This repository contains the word2vec C code, but with comments. Run
git diff originalto see the complete set of changes to the original code.
Cross-reference this code with the original papers:
- Distributed Representations of Words and Phrases and their Compositionality
- Efficient Estimation of Word Representations in Vector Space
and the essential follow-up paper:
The following gensim blog posts by Radim Řehůřek are very interesting and informative:
- Making sense of word2vec
- Deep learning with word2vec and gensim
- Word2vec in Python, Part Two: Optimizing
- Parallelizing word2vec in Python
And---shameless plug---I wrote a C++ implementation of word2vec (the skip-gram with negative sampling (SGNS) algorithm) that also supports streaming (vocabulary and embedding model are learned in one pass, see the write-up on arXiv for details):
- athena (naive-lm-train-raw is the entry point for basic SGNS training)
The original content of README.txt follows the break.
Tools for computing distributed representtion of words
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/