SO_word2vec

A word2vec model trained on Stack Overflow posts

This repository contains information related to the word2vec model presented in paper 'Word Embeddings for the Software Engineering domain' as published on the data showcase track of MSR'18.

Model file

The the pre-trained model is stored in a .bin file (of approximate size 1.5 GB) which can be accessed at this link: http://doi.org/10.5281/zenodo.1199620

Instructions on how to use the model

Prerequisites

To load the model you will need Python 3.5 and the gensim library.

Loading the model

from gensim.models.keyedvectors import KeyedVectors
word_vect = KeyedVectors.load_word2vec_format("SO_vectors_200.bin", binary=True)

Querying the model

Examples of semantic similarity queries

words=['virus','java','mysql']
for w in words:
    try:
        print(word_vect.most_similar(w))
    except KeyError as e:
            print(e)          

print(word_vect.doesnt_match("java c++ python bash".split()))

Examples of analogy queries

print(word_vect.most_similar(positive=['python', 'eclipse'], negative=['java']))

References

• The official gensim docs provide further details and comprehensive documentation on how a word2vec model can be used for various NLP tasks.

• If you want to use this model please cite Efstathiou, V., Chatzilenas, C., Spinellis, D., 2018. "Word Embeddings for the Software Engineering Domain". In Proceedings of the 15th International Conference on Mining Software Repositories. ACM.