Commit efc5e10a authored by ilyasu's avatar ilyasu

added readme

parent e2bd524b
We provide an implementation of the Continuous Bag-of-Words (CBOW) and the Skip-gram model (SG).
Given a text corpus, the word2vec program learns a vector for every word using the Continuous
Bag-of-Words or the Skip-Gram model. The user needs 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
- Whether hierarchical sampling is used
- Whether negative sampling is used, and if so, how many negative samples should be used
- A threshold for downsampling frequent words
- Number of threads to use
- Whether to save the vectors in a text format or a binary format
Thus the programs require a very modest number of parameter. In particular, learning rates
need not be selected.
The file downloads a small (100MB) text corpus, and trains a 200-dimensional CBOW model
with a window of size 5, negative sampling with 5 negative samples, a downsampling of 1e-3, 12 threads, and binary files.
./word2vec -train text8 -output vectors.bin -cbow 1 -size 200 -window 5 -negative 5 -hs 0 -sample 1e-3 -threads 12 -binary 1
Then, to evaluate the fidelity of our vectors, we can run the command, which will run
a battery of tests on the vectors to determine their fidelity. The tests evaluate
the vectors' ability to perform linear analogies.
./distance vectors.bin
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