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RaRe-Technologies / Gensim Data

Licence: lgpl-2.1
Data repository for pretrained NLP models and NLP corpora.

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What is Gensim-data for?

Research datasets regularly disappear, change over time, become obsolete or come without a sane implementation to handle the data format reading and processing.

For this reason, Gensim launched its own dataset storage, committed to long-term support, a sane standardized usage API and focused on datasets for unstructured text processing (no images or audio). This Gensim-data repository serves as that storage.

There's no need for you to use this repository directly. Instead, simply install Gensim and use its download API (see the Quickstart below). It will "talk" to this repository automagically.

💡 When you use the Gensim download API, all data is stored in your ~/gensim-data home folder.

Read more about the project rationale and design decisions in this article: New Download API for Pretrained NLP Models and Datasets.

How does it work?

Technically, the actual (sometimes large) corpora and model files are being stored as release attachments here on Github. Each dataset (and each new version of each dataset) gets its own release, forever immutable.

Each release is accompanied by a usage example and release notes, for example: Corpus of USPTO Patents from 2017; English Wikipedia from 2017 with plaintext section.

🔴 Each dataset comes with its own license, which the users should study carefully before using the dataset!


Quickstart

To load a model or corpus, use either the Python or command line interface of Gensim (you'll need Gensim installed first):

  • Python API

    Example: load a pre-trained model (gloVe word vectors):

    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 a Word2Vec model:

    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 a dataset and return the local file path (no opening):

    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
    
  • The same operations, but from CLI, command line interface:

    python -m gensim.downloader --info  # show info about available models/datasets
    python -m gensim.downloader --download 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

Datasets

name file size read_more description license
20-newsgroups 13 MB The notorious collection of approximately 20,000 newsgroup posts, partitioned (nearly) evenly across 20 different newsgroups. not found
fake-news 19 MB News dataset, contains text and metadata from 244 websites and represents 12,999 posts in total from a specific window of 30 days. The data was pulled using the webhose.io API, and because it's coming from their crawler, not all websites identified by their BS Detector are present in this dataset. Data sources that were missing a label were simply assigned a label of 'bs'. There are (ostensibly) no genuine, reliable, or trustworthy news sources represented in this dataset (so far), so don't trust anything you read. https://creativecommons.org/publicdomain/zero/1.0/
patent-2017 2944 MB Patent Grant Full Text. Contains the full text including tables, sequence data and 'in-line' mathematical expressions of each patent grant issued in 2017. not found
quora-duplicate-questions 20 MB Over 400,000 lines of potential question duplicate pairs. Each line contains IDs for each question in the pair, the full text for each question, and a binary value that indicates whether the line contains a duplicate pair or not. probably https://www.quora.com/about/tos
semeval-2016-2017-task3-subtaskA-unannotated 223 MB SemEval 2016 / 2017 Task 3 Subtask A unannotated dataset contains 189,941 questions and 1,894,456 comments in English collected from the Community Question Answering (CQA) web forum of Qatar Living. These can be used as a corpus for language modelling. These datasets are free for general research use.
semeval-2016-2017-task3-subtaskBC 6 MB SemEval 2016 / 2017 Task 3 Subtask B and C datasets contain train+development (317 original questions, 3,169 related questions, and 31,690 comments), and test datasets in English. The description of the tasks and the collected data is given in sections 3 and 4.1 of the task paper http://alt.qcri.org/semeval2016/task3/data/uploads/semeval2016-task3-report.pdf linked in section “Papers” of https://github.com/RaRe-Technologies/gensim-data/issues/18. All files released for the task are free for general research use
text8 31 MB First 100,000,000 bytes of plain text from Wikipedia. Used for testing purposes; see wiki-english-* for proper full Wikipedia datasets. not found
wiki-english-20171001 6214 MB 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 https://dumps.wikimedia.org/legal.html

Models

name num vectors file size base dataset read_more description parameters preprocessing license
conceptnet-numberbatch-17-06-300 1917247 1168 MB ConceptNet, word2vec, GloVe, and OpenSubtitles 2016 ConceptNet Numberbatch consists of state-of-the-art semantic vectors (also known as word embeddings) that can be used directly as a representation of word meanings or as a starting point for further machine learning. ConceptNet Numberbatch is part of the ConceptNet open data project. ConceptNet provides lots of ways to compute with word meanings, one of which is word embeddings. ConceptNet Numberbatch is a snapshot of just the word embeddings. It is built using an ensemble that combines data from ConceptNet, word2vec, GloVe, and OpenSubtitles 2016, using a variation on retrofitting.
  • dimension - 300
- https://github.com/commonsense/conceptnet-numberbatch/blob/master/LICENSE.txt
fasttext-wiki-news-subwords-300 999999 958 MB Wikipedia 2017, UMBC webbase corpus and statmt.org news dataset (16B tokens) 1 million word vectors trained on Wikipedia 2017, UMBC webbase corpus and statmt.org news dataset (16B tokens).
  • dimension - 300
- https://creativecommons.org/licenses/by-sa/3.0/
glove-twitter-100 1193514 387 MB Twitter (2B tweets, 27B tokens, 1.2M vocab, uncased) Pre-trained vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased (https://nlp.stanford.edu/projects/glove/)
  • dimension - 100
Converted to w2v format with python -m gensim.scripts.glove2word2vec -i <fname> -o glove-twitter-100.txt. http://opendatacommons.org/licenses/pddl/
glove-twitter-200 1193514 758 MB Twitter (2B tweets, 27B tokens, 1.2M vocab, uncased) Pre-trained vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased (https://nlp.stanford.edu/projects/glove/).
  • dimension - 200
Converted to w2v format with python -m gensim.scripts.glove2word2vec -i <fname> -o glove-twitter-200.txt. http://opendatacommons.org/licenses/pddl/
glove-twitter-25 1193514 104 MB Twitter (2B tweets, 27B tokens, 1.2M vocab, uncased) Pre-trained vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased (https://nlp.stanford.edu/projects/glove/).
  • dimension - 25
Converted to w2v format with python -m gensim.scripts.glove2word2vec -i <fname> -o glove-twitter-25.txt. http://opendatacommons.org/licenses/pddl/
glove-twitter-50 1193514 199 MB Twitter (2B tweets, 27B tokens, 1.2M vocab, uncased) Pre-trained vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased (https://nlp.stanford.edu/projects/glove/)
  • dimension - 50
Converted to w2v format with python -m gensim.scripts.glove2word2vec -i <fname> -o glove-twitter-50.txt. http://opendatacommons.org/licenses/pddl/
glove-wiki-gigaword-100 400000 128 MB Wikipedia 2014 + Gigaword 5 (6B tokens, uncased) Pre-trained vectors based on Wikipedia 2014 + Gigaword 5.6B tokens, 400K vocab, uncased (https://nlp.stanford.edu/projects/glove/).
  • dimension - 100
Converted to w2v format with python -m gensim.scripts.glove2word2vec -i <fname> -o glove-wiki-gigaword-100.txt. http://opendatacommons.org/licenses/pddl/
glove-wiki-gigaword-200 400000 252 MB Wikipedia 2014 + Gigaword 5 (6B tokens, uncased) Pre-trained vectors based on Wikipedia 2014 + Gigaword, 5.6B tokens, 400K vocab, uncased (https://nlp.stanford.edu/projects/glove/).
  • dimension - 200
Converted to w2v format with python -m gensim.scripts.glove2word2vec -i <fname> -o glove-wiki-gigaword-200.txt. http://opendatacommons.org/licenses/pddl/
glove-wiki-gigaword-300 400000 376 MB Wikipedia 2014 + Gigaword 5 (6B tokens, uncased) Pre-trained vectors based on Wikipedia 2014 + Gigaword, 5.6B tokens, 400K vocab, uncased (https://nlp.stanford.edu/projects/glove/).
  • dimension - 300
Converted to w2v format with python -m gensim.scripts.glove2word2vec -i <fname> -o glove-wiki-gigaword-300.txt. http://opendatacommons.org/licenses/pddl/
glove-wiki-gigaword-50 400000 65 MB Wikipedia 2014 + Gigaword 5 (6B tokens, uncased) Pre-trained vectors based on Wikipedia 2014 + Gigaword, 5.6B tokens, 400K vocab, uncased (https://nlp.stanford.edu/projects/glove/).
  • dimension - 50
Converted to w2v format with python -m gensim.scripts.glove2word2vec -i <fname> -o glove-wiki-gigaword-50.txt. http://opendatacommons.org/licenses/pddl/
word2vec-google-news-300 3000000 1662 MB Google News (about 100 billion words) Pre-trained vectors trained on a part of the 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/).
  • dimension - 300
- not found
word2vec-ruscorpora-300 184973 198 MB Russian National Corpus (about 250M words) Word2vec Continuous Skipgram vectors trained on full Russian National Corpus (about 250M words). The model contains 185K words.
  • dimension - 300
  • window_size - 10
The corpus was lemmatized and tagged with Universal PoS https://creativecommons.org/licenses/by/4.0/deed.en

(generated by generate_table.py based on 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.

  3. Create a new issue and give us the dataset link. Add a detailed description on why and how you created the dataset, any related papers or research, plus how do you expect other users should use it. Include a code example where relevant.


Gensim-data is open source software released under the LGPL 2.1 license.

Copyright (c) 2018 RARE Technologies.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].