This is a modification of the word2vec software by Mikolov, allowing:
   - performing multiple iterations over the data.
   - the use of arbitraty context features.
   - dumping the context vectors at the end of the process.

This software was used in the paper "Dependency-Based Word Embeddings", Omer
Levy and Yoav Goldberg, 2014.

The "main" binary is word2vecf. See README.word2vecf.txt for usage

Unlike the original word2vec program which is self-contained,
the word2vecf program assumes some precomputations.

In particular, word2vecf DOES NOT handle vocabulary construction, and does
not read an unprocessed input.

The expected files are:
   file mapping words (strings) to their counts
   file mapping contexts (strings) to their counts
   used for constructing the sampling table for the negative training.
   textual file of word-context pairs.
   each pair takes a seperate line.
   the format of a pair is "<word> <context>", i.e. space delimited, where <word> and <context> are strings.
   if we want to prefer some contexts over the others, we should construct the
   training data to contain the bias.

(content below is the README.txt file of the original word2vec software)

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 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