https://github.com/stanfordnlp/GloVe

Software in C and data files for the popular GloVe model for distributed word representations, a.k.a. word vectors or embeddings
https://github.com/stanfordnlp/GloVe

Last synced: about 2 months ago
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Software in C and data files for the popular GloVe model for distributed word representations, a.k.a. word vectors or embeddings

• Host: GitHub
• URL: https://github.com/stanfordnlp/GloVe
• Owner: stanfordnlp
• License: apache-2.0
• Created: 2015-09-01T17:21:18.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2023-09-19T19:39:59.000Z (about 1 year ago)
• Last Synced: 2024-10-15T09:41:47.614Z (about 2 months ago)
• Language: C
• Homepage:
• Size: 233 KB
• Stars: 6,851
• Watchers: 228
• Forks: 1,512
• Open Issues: 81
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-starred - GloVe - GloVe model for distributed word representation (C)

README

        
## GloVe: Global Vectors for Word Representation

| nearest neighbors of 
 frog | Litoria             |  Leptodactylidae | Rana | Eleutherodactylus |

| --- | ------------------------------- | ------------------- | ---------------- | ------------------- |

| Pictures |  |  |  |  |

| Comparisons | man -> woman             |  city -> zip | comparative -> superlative |

| --- | ------------------------|-------------------------|-------------------------|

| GloVe Geometry |   |  |  |

We provide an implementation of the GloVe model for learning word representations, and describe how to download web-dataset vectors or train your own. See the [project page](https://nlp.stanford.edu/projects/glove/) or the [paper](https://nlp.stanford.edu/pubs/glove.pdf) for more information on glove vectors.

## Download pre-trained word vectors

The links below contain word vectors obtained from the respective corpora. If you want word vectors trained on massive web datasets, you need only download one of these text files! Pre-trained word vectors are made available under the Public Domain Dedication and License.





  
 Common Crawl (42B tokens, 1.9M vocab, uncased, 300d vectors, 1.75 GB download): glove.42B.300d.zip [mirror] 

  
 Common Crawl (840B tokens, 2.2M vocab, cased, 300d vectors, 2.03 GB download): glove.840B.300d.zip [mirror] 

  
 Wikipedia 2014 + Gigaword 5 (6B tokens, 400K vocab, uncased, 300d vectors, 822 MB download): glove.6B.zip [mirror] 

  
 Twitter (2B tweets, 27B tokens, 1.2M vocab, uncased, 200d vectors, 1.42 GB download): glove.twitter.27B.zip [mirror] 





## Train word vectors on a new corpus



If the web datasets above don't match the semantics of your end use case, you can train word vectors on your own corpus.

    $ git clone https://github.com/stanfordnlp/glove

    $ cd glove && make

    $ ./demo.sh

Make sure you have the following prerequisites installed when running the steps above:

* GNU Make

* GCC (Clang pretending to be GCC is fine)

* Python and NumPy

The demo.sh script downloads a small corpus, consisting of the first 100M characters of Wikipedia. It collects unigram counts, constructs and shuffles cooccurrence data, and trains a simple version of the GloVe model. It also runs a word analogy evaluation script in python to verify word vector quality. More details about training on your own corpus can be found by reading [demo.sh](https://github.com/stanfordnlp/GloVe/blob/master/demo.sh) or the [src/README.md](https://github.com/stanfordnlp/GloVe/tree/master/src)

### License

All work contained in this package is licensed under the Apache License, Version 2.0. See the include LICENSE file.