facebookresearch/fairseq: Facebook AI Research Sequence-to-Sequence Toolkit written in Python. ·
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: We also provide pre-trained models for translation and language modeling with a convenient torch.hub interface: See the PyTorch Hub tutorials for translation and RoBERTa for more examples. The full documentation contains instructions for getting started, training new models and extending fairseq with new model types and tasks. We provide pre-trained models and pre-processed, binarized test sets for several tasks listed below, as well as example training and evaluation commands. We also have more detailed READMEs to reproduce results from specific papers: fairseq(-py) is MIT-licensed. The license applies to the pre-trained models as well. Please cite as: Facebook AI Research Sequence-to-Sequence Toolkit written in Python.