This project compares performance of 3 word embeddings for the patent classification task using deep neural network, which are:

• GLOVE embeddings of 100 dimensions which was trained on 6 billion tokens corpus
• Uncased BERT-base embeddings (12-layers, 768-hiddens)
• Domain specific embeddings from https://www.researchgate.net/publication/332088506