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https://github.com/dav/word2vec
This tool provides an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words. These representations can be subsequently used in many natural language processing applications and for further research.
https://github.com/dav/word2vec
Last synced: 15 days ago
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This tool provides an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words. These representations can be subsequently used in many natural language processing applications and for further research.
- Host: GitHub
- URL: https://github.com/dav/word2vec
- Owner: dav
- License: apache-2.0
- Created: 2013-08-17T18:11:42.000Z (about 11 years ago)
- Default Branch: master
- Last Pushed: 2021-05-10T21:53:01.000Z (over 3 years ago)
- Last Synced: 2024-10-16T00:24:26.933Z (29 days ago)
- Language: C
- Size: 132 KB
- Stars: 1,621
- Watchers: 96
- Forks: 618
- Open Issues: 21
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
word2vec
========
Original from https://code.google.com/p/word2vec/
I've copied it to a github project so I can apply and track community patches for my needs (starting with capability for Mac OS X compilation).
- ***makefile and some source has been modified for Mac OS X compilation*** See https://code.google.com/p/word2vec/issues/detail?id=1#c5
- ***memory patch for word2vec has been applied*** See https://code.google.com/p/word2vec/issues/detail?id=2
- Project file layout altered
There seems to be a segfault in the compute-accuracy utility.
To get started:
cd scripts && ./demo-word.sh
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Original README text follows:
This tool provides an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words. These representations can be subsequently used in many natural language processing applications and for further research.
Tools for computing distributed representation of words
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We provide an implementation of the Continuous Bag-of-Words (CBOW) and the Skip-gram model (SG), as well as several demo scripts.
Given a text corpus, the word2vec tool learns a vector for every word in the vocabulary using the Continuous
Bag-of-Words or the Skip-Gram neural network architectures. The user should to specify the following:
- desired vector dimensionality
- the size of the context window for either the Skip-Gram or the Continuous Bag-of-Words model
- training algorithm: hierarchical softmax and / or negative sampling
- threshold for downsampling the frequent words
- number of threads to use
- the format of the output word vector file (text or binary)
Usually, the other hyper-parameters such as the learning rate do not need to be tuned for different training sets.
The script demo-word.sh downloads a small (100MB) text corpus from the web, and trains a small word vector model. After the training
is finished, the user can interactively explore the similarity of the words.
More information about the scripts is provided at https://code.google.com/p/word2vec/