TensorFlow-Models/tutorials/embedding

This directory contains models for unsupervised training of word embeddings using the model described in:

(Mikolov, et. al.) Efficient Estimation of Word Representations in Vector Space, ICLR 2013.

Detailed instructions on how to get started and use them are available in the tutorials. Brief instructions are below.

• Word2Vec Tutorial

To download the example text and evaluation data:

wget http://mattmahoney.net/dc/text8.zip -O text8.zip
unzip text8.zip
wget https://storage.googleapis.com/google-code-archive-source/v2/code.google.com/word2vec/source-archive.zip
unzip -p source-archive.zip  word2vec/trunk/questions-words.txt > questions-words.txt
rm source-archive.zip

Assuming you have cloned the git repository, navigate into this directory and run using:

cd models/tutorials/embedding
python word2vec_optimized.py \
  --train_data=text8 \
  --eval_data=questions-words.txt \
  --save_path=/tmp/

To run the code from sources using bazel:

bazel run -c opt models/tutorials/embedding/word2vec_optimized -- \
  --train_data=text8 \
  --eval_data=questions-words.txt \
  --save_path=/tmp/

Here is a short overview of what is in this directory.

File	What's in it?
word2vec.py	A version of word2vec implemented using TensorFlow ops and minibatching.
word2vec_test.py	Integration test for word2vec.
word2vec_optimized.py	A version of word2vec implemented using C ops that does no minibatching.
word2vec_optimized_test.py	Integration test for word2vec_optimized.
word2vec_kernels.cc	Kernels for the custom input and training ops.
word2vec_ops.cc	The declarations of the custom ops.