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