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transformer-translator-pytorch

This is a machine translation project using the basic Transformer introduced in Attention is all you need[1].

I used English-French corpus provided by "European Parliament Proceedings Parallel Corpus 1996-2011"[2]. (You can use any other datasets, of course.)


Configurations

You can set various hyperparameters in src/constants.py file.

The description of each variable is as follows.


Parameters for data

Argument Type Description Default
DATA_DIR str Name of the parent directory where data files are stored. 'data'
SP_DIR str Path for the directory which contains the sentence tokenizers and vocab files. f'{DATA_DIR}/sp'
SRC_DIR str Name of the directory which contains the source data files. 'src'
TRG_DIR str Name of the directory which contains the target data files. 'trg'
SRC_RAW_DATA_NAME str Name of the source raw data file. raw_data.src
TRG_RAW_DATA_NAME str Name of the target raw data file. raw_data.trg
TRAIN_NAME str Name of the train data file. train.txt
VALID_NAME str Name of the validation data file. valid.txt
TEST_NAME str Name of the test data file. test.txt

Parameters for Sentencepiece

Argument Type Description Default
pad_id int The index of pad token. 0
sos_id int The index of start token. 1
eos_id int The index of end token. 2
unk_id int The index of unknown token. 3
src_model_prefix str The file name prefix for the source language tokenizer & vocabulary. src_sp
trg_model_prefix str The file name prefix for the target language tokenizer & vocabulary. trg_sp
sp_vocab_size int The size of vocabulary. 16000
character_coverage float The value for character coverage. 1.0
model_type str The type of sentencepiece model. (unigram, bpe, char, or word) unigram

Parameters for the transformer & training

Argument Type Description Default
device torch.device The device type. (CUDA or CPU) torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
learning_rate float The learning rate. 1e-4
batch_size int The batch size. 80
seq_len int The maximum length of a sentence. 200
num_heads int The number of heads for Multi-head attention. 8
num_layers int The number of layers in the encoder & the decoder. 6
d_model int The size of hidden states in the model. 512
d_ff int The size of intermediate hidden states in the feed-forward layer. 2048
d_k int The size of dimension which a single head should take. (Make sure that d_model is divided into num_heads.) d_model // num_heads
drop_out_rate float The dropout rate. 0.1
num_epochs int The total number of iterations. 10
beam_size int The beam size. (Only used when the beam search is used at inference time.) 8
ckpt_dir str The path for saved checkpoints. saved_model

How to run

  1. Download the dataset from "European Parliament Proceedings Parallel Corpus 1996-2011".

    You can choose any parallel corpus you want. (I chose English-French for example.)

    Download it and extract it until you have two raw text files, europarl-v7.SRC-TRG.SRC and europarl-v7.SRC-TRG.TRG.

    Make DATA_DIR directory in the root directory and put raw texts in it.

    Name each SRC_RAW_DATA_NAME and TRG_RAW_DATA_NAME.

    Of course, you can use additional datasets and just make sure that the formats/names of raw data files are same as those of above datasets.


  2. Install all required packages.

    pip install -r requirements.txt

  3. Run src/sentencepiece_train.py.

    python src/sentencepiece_train.py

    Then there would be SP_DIR directory containing two sentencepiece models and two vocab files.

    Each model and vocab files are for source language and target language.

    In default setting, the structure of whole data directory should be like below.

    • data
      • sp
        • src_sp.model
        • src_sp.vocab
        • tar_sp.model
        • tar_sp.vocab
      • src
        • train.txt
        • valid.txt
      • trg
        • train.txt
        • valid.txt
      • raw_data.src
      • raw_data.tar

  4. Run below command to train a transformer model for machine translation.

    python src/main.py --mode='train' --ckpt_name=CHECKPOINT_NAME
    • --mode: You have to specify the mode among two options, 'train' or 'inference'.
    • --ckpt_name: This specify the checkpoint file name. This would be the name of trained checkpoint and you can continue your training with this model in the case of resuming training. If you want to conduct training first, this parameter should be omitted. When testing, this would be the name of the checkpoint you want to test. (default: None)

  5. Run below command to conduct an inference with the trained model.

    python src/main.py --mode='inference' --ckpt_name=CHECKPOINT_NAME --input=INPUT_TEXT --decode=DECODING_STRATEGY
    • --input: This is an input sequence you want to translate.
    • --decode: This makes the decoding algorithm into either greedy method or beam search. Make this parameter 'greedy' or 'beam'. (default: greedy)

References

[1] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008). (http://papers.nips.cc/paper/7181-attention-is-all-you-need)

[2] Koehn, P. (2005, September). Europarl: A parallel corpus for statistical machine translation. In MT summit (Vol. 5, pp. 79-86). (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.459.5497&rep=rep1&type=pdf)