From 19b856044739bbc9ce01070132c6231bc993cd4d Mon Sep 17 00:00:00 2001 From: Menshikh Ivan Date: Sun, 12 Nov 2017 09:34:58 +0500 Subject: [PATCH] Final cleanup in repo (#1) * script for content generation * add quickstart section * add info to script * fix heading * Add available data to README * new instruction[1] * new instruction[2] * remove hardcoded magic const * Fixed examples, update data tables, rework user instruction. * fix * modify example, highlight gensim-data * add \n to generator last line * basename -> abspath * add example with return_path=True * add bold * fix indentation * remove abspath * fix example * simple workaround for manual testing * fix for \n[GCC] * README fixes * add license, issue instructions * logger.exception for exceptions + report in the end * add info about storing data --- CONTRIBUTING.md | 11 + LICENSE | 502 ++++++++++++++++++++++++++++++++++++++++++++++ README.md | 145 +++++++++++-- _test_all.py | 58 ++++++ generate_table.py | 50 +++++ 5 files changed, 744 insertions(+), 22 deletions(-) create mode 100644 CONTRIBUTING.md create mode 100644 LICENSE create mode 100644 _test_all.py create mode 100644 generate_table.py diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..f147b0a --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,11 @@ +For reporting bugs, please see [contribution-guide.org](http://www.contribution-guide.org/). Be as concrete as possible, include relevant logs, package versions etc. + +For adding a new data set (corpus or model): + +1. Share a compressed file with your data set on any file-sharing service. + +2. Post the link here. + +3. Add a **detailed description** on how you created the dataset, related papers or research, plus how users should use it. Include a code example where relevant. + +Thanks! diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..4362b49 --- /dev/null +++ b/LICENSE @@ -0,0 +1,502 @@ + GNU LESSER GENERAL PUBLIC LICENSE + Version 2.1, February 1999 + + Copyright (C) 1991, 1999 Free Software Foundation, Inc. + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + +[This is the first released version of the Lesser GPL. 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In case, dataset/model size is greater than 2Gb, use: -```tar cz data-file-name|split -d -b 2Gib - data-name.tar.gz_``` -This will split the dataset/model into files of <=2Gb. -2. Now, find the value of checksum of this tar.gz(in case of multiple parts, find separate checksum of each) file. Use the function below : -``` -import hashlib -def calculate_md5_checksum(tar_file): - hash_md5 = hashlib.md5() - with open(tar_file, "rb") as f: - for chunk in iter(lambda: f.read(4096), b""): - hash_md5.update(chunk) - return hash_md5.hexdigest() -``` -3. After that, create the ```__init__.py``` file for dataset/model. ```__init__.py``` file contains load_data() function. -For datasets, this function returns the path to dataset file/folder. -For model, load_data() fucntion loads and returns the model. - -4. Next, create a new release. Add model/dataset name in the fields: tag version and release title. -5. Then, upload the model/dataset tar.gz file(files in case of multipart) and ```__init__.py``` files and click on publish release. -6. Lastly, update the list.json file. +# Gensim data +This repository keeps the models and datasets for the [gensim](https://github.com/RaRe-Technologies/gensim) download API. It serves as the data storage, and shouldn't be used directly (unless you're adding new datasets to it). + +💡 When you use the gensim download API, **all data will be stored in the `~/gensim-data` folder**. + +In current repository, all data stored as attachment files in [github-releases](https://github.com/RaRe-Technologies/gensim-data/releases) + +## Quickstart + +To load a model/dataset, use either the Python or command line interface: + +- **Python API** + + Example: load a pre-trained model (gloVe word vectors) + + ```python + import gensim.downloader as api + + info = api.info() # show info about available models/datasets + model = api.load("glove-twitter-25") # download the model and return as object ready for use + model.most_similar("cat") + + """ + output: + + [(u'dog', 0.9590819478034973), + (u'monkey', 0.9203578233718872), + (u'bear', 0.9143137335777283), + (u'pet', 0.9108031392097473), + (u'girl', 0.8880630135536194), + (u'horse', 0.8872727155685425), + (u'kitty', 0.8870542049407959), + (u'puppy', 0.886769711971283), + (u'hot', 0.8865255117416382), + (u'lady', 0.8845518827438354)] + + """ + ``` + + Example: load a corpus and use it to train Word2Vec + + ```python + from gensim.models.word2vec import Word2Vec + import gensim.downloader as api + + corpus = api.load('text8') # download the corpus and return it opened as an iterable + model = Word2Vec(corpus) # train a model from the corpus + model.most_similar("car") + + """ + output: + + [(u'driver', 0.8273754119873047), + (u'motorcycle', 0.769528865814209), + (u'cars', 0.7356342077255249), + (u'truck', 0.7331641912460327), + (u'taxi', 0.718338131904602), + (u'vehicle', 0.7177008390426636), + (u'racing', 0.6697118878364563), + (u'automobile', 0.6657308340072632), + (u'passenger', 0.6377975344657898), + (u'glider', 0.6374964714050293)] + + """ + ``` + + Example: **only** download and return the file path (no opening): + + ```python + import gensim.downloader as api + + print(api.load("20-newsgroups", return_path=True)) # output: /home/user/gensim-data/20-newsgroups/20-newsgroups.gz + print(api.load("glove-twitter-25", return_path=True)) # output: /home/user/gensim-data/glove-twitter-25/glove-twitter-25.gz + ``` + + - **CLI, command line interface** + ```bash + python -m gensim.downloader --info # show info about available models/datasets + python -m gensim.downloader --info text8 # download text8 dataset to ~/gensim-data/text8 + python -m gensim.downloader --download glove-twitter-25 # download model to ~/gensim-data/glove-twitter-50/ + ``` + +## Available data + +### Corpora + +| name | source | description | +|------|--------|-------------| +| 20-newsgroups | http://qwone.com/~jason/20Newsgroups/ | The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups | +| fake-news | Kaggle | It contains text and metadata scraped from 244 websites tagged as 'bullshit' here by the BS Detector Chrome Extension by Daniel Sieradski. | +| text8 | http://mattmahoney.net/dc/text8.zip | Cleaned small sample from wikipedia | +| wiki-en | https://dumps.wikimedia.org/enwiki/20171001/ | Extracted Wikipedia dump from October 2017. Produced by `python -m gensim.scripts.segment_wiki -f enwiki-20171001-pages-articles.xml.bz2 -o wiki-en.gz` | + +### Pretrained models + +| name | description | related papers | preprocessing | parameters | +|------|-------------|------------|--------|---------------| +| glove-twitter-100 | Pre-trained vectors, 2B tweets, 27B tokens, 1.2M vocab, uncased. https://nlp.stanford.edu/projects/glove/ | https://nlp.stanford.edu/pubs/glove.pdf | Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i -o glove-twitter-100.txt` | dimensions = 100 | +| glove-twitter-200 | Pre-trained vectors, 2B tweets, 27B tokens, 1.2M vocab, uncased. https://nlp.stanford.edu/projects/glove/ | https://nlp.stanford.edu/pubs/glove.pdf | Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i -o glove-twitter-200.txt` | dimensions = 200 | +| glove-twitter-25 | Pre-trained vectors, 2B tweets, 27B tokens, 1.2M vocab, uncased. https://nlp.stanford.edu/projects/glove/ | https://nlp.stanford.edu/pubs/glove.pdf | Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i -o glove-twitter-25.txt` | dimensions = 25 | +| glove-twitter-50 | Pre-trained vectors, 2B tweets, 27B tokens, 1.2M vocab, uncased. https://nlp.stanford.edu/projects/glove/ | https://nlp.stanford.edu/pubs/glove.pdf | Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i -o glove-twitter-50.txt` | dimensions = 50 | +| glove-wiki-gigaword-100 | Pre-trained vectors ,Wikipedia 2014 + Gigaword 5,6B tokens, 400K vocab, uncased. https://nlp.stanford.edu/projects/glove/ | https://nlp.stanford.edu/pubs/glove.pdf | Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i -o glove-wiki-gigaword-100.txt` | dimensions = 100 | +| glove-wiki-gigaword-200 | Pre-trained vectors ,Wikipedia 2014 + Gigaword 5,6B tokens, 400K vocab, uncased. https://nlp.stanford.edu/projects/glove/ | https://nlp.stanford.edu/pubs/glove.pdf | Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i -o glove-wiki-gigaword-200.txt` | dimentions = 200 | +| glove-wiki-gigaword-300 | Pre-trained vectors, Wikipedia 2014 + Gigaword 5, 6B tokens, 400K vocab, uncased. https://nlp.stanford.edu/projects/glove/ | https://nlp.stanford.edu/pubs/glove.pdf | Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i -o glove-wiki-gigaword-300.txt` | dimensions = 300 | +| glove-wiki-gigaword-50 | Pre-trained vectors ,Wikipedia 2014 + Gigaword 5,6B tokens, 400K vocab, uncased. https://nlp.stanford.edu/projects/glove/ | https://nlp.stanford.edu/pubs/glove.pdf | Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i -o glove-wiki-gigaword-50.txt` | dimensions = 50 | +| word2vec-google-news-300 | Pre-trained vectors trained on part of Google News dataset (about 100 billion words). The model contains 300-dimensional vectors for 3 million words and phrases. The phrases were obtained using a simple data-driven approach described in 'Distributed Representations of Words and Phrases and their Compositionality', https://code.google.com/archive/p/word2vec/ | https://arxiv.org/abs/1301.3781, https://arxiv.org/abs/1310.4546, https://www.microsoft.com/en-us/research/publication/linguistic-regularities-in-continuous-space-word-representations/?from=http%3A%2F%2Fresearch.microsoft.com%2Fpubs%2F189726%2Frvecs.pdf | - | dimensions = 300 | + +(generated by [generate_table.py](https://github.com/RaRe-Technologies/gensim-data/blob/master/generate_table.py) based on [list.json](https://github.com/RaRe-Technologies/gensim-data/blob/master/list.json)) + + +# Want to add a new corpus or model? + +1. Compress your data set using gzip or bz2. + +2. Share the compressed file on any file-sharing service. + +2. Create a [new issue](https://github.com/RaRe-Technologies/gensim-data/issues) and give us the dataset link. Add a **detailed description** on how you created the dataset, related papers or research, plus how users should use it. Include a code example where relevant. + +---------------- + +`Gensim-data` is open source software released under the [LGPL license](https://github.com/rare-technologies/gensim-data/blob/master/LICENSE). + +Copyright (c) 2017 [RaRe Technologies](https://rare-technologies.com/) diff --git a/_test_all.py b/_test_all.py new file mode 100644 index 0000000..4229812 --- /dev/null +++ b/_test_all.py @@ -0,0 +1,58 @@ +import sys +import os +import shutil +import logging +import gensim.downloader as api +from gensim.downloader import base_dir + +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") +logger = logging.getLogger("test-downloader-api") + +logger.info(sys.version.replace("\n", " ")) +if os.path.exists(base_dir): + logger.warning("REMOVE {}".format(base_dir)) + shutil.rmtree(base_dir) + +info = api.info() + +logger.info("Test models") +failed_models = [] +for idx, model_name in enumerate(info["models"]): + logger.info("{} ({} of {})".format(model_name, idx + 1, len(info["models"]))) + + try: + logger.info(api.load(model_name).most_similar("who")) + except Exception as e: + logger.exception(e) + failed_models.append(model_name) + +if failed_models: + logger.critical("FAILED MODELS: {}".format(", ".join(failed_models))) + +logger.info("#" * 25) +logger.info("Test datasets") +failed_datasets = [] +for idx, dataset_name in enumerate(info["corpora"]): + logger.info("{} ({} of {})".format(dataset_name, idx + 1, len(info["corpora"]))) + try: + res = sum(1 for _ in api.load(dataset_name)) + if res == 0: + raise Exception("empty dataset") + + except Exception as e: + logger.exception(e) + failed_datasets.append(dataset_name) + +if failed_datasets: + logger.critical("FAILED DATASETS: {}".format(", ".join(failed_datasets))) + +logger.info("#" * 25) +if len(failed_datasets + failed_models) == 0: + logger.info("Successful finished without errors!") + +else: + + logger.critical("FIX THIS:") + logger.critical("Models: {}".format(", ".join(failed_models))) + logger.critical("Datasets: {}".format(", ".join(failed_datasets))) +logger.info("#" * 25) diff --git a/generate_table.py b/generate_table.py new file mode 100644 index 0000000..227299e --- /dev/null +++ b/generate_table.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python +"""Script for generation table with data/models from list.json in .md format""" +import argparse +import json + + +DATASETS_SOURCE = "list.json" + + +def generate_table(fn): + with open(fn) as infile: + data = json.loads(infile.read()) + + datasets = sorted(data["corpora"].items(), key=lambda kv: kv[0]) + models = sorted(data["models"].items(), key=lambda kv: kv[0]) + + print("## Available data") + print("### Datasets") + print("| name | source | description |") + print("|------|--------|-------------|") + for name, other in datasets: + if name.startswith("__testing_"): + continue + print("| {name} | {source} | {description} |".format( + name=name, source=other["source"], description=other["description"] + )) + + print("") + print("### Models") + print("| name | description | papers | preprocessing | parameters |") + print("|------|-------------|------------|--------|---------------|") + for name, other in models: + if name.startswith("__testing_"): + continue + print("| {name} | {description} | {papers} | {preprocessing} | {parameters} |".format( + name=name, description=other["description"], parameters=other.get("parameters", "-"), + papers=other.get("papers", "-"), preprocessing=other.get("preprocessing", "-") + )) + print("\n(generated by {} based on {})".format(__file__, fn)) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--input-file", help="Path to {}".format(DATASETS_SOURCE), default=DATASETS_SOURCE) + args = parser.parse_args() + generate_table(args.input_file) + + +if __name__ == "__main__": + main()