This code lets you experiment with pre-trained word embeddings using plain Python 3 with no additional dependencies.
See the blog post Playing with Word Vectors for a detailed explanation.
This repo only includes a small data file with 1000 words. To get interesting results you'll need to download the pre-trained word vectors from the fastText website.
But don't use the whole 2GB file! The program would use too much memory. Instead, once you download the file take only the top n words, save them to a separate file, and remove the first line. For example:
$ cat data/wiki-news-300d-1M.vec | head -n 60001 | tail -n 60000 > data/vectors.vec
Then you can run:
# Find words related to given word
$ python3 related.pyand:
# Complete analogies
$ python3 analogies.pyThe code doesn't use numpy or any third party dependencies. This is so that anyone can run the code easily using vanilla Python 3.
There is a separate branch that uses numpy for the vector math and achieves about 12x speedup on my laptop.
I'm using the mypy type checker to find bugs as I type in VS Code (using this plugin). However, you don't need to have mypy installed. Python 3 will run the code just fine.
MIT