Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks.
We provide reference implementations of various sequence modeling papers:
List of implemented papers
- Convolutional Neural Networks (CNN)
- Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)
- Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
- Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)
- Hierarchical Neural Story Generation (Fan et al., 2018)
- wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)
- LightConv and DynamicConv models