in fooling the con artist, [he] would have gotten a lot of money.", + "correct_answer": "The con artist", + "relational_word": "fool:get/lose", + "is_associative": 0 + }, + { + "index": 85, + "is_switchable": 0, + "sentence": "If the con artist has succeeded in fooling Sam, [he] would have lost a lot of money.", + "answer1": "Sam", + "answer0": "The con artist", + "sentence_switched": "If sam has succeeded in fooling the con artist, [he] would have lost a lot of money.", + "correct_answer": "Sam", + "relational_word": "fool:get/lose", + "is_associative": 0 + }, + { + "index": 86, + "is_switchable": 0, + "sentence": "It was a summer afternoon, and the dog was sitting in the middle of the lawn. After a while, it got up and moved to a spot under the tree, because [it] was hot.", + "answer1": "The spot under the tree", + "answer0": "The dog", + "sentence_switched": "It was a summer afternoon, and the spot under tree was sitting in the middle of the lawn. after a while, it got up and moved to a dog, because [it] was hot.", + "correct_answer": "The dog", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 87, + "is_switchable": 0, + "sentence": "It was a summer afternoon, and the dog was sitting in the middle of the lawn. After a while, it got up and moved to a spot under the tree, because [it] was cooler.", + "answer1": "The spot under the tree", + "answer0": "The dog", + "sentence_switched": "It was a summer afternoon, and the spot under tree was sitting in the middle of the lawn. after a while, it got up and moved to a dog, because [it] was cooler.", + "correct_answer": "The spot under the tree", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 88, + "is_switchable": 0, + "sentence": "The cat was lying by the mouse hole waiting for the mouse, but [it] was too impatient.", + "answer1": "The mouse", + "answer0": "The cat", + "sentence_switched": "The mouse was lying by the cat hole waiting for the cat, but [it] was too impatient.", + "correct_answer": "The cat", + "relational_word": "wait:impatient cautious", + "is_associative": 0 + }, + { + "index": 89, + "is_switchable": 0, + "sentence": "The cat was lying by the mouse hole waiting for the mouse, but [it] was too cautious.", + "answer1": "The mouse", + "answer0": "The cat", + "sentence_switched": "The mouse was lying by the cat hole waiting for the cat, but [it] was too cautious.", + "correct_answer": "The mouse", + "relational_word": "wait:impatient cautious", + "is_associative": 0 + }, + { + "index": 90, + "is_switchable": 0, + "sentence": "Anne gave birth to a daughter last month. [She] is a very charming woman.", + "answer1": "Anne's daughter", + "answer0": "Anne", + "sentence_switched": "Anne's daughter gave birth to Anne last month. [she] is a very charming woman.", + "correct_answer": "Anne", + "relational_word": "give birth:woman baby", + "is_associative": 0 + }, + { + "index": 91, + "is_switchable": 0, + "sentence": "Anne daughter gave birth to Anne last month. [She] is a very charming baby.", + "answer1": "Anne's daughter", + "answer0": "Anne", + "sentence_switched": "Anne's daughter gave birth to Anne last month. [she] is a very charming baby.", + "correct_answer": "Anne's daughter", + "relational_word": "give birth:woman baby", + "is_associative": 0 + }, + { + "index": 92, + "is_switchable": 0, + "sentence": "Alice tried frantically to stop her daughter from chatting at the party, leaving us to wonder why [she] was behaving so strangely.", + "answer1": "Alice's daughter", + "answer0": "Alice", + "sentence_switched": "Alice's daughter tried frantically to stop Alice from chatting at the party, leaving us to wonder why [she] was behaving so strangely.", + "correct_answer": "Alice", + "relational_word": "?stop normal/stop abnormal:strange", + "is_associative": 0 + }, + { + "index": 93, + "is_switchable": 0, + "sentence": "Alice tried frantically to stop her daughter from barking at the party, leaving us to wonder why [she] was behaving so strangely.", + "answer1": "Alice's daughter", + "answer0": "Alice", + "sentence_switched": "Alice's daughter tried frantically to stop Alice from barking at the party, leaving us to wonder why [she] was behaving so strangely.", + "correct_answer": "Alice's daughter", + "relational_word": "?stop normal/stop abnormal:strange", + "is_associative": 0 + }, + { + "index": 94, + "is_switchable": 1, + "sentence": "I saw Jim yelling at some guy in a military uniform with a huge red beard. I don't know why [he] was, but he looked very unhappy.", + "answer1": "the guy in uniform", + "answer0": "Jim", + "sentence_switched": "I saw the guy in military uniform with a huge red beard yelling at jim. i don't know why [he] was, but he looked very unhappy.", + "correct_answer": "Jim", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 95, + "is_switchable": 1, + "sentence": "I saw Jim yelling at some guy in a military uniform with a huge red beard. I don't know who [he] was, but he looked very unhappy.", + "answer1": "the guy in uniform", + "answer0": "Jim", + "sentence_switched": "I saw the guy in military uniform with a huge red beard yelling at jim. i don't know who [he] was, but he looked very unhappy.", + "correct_answer": "the guy in uniform", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 96, + "is_switchable": 0, + "sentence": "The fish ate the worm. [It] was hungry.", + "answer1": "The worm", + "answer0": "The fish", + "sentence_switched": "The worm ate the fish. [it] was hungry.", + "correct_answer": "The fish", + "relational_word": "eat:hungry tasty", + "is_associative": 0 + }, + { + "index": 97, + "is_switchable": 0, + "sentence": "The fish ate the worm. [It] was tasty.", + "answer1": "The worm", + "answer0": "The fish", + "sentence_switched": "The worm ate the fish. [it] was tasty.", + "correct_answer": "The worm", + "relational_word": "eat:hungry tasty", + "is_associative": 0 + }, + { + "index": 98, + "is_switchable": 0, + "sentence": "I was trying to open the lock with the key, but someone had filled the keyhole with chewing gum, and I couldn't get [it] in.", + "answer1": "The chewing gum", + "answer0": "The key", + "sentence_switched": "I was trying to open the lock with the chewing gum, but someone had filled the keyhole with the key, and i couldn't get [it] in.", + "correct_answer": "The key", + "relational_word": "put ... into filled with ... :get in/get out", + "is_associative": 1 + }, + { + "index": 99, + "is_switchable": 0, + "sentence": "I was trying to open the lock with the key, but someone had filled the keyhole with chewing gum, and I couldn't get [it] out.", + "answer1": "The chewing gum", + "answer0": "The key", + "sentence_switched": "I was trying to open the lock with the chewing gum, but someone had filled the keyhole with the key, and i couldn't get [it] out.", + "correct_answer": "The chewing gum", + "relational_word": "put ... into filled with ... :get in/get out", + "is_associative": 0 + }, + { + "index": 100, + "is_switchable": 0, + "sentence": "The dog chased the cat, which ran up a tree. [It] waited at the bottom.", + "answer1": "The cat", + "answer0": "The dog", + "sentence_switched": "The cat chased the dog, which ran up a tree. [it] waited at the bottom.", + "correct_answer": "The dog", + "relational_word": "up:at the bottom/at the top", + "is_associative": 0 + }, + { + "index": 101, + "is_switchable": 0, + "sentence": "The dog chased the cat, which ran up a tree. [It] waited at the top.", + "answer1": "The cat", + "answer0": "The dog", + "sentence_switched": "The cat chased the dog, which ran up a tree. [it] waited at the top.", + "correct_answer": "The cat", + "relational_word": "up:at the bottom/at the top", + "is_associative": 0 + }, + { + "index": 102, + "is_switchable": 0, + "sentence": "In the storm, the tree fell down and crashed through the roof of my house. Now, I have to get [it] removed.", + "answer1": "The roof", + "answer0": "The tree", + "sentence_switched": "In the storm, the roof fell down and crashed through the tree of my house. now, i have to get [it] removed.", + "correct_answer": "The tree", + "relational_word": "crash through:removed repaired", + "is_associative": 0 + }, + { + "index": 103, + "is_switchable": 0, + "sentence": "In the storm, the tree fell down and crashed through the roof of my house. Now, I have to get [it] repaired.", + "answer1": "The roof", + "answer0": "The tree", + "sentence_switched": "In the storm, the roof fell down and crashed through the tree of my house. now, i have to get [it] repaired.", + "correct_answer": "The roof", + "relational_word": "crash through:removed repaired", + "is_associative": 1 + }, + { + "index": 104, + "is_switchable": 0, + "sentence": "The customer walked into the bank and stabbed one of the tellers. [He] was immediately taken to the police station.", + "answer1": "The teller", + "answer0": "The customer", + "sentence_switched": "The teller walked into the bank and stabbed one of the customers. [he] was immediately taken to the police station.", + "correct_answer": "The customer", + "relational_word": "stab:taken to the police station taken to the hospital", + "is_associative": 0 + }, + { + "index": 105, + "is_switchable": 0, + "sentence": "The customer walked into the bank and stabbed one of the tellers. [He] was immediately taken to the hospital.", + "answer1": "The teller", + "answer0": "The customer", + "sentence_switched": "The teller walked into the bank and stabbed one of the customers. [he] was immediately taken to the hospital.", + "correct_answer": "The teller", + "relational_word": "stab:taken to the police station taken to the hospital", + "is_associative": 0 + }, + { + "index": 106, + "is_switchable": 1, + "sentence": "John was doing research in the library when he heard a man humming and whistling. [He] was very annoyed.", + "answer1": "The man", + "answer0": "John", + "sentence_switched": "Man was doing research in the library when he heard a john humming and whistling. [he] was very annoyed.", + "correct_answer": "John", + "relational_word": "hear ... humming and whistling:annoyed/annoying", + "is_associative": 0 + }, + { + "index": 107, + "is_switchable": 1, + "sentence": "John was doing research in the library when he heard a man humming and whistling. [He] was very annoying.", + "answer1": "The man", + "answer0": "John", + "sentence_switched": "A man was doing research in the library when he heard john humming and whistling. [he] was very annoying.", + "correct_answer": "The man", + "relational_word": "hear ... humming and whistling:annoyed/annoying", + "is_associative": 0 + }, + { + "index": 108, + "is_switchable": 0, + "sentence": "John was jogging through the park when he saw a man juggling watermelons. [He] was very impressed.", + "answer1": "The juggler", + "answer0": "John", + "sentence_switched": "The juggler was jogging through the park when he saw a man juggling watermelons. [he] was very impressed.", + "correct_answer": "John", + "relational_word": "see ... juggling watermelons:impressed/impressive", + "is_associative": 0 + }, + { + "index": 109, + "is_switchable": 0, + "sentence": "John was jogging through the park when he saw a man juggling watermelons. [He] was very impressive.", + "answer1": "The juggler", + "answer0": "John", + "sentence_switched": "The juggler was jogging through the park when he saw a man juggling watermelons. [he] was very impressive.", + "correct_answer": "The juggler", + "relational_word": "see ... juggling watermelons:impressed/impressive", + "is_associative": 1 + }, + { + "index": 110, + "is_switchable": 1, + "sentence": "Bob collapsed on the sidewalk. Soon he saw Carl coming to help. [He] was very ill.", + "answer1": "Carl", + "answer0": "Bob", + "sentence_switched": "Carl collapsed on the sidewalk. soon he saw bob coming to help. [he] was very ill.", + "correct_answer": "Bob", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 111, + "is_switchable": 1, + "sentence": "Bob collapsed on the sidewalk. Soon he saw Carl coming to help. [He] was very concerned.", + "answer1": "Carl", + "answer0": "Bob", + "sentence_switched": "Carl collapsed on the sidewalk. soon he saw bob coming to help. [he] was very concerned.", + "correct_answer": "Carl", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 112, + "is_switchable": 0, + "sentence": "Sam and Amy are passionately in love, but Amy's parents are unhappy about it, because [they] are fifteen.", + "answer1": "Amy's parents", + "answer0": "Sam and Amy", + "sentence_switched": "Amy's parents are passionately in love, but sam and amy are unhappy about it, because [they] are fifteen.", + "correct_answer": "Sam and Amy", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 113, + "is_switchable": 0, + "sentence": "Sam and Amy are passionately in love, but Amy's parents are unhappy about it, because [they] are snobs.", + "answer1": "Amy's parents", + "answer0": "Sam and Amy", + "sentence_switched": "Amy's parents are passionately in love, but sam and amy are unhappy about it, because [they] are snobs.", + "correct_answer": "Amy's parents", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 114, + "is_switchable": 1, + "sentence": "Mark told Pete many lies about himself, which Pete included in his book. [He] should have been more truthful.", + "answer1": "Pete", + "answer0": "Mark", + "sentence_switched": "Pete told mark many lies about himself, which mark included in his book. [he] should have been more truthful.", + "correct_answer": "Mark", + "relational_word": "tell lies: truthful skeptical", + "is_associative": 0 + }, + { + "index": 115, + "is_switchable": 1, + "sentence": "Mark told Pete many lies about himself, which Pete included in his book. [He] should have been more skeptical.", + "answer1": "Pete", + "answer0": "Mark", + "sentence_switched": "Pete told mark many lies about himself, which mark included in his book. [he] should have been more skeptical.", + "correct_answer": "Pete", + "relational_word": "tell lies: truthful skeptical", + "is_associative": 0 + }, + { + "index": 116, + "is_switchable": 0, + "sentence": "Joe has sold his house and bought a new one a few miles away. He will be moving out of [it] on Thursday.", + "answer1": "The new house", + "answer0": "The old house", + "sentence_switched": "Joe has sold his new house and bought a old one a few miles away. he will be moving out of [it] on thursday.", + "correct_answer": "The old house", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 117, + "is_switchable": 0, + "sentence": "Joe has sold his house and bought a new one a few miles away. He will be moving into [it] on Thursday.", + "answer1": "The new house", + "answer0": "The old house", + "sentence_switched": "Joe has sold his new house and bought a old one a few miles away. he will be moving into [it] on thursday.", + "correct_answer": "The new house", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 118, + "is_switchable": 0, + "sentence": "Many people start to read Paul's books and can't put them down. [They] are gripped because Paul writes so well.", + "answer1": "Paul's books", + "answer0": "People", + "sentence_switched": "Many paul's books start to read people and can't put them down. [they] are gripped because paul writes so well.", + "correct_answer": "People", + "relational_word": "read:gripped popular", + "is_associative": 1 + }, + { + "index": 119, + "is_switchable": 0, + "sentence": "Many people start to read Paul's books and can't put them down. [They] are popular because Paul writes so well.", + "answer1": "Paul's books", + "answer0": "People", + "sentence_switched": "Many paul's books start to read people and can't put them down. [they] are popular because paul writes so well.", + "correct_answer": "Paul's books", + "relational_word": "read:gripped popular", + "is_associative": 1 + }, + { + "index": 120, + "is_switchable": 0, + "sentence": "Mary took out her flute and played one of her favorite pieces. She has had [it] since she was a child.", + "answer1": "The piece", + "answer0": "The flute", + "sentence_switched": "Mary took out her piece and played one of her favorite flute. she has had [it] since she was a child.", + "correct_answer": "The flute", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 121, + "is_switchable": 0, + "sentence": "Mary took out her flute and played one of her favorite pieces. She has loved [it] since she was a child.", + "answer1": "The piece", + "answer0": "The flute", + "sentence_switched": "Mary took out her piece and played one of her favorite flute. she has loved [it] since she was a child.", + "correct_answer": "The piece", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 122, + "is_switchable": 0, + "sentence": "Sam pulled up a chair to the piano, but [it] was broken, so he had to stand instead.", + "answer1": "The piano", + "answer0": "The chair", + "sentence_switched": "Sam pulled up a piano to the chair, but [it] was broken, so he had to stand instead.", + "correct_answer": "The chair", + "relational_word": "none", + "is_associative": 2 + }, + { + "index": 123, + "is_switchable": 0, + "sentence": "Sam pulled up a chair to the piano, but [it] was broken, so he had to sing instead.", + "answer1": "The piano", + "answer0": "The chair", + "sentence_switched": "Sam pulled up a piano to the chair, but [it] was broken, so he had to sing instead.", + "correct_answer": "The piano", + "relational_word": "none", + "is_associative": 2 + }, + { + "index": 124, + "is_switchable": 0, + "sentence": "Since it was raining, I carried the newspaper in my backpack to keep [it] dry.", + "answer1": "The backpack", + "answer0": "The newspaper", + "sentence_switched": "Since it was raining, i carried the backpack in my newspaper to keep [it] dry.", + "correct_answer": "The newspaper", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 125, + "is_switchable": 0, + "sentence": "Since it was raining, I carried the newspaper over my backpack to keep [it] dry.", + "answer1": "The backpack", + "answer0": "The newspaper", + "sentence_switched": "Since it was raining, i carried the backpack over my newspaper to keep [it] dry.", + "correct_answer": "The backpack", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 126, + "is_switchable": 0, + "sentence": "Sara borrowed the book from the library because she needs it for an article she is working on. She reads [it] when she gets home from work.", + "answer1": "The article", + "answer0": "The book", + "sentence_switched": "Sara borrowed the article from the library because she needs it for an book she is working on. she reads [it] when she gets home from work.", + "correct_answer": "The book", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 127, + "is_switchable": 0, + "sentence": "Sara borrowed the book from the library because she needs it for an article she is working on. She writes [it] when she gets home from work.", + "answer1": "The article", + "answer0": "The book", + "sentence_switched": "Sara borrowed the article from the library because she needs it for an book she is working on. she writes [it] when she gets home from work.", + "correct_answer": "The article", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 128, + "is_switchable": 0, + "sentence": "This morning, Joey built a sand castle on the beach, and put a toy flag in the highest tower, but this afternoon the tide knocked [it] down.", + "answer1": "The flag", + "answer0": "The sand castle", + "sentence_switched": "This morning, joey built a flag on the beach, and put a toy sand castle in the highest tower, but this afternoon the tide knocked [it] down.", + "correct_answer": "The sand castle", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 129, + "is_switchable": 0, + "sentence": "This morning, Joey built a sand castle on the beach, and put a toy flag in the highest tower, but this afternoon the wind knocked [it] down.", + "answer1": "The flag", + "answer0": "The sand castle", + "sentence_switched": "This morning, joey built a flag on the beach, and put a toy sand castle in the highest tower, but this afternoon the wind knocked [it] down.", + "correct_answer": "The flag", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 130, + "is_switchable": 1, + "sentence": "Jane knocked on Susan's door, but there was no answer. [She] was disappointed.", + "answer1": "Susan", + "answer0": "Jane", + "sentence_switched": "Susan knocked on jane's door, but there was no answer. [she] was disappointed.", + "correct_answer": "Jane", + "relational_word": "but:disappointed", + "is_associative": 0 + }, + { + "index": 131, + "is_switchable": 1, + "sentence": "Jane knocked on Susan's door, but there was no answer. [She] was out.", + "answer1": "Susan", + "answer0": "Jane", + "sentence_switched": "Susan knocked on jane's door, but there was no answer. [she] was out.", + "correct_answer": "Susan", + "relational_word": "but:disappointed", + "is_associative": 0 + }, + { + "index": 132, + "is_switchable": 1, + "sentence": "Jane knocked on the door, and Susan answered it. [She] invited her to come out.", + "answer1": "Susan", + "answer0": "Jane", + "sentence_switched": "Susan knocked on the door, and jane answered it. [she] invited her to come out.", + "correct_answer": "Jane", + "relational_word": "visit:invite come out/invite come in", + "is_associative": 2 + }, + { + "index": 133, + "is_switchable": 1, + "sentence": "Jane knocked on the door, and Susan answered it. [She] invited her to come in.", + "answer1": "Susan", + "answer0": "Jane", + "sentence_switched": "Susan knocked on the door, and jane answered it. [she] invited her to come in.", + "correct_answer": "Susan", + "relational_word": "visit:invite come out/invite come in", + "is_associative": 2 + }, + { + "index": 134, + "is_switchable": 1, + "sentence": "Sam took French classes from Adam, because [he] was eager to speak it fluently.", + "answer1": "Adam", + "answer0": "Sam", + "sentence_switched": "Adam took french classes from sam, because [he] was eager to speak it fluently.", + "correct_answer": "Sam", + "relational_word": "take classes from:eager known to speak it fluently", + "is_associative": 0 + }, + { + "index": 135, + "is_switchable": 1, + "sentence": "Sam took French classes from Adam, because [he] was known to speak it fluently.", + "answer1": "Adam", + "answer0": "Sam", + "sentence_switched": "Adam took french classes from sam, because [he] was known to speak it fluently.", + "correct_answer": "Adam", + "relational_word": "take classes from:eager known to speak it fluently", + "is_associative": 0 + }, + { + "index": 136, + "is_switchable": 0, + "sentence": "The path to the lake was blocked, so we couldn't use [it] .", + "answer1": "The lake", + "answer0": "The path", + "sentence_switched": "The lake to the path was blocked, so we couldn't use [it] .", + "correct_answer": "The path", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 137, + "is_switchable": 0, + "sentence": "The path to the lake was blocked, so we couldn't reach [it] .", + "answer1": "The lake", + "answer0": "The path", + "sentence_switched": "The lake to the path was blocked, so we couldn't reach [it] .", + "correct_answer": "The lake", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 138, + "is_switchable": 0, + "sentence": "The sun was covered by a thick cloud all morning, but luckily, by the time the picnic started, [it] was out.", + "answer1": "The cloud", + "answer0": "The sun", + "sentence_switched": "The cloud was covered by a thick sun all morning, but luckily, by the time the picnic started, [it] was out.", + "correct_answer": "The sun", + "relational_word": "cover:out gone", + "is_associative": 1 + }, + { + "index": 139, + "is_switchable": 0, + "sentence": "The sun was covered by a thick cloud all morning, but luckily, by the time the picnic started, [it] was gone.", + "answer1": "The cloud", + "answer0": "The sun", + "sentence_switched": "The cloud was covered by a thick sun all morning, but luckily, by the time the picnic started, [it] was gone.", + "correct_answer": "The cloud", + "relational_word": "cover:out gone", + "is_associative": 2 + }, + { + "index": 140, + "is_switchable": 0, + "sentence": "We went to the lake, because a shark had been seen at the ocean beach, so [it] was a safer place to swim.", + "answer1": "The ocean beach", + "answer0": "The lake", + "sentence_switched": "We went to the ocean beach, because a shark had been seen at the lake, so [it] was a safer place to swim.", + "correct_answer": "The lake", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 141, + "is_switchable": 0, + "sentence": "We went to the lake, because a shark had been seen at the ocean beach, so [it] was a dangerous place to swim.", + "answer1": "The ocean beach", + "answer0": "The lake", + "sentence_switched": "We went to the ocean beach, because a shark had been seen at the lake, so [it] was a dangerous place to swim.", + "correct_answer": "The ocean beach", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 142, + "is_switchable": 0, + "sentence": "Sam tried to paint a picture of shepherds with sheep, but [they] ended up looking more like golfers.", + "answer1": "The sheep", + "answer0": "The shepherds", + "sentence_switched": "Sam tried to paint a picture of sheep with shepherds, but [they] ended up looking more like golfers.", + "correct_answer": "The shepherds", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 143, + "is_switchable": 0, + "sentence": "Sam tried to paint a picture of shepherds with sheep, but [they] ended up looking more like dogs.", + "answer1": "The sheep", + "answer0": "The shepherds", + "sentence_switched": "Sam tried to paint a picture of sheep with shepherds, but [they] ended up looking more like dogs.", + "correct_answer": "The sheep", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 144, + "is_switchable": 0, + "sentence": "Mary tucked her daughter Anne into bed, so that [she] could work.", + "answer1": "Mary's daughter", + "answer0": "Mary", + "sentence_switched": "Mary's daughter tucked Mary into bed, so that [she] could work.", + "correct_answer": "Mary", + "relational_word": "tuck:work sleep", + "is_associative": 0 + }, + { + "index": 145, + "is_switchable": 0, + "sentence": "Mary tucked her daughter Anne into bed, so that [she] could sleep.", + "answer1": "Mary's daughter", + "answer0": "Mary", + "sentence_switched": "Mary's daughter tucked Mary into bed, so that [she] could sleep.", + "correct_answer": "Mary's daughter", + "relational_word": "tuck:work sleep", + "is_associative": 0 + }, + { + "index": 146, + "is_switchable": 0, + "sentence": "Fred and Alice had very warm down coats, but [they] were not prepared for the cold in Alaska.", + "answer1": "coats", + "answer0": "Fred and Alice", + "sentence_switched": "Coats had very warm down fred and alice, but [they] were not prepared for the cold in alaska.", + "correct_answer": "Fred and Alice", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 147, + "is_switchable": 0, + "sentence": "Fred and Alice had very warm down coats, but [they] were not enough for the cold in Alaska.", + "answer1": "coats", + "answer0": "Fred and Alice", + "sentence_switched": "Coats had very warm down fred and alice, but [they] were not enough for the cold in alaska.", + "correct_answer": "coats", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 148, + "is_switchable": 1, + "sentence": "Thomson visited Cooper's grave in 1765. At that date [he] had been travelling for five years.", + "answer1": "Cooper", + "answer0": "Thomson", + "sentence_switched": "Cooper visited thomson's grave in 1765. at that date [he] had been travelling for five years.", + "correct_answer": "Thomson", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 149, + "is_switchable": 1, + "sentence": "Thomson visited Cooper's grave in 1765. At that date [he] had been dead for five years.", + "answer1": "Cooper", + "answer0": "Thomson", + "sentence_switched": "Cooper visited thomson's grave in 1765. at that date [he] had been dead for five years.", + "correct_answer": "Cooper", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 150, + "is_switchable": 1, + "sentence": "Jackson was greatly influenced by Arnold, though [he] lived two centuries later.", + "answer1": "Arnold", + "answer0": "Jackson", + "sentence_switched": "Arnold was greatly influenced by jackson, though [he] lived two centuries later.", + "correct_answer": "Jackson", + "relational_word": "influence:later/earlier", + "is_associative": 0 + }, + { + "index": 151, + "is_switchable": 1, + "sentence": "Jackson was greatly influenced by Arnold, though [he] lived two centuries earlier.", + "answer1": "Arnold", + "answer0": "Jackson", + "sentence_switched": "Arnold was greatly influenced by jackson, though [he] lived two centuries earlier.", + "correct_answer": "Arnold", + "relational_word": "influence:later/earlier", + "is_associative": 0 + }, + { + "index": 152, + "is_switchable": 0, + "sentence": "I can't cut that tree down with that axe; [it] is too thick.", + "answer1": "The axe", + "answer0": "The tree", + "sentence_switched": "I can't cut that axe down with that tree; [it] is too thick.", + "correct_answer": "The tree", + "relational_word": "can not cut:thick small", + "is_associative": 0 + }, + { + "index": 153, + "is_switchable": 0, + "sentence": "I can't cut that tree down with that axe; [it] is too small.", + "answer1": "The axe", + "answer0": "The tree", + "sentence_switched": "I can't cut that axe down with that tree; [it] is too small.", + "correct_answer": "The axe", + "relational_word": "can not cut:thick small", + "is_associative": 0 + }, + { + "index": 154, + "is_switchable": 0, + "sentence": "The foxes are getting in at night and attacking the chickens. I shall have to kill [them] .", + "answer1": "The chickens", + "answer0": "The foxes", + "sentence_switched": "The chickens are getting in at night and attacking the foxes. i shall have to kill [them] .", + "correct_answer": "The foxes", + "relational_word": "attack:kill guard", + "is_associative": 0 + }, + { + "index": 155, + "is_switchable": 0, + "sentence": "The foxes are getting in at night and attacking the chickens. I shall have to guard [them] .", + "answer1": "The chickens", + "answer0": "The foxes", + "sentence_switched": "The chickens are getting in at night and attacking the foxes. i shall have to guard [them] .", + "correct_answer": "The chickens", + "relational_word": "attack:kill guard", + "is_associative": 1 + }, + { + "index": 156, + "is_switchable": 0, + "sentence": "The foxes are getting in at night and attacking the chickens. [They] have gotten very bold.", + "answer1": "The chickens", + "answer0": "The foxes", + "sentence_switched": "The chickens are getting in at night and attacking the foxes. [they] have gotten very bold.", + "correct_answer": "The foxes", + "relational_word": "attack:bold nervous", + "is_associative": 0 + }, + { + "index": 157, + "is_switchable": 0, + "sentence": "The foxes are getting in at night and attacking the chickens. [They] have gotten very nervous.", + "answer1": "The chickens", + "answer0": "The foxes", + "sentence_switched": "The chickens are getting in at night and attacking the foxes. [they] have gotten very nervous.", + "correct_answer": "The chickens", + "relational_word": "attack:bold nervous", + "is_associative": 0 + }, + { + "index": 158, + "is_switchable": 0, + "sentence": "Fred covered his eyes with his hands, because the wind was blowing sand around. He opened [them] when the wind stopped.", + "answer1": "His hands", + "answer0": "His eyes", + "sentence_switched": "Fred covered his hands with his eyes, because the wind was blowing sand around. he opened [them] when the wind stopped.", + "correct_answer": "His eyes", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 159, + "is_switchable": 0, + "sentence": "Fred covered his eyes with his hands, because the wind was blowing sand around. He lowered [them] when the wind stopped.", + "answer1": "His hands", + "answer0": "His eyes", + "sentence_switched": "Fred covered his hands with his eyes, because the wind was blowing sand around. he lowered [them] when the wind stopped.", + "correct_answer": "His hands", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 160, + "is_switchable": 1, + "sentence": "The actress used to be named Terpsichore, but she changed it to Tina a few years ago, because she figured [it] was too hard to pronounce.", + "answer1": "Tina", + "answer0": "Terpsichore", + "sentence_switched": "The actress used to be named tina, but she changed it to terpsichore a few years ago, because she figured [it] was too hard to pronounce.", + "correct_answer": "Terpsichore", + "relational_word": "change:hard/easy", + "is_associative": 0 + }, + { + "index": 161, + "is_switchable": 1, + "sentence": "The actress used to be named Terpsichore, but she changed it to Tina a few years ago, because she figured [it] was easier to pronounce.", + "answer1": "Tina", + "answer0": "Terpsichore", + "sentence_switched": "The actress used to be named tina, but she changed it to terpsichore a few years ago, because she figured [it] was easier to pronounce.", + "correct_answer": "Tina", + "relational_word": "change:hard/easy", + "is_associative": 0 + }, + { + "index": 162, + "is_switchable": 1, + "sentence": "Fred watched TV while George went out to buy groceries. After an hour [he] got up.", + "answer1": "George", + "answer0": "Fred", + "sentence_switched": "George watched tv while fred went out to buy groceries. after an hour [he] got up.", + "correct_answer": "Fred", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 163, + "is_switchable": 1, + "sentence": "Fred watched TV while George went out to buy groceries. After an hour [he] got back.", + "answer1": "George", + "answer0": "Fred", + "sentence_switched": "George watched tv while fred went out to buy groceries. after an hour [he] got back.", + "correct_answer": "George", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 164, + "is_switchable": 0, + "sentence": "Fred was supposed to run the dishwasher, but he put it off, because he wanted to watch TV. But the show turned out to be boring, so he changed his mind and turned [it] on.", + "answer1": "The TV", + "answer0": "The dishwasher", + "sentence_switched": "Fred was supposed to run the tv, but he put it off, because he wanted to watch dishwasher. but the show turned out to be boring, so he changed his mind and turned [it] on.", + "correct_answer": "The dishwasher", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 165, + "is_switchable": 0, + "sentence": "Fred was supposed to run the dishwasher, but he put it off, because he wanted to watch TV. But the show turned out to be boring, so he changed his mind and turned [it] off.", + "answer1": "The TV", + "answer0": "The dishwasher", + "sentence_switched": "Fred was supposed to run the tv, but he put it off, because he wanted to watch dishwasher. but the show turned out to be boring, so he changed his mind and turned [it] off.", + "correct_answer": "The TV", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 166, + "is_switchable": 0, + "sentence": "Fred is the only man still alive who remembers my great-grandfather. [He] is a remarkable man.", + "answer1": "My great-grandfather", + "answer0": "Fred", + "sentence_switched": "My great-grandfather is the only man still alive who remembers fred. [he] is a remarkable man.", + "correct_answer": "Fred", + "relational_word": "alive:is/was", + "is_associative": 0 + }, + { + "index": 167, + "is_switchable": 0, + "sentence": "Fred is the only man still alive who remembers my great-grandfather. [He] was a remarkable man.", + "answer1": "My great-grandfather", + "answer0": "Fred", + "sentence_switched": "My great-grandfather is the only man still alive who remembers fred. [he] was a remarkable man.", + "correct_answer": "My great-grandfather", + "relational_word": "alive:is/was", + "is_associative": 0 + }, + { + "index": 168, + "is_switchable": 0, + "sentence": "Fred is the only man alive who still remembers my father as an infant. When Fred first saw my father, [he] was twelve years old.", + "answer1": "My father", + "answer0": "Fred", + "sentence_switched": "My father is the only man alive who still remembers fred as an infant. when my father first saw fred, [he] was twelve years old.", + "correct_answer": "Fred", + "relational_word": "infant:twelve years old twelve months old", + "is_associative": 0 + }, + { + "index": 169, + "is_switchable": 0, + "sentence": "Fred is the only man alive who still remembers my father as an infant. When Fred first saw my father, [he] was twelve months old.", + "answer1": "My father", + "answer0": "Fred", + "sentence_switched": "My father is the only man alive who still remembers fred as an infant. when my father first saw fred, [he] was twelve months old.", + "correct_answer": "My father", + "relational_word": "infant:twelve years old twelve months old", + "is_associative": 0 + }, + { + "index": 170, + "is_switchable": 1, + "sentence": "In July, Kamtchatka declared war on Yakutsk. Since Yakutsk's army was much better equipped and ten times larger, [they] were defeated within weeks.", + "answer1": "Yakutsk", + "answer0": "Kamchatka", + "sentence_switched": "In july, Yakutsk declared war on kamchatka. since kamchatka's army was much better equipped and ten times larger, [they] were defeated within weeks.", + "correct_answer": "Kamchatka", + "relational_word": "better equipped and large:defeated/victorious", + "is_associative": 0 + }, + { + "index": 171, + "is_switchable": 1, + "sentence": "In July, Kamtchatka declared war on Yakutsk. Since Yakutsk's army was much better equipped and ten times larger, [they] were victorious within weeks.", + "answer1": "Yakutsk", + "answer0": "Kamchatka", + "sentence_switched": "In july, Yakutsk declared war on kamchatka. since kamchatka's army was much better equipped and ten times larger, [they] were victorious within weeks.", + "correct_answer": "Yakutsk", + "relational_word": "better equipped and large:defeated/victorious", + "is_associative": 0 + }, + { + "index": 172, + "is_switchable": 0, + "sentence": "Look! There is a minnow swimming right below that duck! [It] had better get away to safety fast!", + "answer1": "The duck", + "answer0": "The minnow", + "sentence_switched": "Look! there is a duck swimming right below that minnow! [it] had better get away to safety fast!", + "correct_answer": "The minnow", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 173, + "is_switchable": 0, + "sentence": "Look! There is a shark swimming right below that duck! [It] had better get away to safety fast!", + "answer1": "The duck", + "answer0": "The shark", + "sentence_switched": "Look! there is a duck swimming right below that shark! [it] had better get away to safety fast!", + "correct_answer": "The duck", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 174, + "is_switchable": 0, + "sentence": "Archaeologists have concluded that humans lived in Laputa 20,000 years ago. [They] hunted for evidence on the river banks.", + "answer1": "Prehistoric humans", + "answer0": "Archaeologists", + "sentence_switched": "Prehistoric humans have concluded that humans lived in laputa 20,000 years ago. [they] hunted for evidence on the river banks.", + "correct_answer": "Archaeologists", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 175, + "is_switchable": 0, + "sentence": "Archaeologists have concluded that humans lived in Laputa 20,000 years ago. [They] hunted for deer on the river banks.", + "answer1": "Prehistoric humans", + "answer0": "Archaeologists", + "sentence_switched": "Prehistoric humans have concluded that humans lived in laputa 20,000 years ago. [they] hunted for deer on the river banks.", + "correct_answer": "Prehistoric humans", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 176, + "is_switchable": 0, + "sentence": "The scientists are studying three species of fish that have recently been found living in the Indian Ocean. [They] began two years ago.", + "answer1": "The fish", + "answer0": "The scientists", + "sentence_switched": "The fish are studying three species of scientists that have recently been found living in the indian ocean. [they] began two years ago.", + "correct_answer": "The scientists", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 177, + "is_switchable": 0, + "sentence": "The scientists are studying three species of fish that have recently been found living in the Indian Ocean. [They] appeared two years ago.", + "answer1": "The fish", + "answer0": "The scientists", + "sentence_switched": "The fish are studying three species of scientists that have recently been found living in the indian ocean. [they] appeared two years ago.", + "correct_answer": "The fish", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 178, + "is_switchable": 0, + "sentence": "The journalists interviewed the stars of the new movie. [They] were very persistent, so the interview lasted for a long time.", + "answer1": "The stars", + "answer0": "The journalists", + "sentence_switched": "The stars interviewed the journalists of the new movie. [they] were very persistent, so the interview lasted for a long time.", + "correct_answer": "The journalists", + "relational_word": "interview:persistent cooperative", + "is_associative": 0 + }, + { + "index": 179, + "is_switchable": 0, + "sentence": "The journalists interviewed the stars of the new movie. [They] were very cooperative, so the interview lasted for a long time.", + "answer1": "The stars", + "answer0": "The journalists", + "sentence_switched": "The stars interviewed the journalists of the new movie. [they] were very cooperative, so the interview lasted for a long time.", + "correct_answer": "The stars", + "relational_word": "interview:persistent cooperative", + "is_associative": 0 + }, + { + "index": 180, + "is_switchable": 0, + "sentence": "The police arrested all of the gang members. [They] were trying to stop the drug trade in the neighborhood.", + "answer1": "The gang members", + "answer0": "The police", + "sentence_switched": "The gang members arrested all of the police. [they] were trying to stop the drug trade in the neighborhood.", + "correct_answer": "The police", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 181, + "is_switchable": 0, + "sentence": "The police arrested all of the gang members. [They] were trying to run the drug trade in the neighborhood.", + "answer1": "The gang members", + "answer0": "The police", + "sentence_switched": "The gang members arrested all of the police. [they] were trying to run the drug trade in the neighborhood.", + "correct_answer": "The gang members", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 182, + "is_switchable": 0, + "sentence": "I put the cake away in the refrigerator. [It] has a lot of butter in it.", + "answer1": "The refrigerator", + "answer0": "The cake", + "sentence_switched": "I put the refrigerator away in the cake. [it] has a lot of butter in it.", + "correct_answer": "The cake", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 183, + "is_switchable": 0, + "sentence": "I put the cake away in the refrigerator. [It] has a lot of leftovers in it.", + "answer1": "The refrigerator", + "answer0": "The cake", + "sentence_switched": "I put the refrigerator away in the cake. [it] has a lot of leftovers in it.", + "correct_answer": "The refrigerator", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 184, + "is_switchable": 0, + "sentence": "Sam broke both his ankles and he's walking with crutches. But a month or so from now [they] should be better.", + "answer1": "The crutches", + "answer0": "The ankles", + "sentence_switched": "Sam broke both his crutches and he's walking with ankles. but a month or so from now [they] should be better.", + "correct_answer": "The ankles", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 185, + "is_switchable": 0, + "sentence": "Sam broke both his ankles and he's walking with crutches. But a month or so from now [they] should be unnecessary.", + "answer1": "The crutches", + "answer0": "The ankles", + "sentence_switched": "Sam broke both his crutches and he's walking with ankles. but a month or so from now [they] should be unnecessary.", + "correct_answer": "The crutches", + "relational_word": "none", + "is_associative": 1 + }, + { + "index": 186, + "is_switchable": 0, + "sentence": "When the sponsors of the bill got to the town hall, they were surprised to find that the room was full of opponents. [They] were very much in the minority.", + "answer1": "The opponents", + "answer0": "The sponsors", + "sentence_switched": "When the opponents of the bill got to the town hall, they were surprised to find that the room was full of sponsors. [they] were very much in the minority.", + "correct_answer": "The sponsors", + "relational_word": "be full of:minority/majority", + "is_associative": 0 + }, + { + "index": 187, + "is_switchable": 0, + "sentence": "When the sponsors of the bill got to the town hall, they were surprised to find that the room was full of opponents. [They] were very much in the majority.", + "answer1": "The opponents", + "answer0": "The sponsors", + "sentence_switched": "When the opponents of the bill got to the town hall, they were surprised to find that the room was full of sponsors. [they] were very much in the majority.", + "correct_answer": "The opponents", + "relational_word": "be full of:minority/majority", + "is_associative": 0 + }, + { + "index": 188, + "is_switchable": 1, + "sentence": "Everyone really loved the oatmeal cookies; only a few people liked the chocolate chip cookies. Next time, we should make more of [them] .", + "answer1": "The chocolate chip cookies", + "answer0": "The oatmeal cookies", + "sentence_switched": "Everyone really loved the chocolate chip cookies; only a few people liked the oatmeal cookies. next time, we should make more of [them] .", + "correct_answer": "The oatmeal cookies", + "relational_word": "like over:more/fewer", + "is_associative": 0 + }, + { + "index": 189, + "is_switchable": 1, + "sentence": "Everyone really loved the oatmeal cookies; only a few people liked the chocolate chip cookies. Next time, we should make fewer of [them] .", + "answer1": "The chocolate chip cookies", + "answer0": "The oatmeal cookies", + "sentence_switched": "Everyone really loved the chocolate chip cookies; only a few people liked the oatmeal cookies. next time, we should make fewer of [them] .", + "correct_answer": "The chocolate chip cookies", + "relational_word": "like over:more/fewer", + "is_associative": 0 + }, + { + "index": 190, + "is_switchable": 0, + "sentence": "We had hoped to place copies of our newsletter on all the chairs in the auditorium, but there were simply not enough of [them] .", + "answer1": "chairs", + "answer0": "copies of the newsletter", + "sentence_switched": "We had hoped to place chairs on all the copies of the newsletter in the auditorium, but there were simply not enough of [them] .", + "correct_answer": "copies of the newsletter", + "relational_word": "place on all:not enough/too many", + "is_associative": 0 + }, + { + "index": 191, + "is_switchable": 0, + "sentence": "We had hoped to place copies of our newsletter on all the chairs in the auditorium, but there were simply too many of [them] .", + "answer1": "chairs", + "answer0": "copies of the newsletter", + "sentence_switched": "We had hoped to place chairs on all the copies of the newsletter in the auditorium, but there were simply too many of [them] .", + "correct_answer": "chairs", + "relational_word": "place on all:not enough/too many", + "is_associative": 0 + }, + { + "index": 192, + "is_switchable": 0, + "sentence": "I stuck a pin through a carrot. When I pulled the pin out, [it] left a hole.", + "answer1": "The carrot", + "answer0": "The pin", + "sentence_switched": "I stuck a carrot through a pin. when i pulled the carrot out, [it] left a hole.", + "correct_answer": "The pin", + "relational_word": "stick:leave have", + "is_associative": 1 + }, + { + "index": 193, + "is_switchable": 0, + "sentence": "I stuck a pin through a carrot. When I pulled the pin out, [it] had a hole.", + "answer1": "The carrot", + "answer0": "The pin", + "sentence_switched": "I stuck a carrot through a pin. when i pulled the carrot out, [it] had a hole.", + "correct_answer": "The carrot", + "relational_word": "stick:leave have", + "is_associative": 2 + }, + { + "index": 194, + "is_switchable": 0, + "sentence": "I couldn't find a spoon, so I tried using a pen to stir my coffee. But that turned out to be a bad idea, because [it] got full of coffee.", + "answer1": "The coffee", + "answer0": "The pen", + "sentence_switched": "I couldn't find a spoon, so i tried using a coffee to stir my pen. but that turned out to be a bad idea, because [it] got full of pen.", + "correct_answer": "The pen", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 195, + "is_switchable": 0, + "sentence": "I couldn't find a spoon, so I tried using a pen to stir my coffee. But that turned out to be a bad idea, because [it] got full of ink.", + "answer1": "The coffee", + "answer0": "The pen", + "sentence_switched": "I couldn't find a spoon, so i tried using a coffee to stir my pen. but that turned out to be a bad idea, because [it] got full of ink.", + "correct_answer": "The coffee", + "relational_word": "none", + "is_associative": 0 + }, + { + "index": 196, + "is_switchable": 1, + "sentence": "Steve follows Fred's example in everything. [He] admires him hugely.", + "answer1": "Fred", + "answer0": "Steve", + "sentence_switched": "Fred follows steve's example in everything. [he] admires him hugely.", + "correct_answer": "Steve", + "relational_word": "follow:admire/influence", + "is_associative": 2 + }, + { + "index": 197, + "is_switchable": 1, + "sentence": "Steve follows Fred's example in everything. [He] influences him hugely.", + "answer1": "Fred", + "answer0": "Steve", + "sentence_switched": "Fred follows steve's example in everything. [he] influences him hugely.", + "correct_answer": "Fred", + "relational_word": "follow:admire/influence", + "is_associative": 2 + }, + { + "index": 198, + "is_switchable": 0, + "sentence": "The table won't fit through the doorway because [it] is too wide.", + "answer1": "The doorway", + "answer0": "The table", + "sentence_switched": "The doorway won't fit through the table because [it] is too wide.", + "correct_answer": "The table", + "relational_word": "fit through:wide/narrow", + "is_associative": 0 + }, + { + "index": 199, + "is_switchable": 0, + "sentence": "The table won't fit through the doorway because [it] is too narrow.", + "answer1": "The doorway", + "answer0": "The table", + "sentence_switched": "The doorway won't fit through the table because [it] is too narrow.", + "correct_answer": "The doorway", + "relational_word": "fit through:wide/narrow", + "is_associative": 0 + }, + { + "index": 200, + "is_switchable": 1, + "sentence": "Grace was happy to trade me her sweater for my jacket. She thinks [it] looks dowdy on her.", + "answer1": "The jacket", + "answer0": "The sweater", + "sentence_switched": "Grace was happy to trade me her jacket for my sweater. she thinks [it] looks dowdy on her.", + "correct_answer": "The sweater", + "relational_word": "trade:dowdy/great", + "is_associative": 0 + }, + { + "index": 201, + "is_switchable": 1, + "sentence": "Grace was happy to trade me her sweater for my jacket. She thinks [it] looks great on her.", + "answer1": "The jacket", + "answer0": "The sweater", + "sentence_switched": "Grace was happy to trade me her jacket for my sweater. she thinks [it] looks great on her.", + "correct_answer": "The jacket", + "relational_word": "trade:dowdy/great", + "is_associative": 0 + }, + { + "index": 202, + "is_switchable": 1, + "sentence": "John hired Bill to take care of [him] .", + "answer1": "Bill", + "answer0": "John", + "sentence_switched": "Bill hired john to take care of [him] .", + "correct_answer": "John", + "relational_word": "hire/hire oneself to:take care of", + "is_associative": 0 + }, + { + "index": 203, + "is_switchable": 1, + "sentence": "John hired himself out to Bill to take care of [him] .", + "answer1": "Bill", + "answer0": "John", + "sentence_switched": "Bill hired himself out to john to take care of [him] .", + "correct_answer": "Bill", + "relational_word": "hire/hire oneself to:take care of", + "is_associative": 0 + }, + { + "index": 204, + "is_switchable": 1, + "sentence": "John promised Bill to leave, so an hour later [he] left.", + "answer1": "Bill", + "answer0": "John", + "sentence_switched": "Bill promised john to leave, so an hour later [he] left.", + "correct_answer": "John", + "relational_word": "promise/order", + "is_associative": 0 + }, + { + "index": 205, + "is_switchable": 1, + "sentence": "John ordered Bill to leave, so an hour later [he] left.", + "answer1": "Bill", + "answer0": "John", + "sentence_switched": "Bill ordered john to leave, so an hour later [he] left.", + "correct_answer": "Bill", + "relational_word": "promise/order", + "is_associative": 0 + }, + { + "index": 206, + "is_switchable": 1, + "sentence": "Sam Goodman's biography of the Spartan general Xenophanes conveys a vivid sense of the difficulties [he] faced in his research.", + "answer1": "Xenophanes", + "answer0": "Goodman", + "sentence_switched": "Sam xenophanes's biography of the spartan general goodman conveys a vivid sense of the difficulties [he] faced in his research.", + "correct_answer": "Goodman", + "relational_word": "none", + "is_associative": 2 + }, + { + "index": 207, + "is_switchable": 1, + "sentence": "Sam Goodman's biography of the Spartan general Xenophanes conveys a vivid sense of the difficulties [he] faced in his childhood.", + "answer1": "Xenophanes", + "answer0": "Goodman", + "sentence_switched": "Sam xenophanes's biography of the spartan general goodman conveys a vivid sense of the difficulties [he] faced in his childhood.", + "correct_answer": "Xenophanes", + "relational_word": "none", + "is_associative": 2 + }, + { + "index": 208, + "is_switchable": 0, + "sentence": "Emma's mother had died long ago, and [her] education had been managed by an excellent woman as governess.", + "answer1": "Emma's mother", + "answer0": "Emma", + "sentence_switched": "Emma had died long ago, and [her] education had been managed by an excellent woman as governess.", + "correct_answer": "Emma", + 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