Deep learning implementation for NLP with NNabla

Tiny implementation of deep learning models for NLP with Sony's NNabla.

Tested environment

Python 3.7.2
NNabla v1.0.11

Models

Language models

A vanilla recurrent neural network language model (language-models/rnnlm/)
LSTM language model (language-models/lstmlm/)
Character-level convolutional LSTM language model (language-models/char-cnn-lstmlm/)
Continuous Bag-of-Words (CBOW) model (language-models/cbow/)

Word embeddings

GloVe model (word-embeddings/glove/)
Poincaré embeddngs (word-embeddings/poincare-embeddings/)

Seq2Seq models

Encoder-decoder (seq2seq/encoder-decoder/)
Encoder-decoder + global attention (seq2seq/encoder-decoder-with-attention/)

Text classifiers

fastText (text-classifications/fasttext/)
Self attention (text-classifications/self_attention/)
LSTM classifier (text-classifications/lstm-classifier/)

Future work

Skip-gram model
Peephole LSTM
GRU
Transformer
ELMo
etc.