Models

The models submodule contains implementations of various algorithms that can be used in addition to external packages to evaluate and develop new natural language processing systems. A description of which algorithms are used in each scenario can be found on this table

Summary

The following table summarizes each submodule.

Submodule	Description
bert	This submodule includes the BERT-based models for sequence classification, token classification, and sequence encoding.
gensen	This submodule includes a distributed Pytorch implementation based on Horovod of learning general purpose distributed sentence representations via large scale multi-task learning by refactoring https://github.com/Maluuba/gensen
pretrained embeddings	This submodule provides utilities to download and extract pretrained word embeddings trained with Word2Vec, GloVe, fastText methods.
pytorch_modules	This submodule provides Pytorch modules like Gated Recurrent Unit with peepholes.
xlnet	This submodule includes the XLNet-based model for sequence classification.