Summary
A skip-gram based implementation of the famous word2vec algorithm for generating text embeddings. The two major portions of work are:
i) word2vec using cross entropy loss and noise contrastive estimation(NCE) loss.
ii) Evaluation of the implementation on wor analogy tasks viz. King + Man - Woman ---> Queen

Scripts:
i) word2vec_basic.py: This file is the main script for training word2vec model.
ii) loss_func.py: This file has the two loss functions cross entropy and nce.
iii) word_analogy.py: This file is for evaluating relation between pairs of words -- called MaxDiff question.

Startup:
word2vec_basic.py [cross_entropy | nce]

References:
https://lilianweng.github.io/lil-log/2017/10/15/learning-word-embedding.html#cross-entropy https://www.cs.toronto.edu/~amnih/papers/wordreps.pdf
https://www.youtube.com/watch?v=kEMJRjEdNzM&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=2
https://en.wikipedia.org/wiki/MaxDiff