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

Assuming you have cloned the git repository, navigate into this directory. To download the example text and evaluation data:

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

You will need to compile the ops as follows:

TF_INC=$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_include())')
g++ -std=c++11 -shared word2vec_ops.cc word2vec_kernels.cc -o word2vec_ops.so -fPIC -I $TF_INC -O2 -D_GLIBCXX_USE_CXX11_ABI=0

On Mac, add -undefined dynamic_lookup to the g++ command.

(For an explanation of what this is doing, see the tutorial on Adding a New Op to TensorFlow. The flag -D_GLIBCXX_USE_CXX11_ABI=0 is included to support newer versions of gcc. However, if you compiled TensorFlow from source using gcc 5 or later, you may need to exclude the flag.) Then run using:

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

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