/seq2seq.pytorch

Sequence-to-Sequence learning using PyTorch

Primary LanguagePythonMIT LicenseMIT

Seq2Seq in PyTorch

This is a complete suite for training sequence-to-sequence models in PyTorch. It consists of several models and code to both train and infer using them.

Using this code you can train:

  • Neural-machine-translation (NMT) models
  • Language models
  • Image to caption generation
  • Skip-thought sentence representations
  • And more...

Models

Models currently available:

Datasets

Datasets currently available:

  • WMT16
  • OpenSubtitles 2016
  • COCO image captions

All datasets can be tokenized using 3 available segmentation methods:

  • Character based segmentation
  • Word based segmentation
  • Byte-pair-encoding (BPE) as suggested by bpe with selectable number of tokens.

After choosing a tokenization method, a vocabulary will be generated and saved for future inference.

Training methods

The models can be trained using several methods:

  • Basic Seq2Seq - given encoded sequence, generate (decode) output sequence. Training is done with teacher-forcing.
  • Multi Seq2Seq - where several tasks (such as multiple languages) are trained simultaneously by using the data sequences as both input to the encoder and output for decoder.
  • Image2Seq - used to train image to caption generators.

Usage

Example training scripts are available in scripts folder. Inference examples are available in examples folder.