mofidy the readme
-### Models
+# 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](../../README.md#content)
-This includes:
-* [BERT](./bert/common.py)
-* [GenSen](./gensen/gensen.py)
-* Pretrained embeddings ([Word2Vec](./pretrained_embeddings/word2vec.py),
-[fastText](./pretrained_embeddings/fasttext.py),
-[GloVe](./pretrained_embeddings/glove.py))
-* Pytorch's conditional Gated Recurrent Unit ([GRU](./pytorch_modules/conditional_gru.py))
\ No newline at end of file
+## Summary
+
+The following table summarizes each submodule.
+
+|Submodule|Description|
+|[bert](./bert/README.md)| This submodule includes the BERT-based models for sequence classification, token classification, and sequence necoding.|
+|[gensen](./gensen/README.md)| This submodule includes a distributed Pytorch implementation based on [Horovod](https://github.com/horovod/horovod) of [learning general purpose distributed sentence representations via large scale multi-task learning](https://arxiv.org/abs/1804.00079) by refactoring https://github.com/Maluuba/gensen|
+|[pretrained embeddings](./pretrained_embeddings) | This submodule provides utilities to download and extractor pretrained word embeddings trained with Word2Vec, GloVe, fastText methods.|
+|[pytorch_modules](./pytorch_modules/README.md)| This submodule provides Pytorch modules like Gated Recurrent Unit with peepholes. |
