Fairseq(-py) is a sequence modeling toolkit written in Python and developed at Facebook’s AI Research. This toolkit allows AI researchers and developers to train customized models for translation, summarization, language modeling, and other text generation tasks. It provides reference implementations of various sequence-to-sequence models, including Long Short-Term Memory (LSTM) networks and a novel convolutional neural network (CNN). This can increase the speed for generating translations than comparable recurrent neural network (RNN) models.
Features
Fairseq toolkit provides reference implementations of various sequence-to-sequence models, including:
- Convolutional Neural Networks (CNN)
- LightConv and DynamicConv models
- Long Short-Term Memory (LSTM) networks
- Transformer (self-attention) networks
- Non-autoregressive Transformers
- multi-GPU (distributed) training on one machine or across multiple machines
Github: https://github.com/pytorch/fairseq
Installation: https://ai.facebook.com/tools/fairseq/
Paper: https://arxiv.org/pdf/1904.01038.pdf
Video: https://www.youtube.com/watch?v=OtgDdWtHvto
Installation
Requirements
- A PyTorch installation
- For training new models, you’ll also need an NVIDIA GPU and NCCL
- Python version 3.6
Install fairseq-py.
pip install fairseqOn MacOS:
CFLAGS="-stdlib=libc++" pip install fairseqFull Documentation: https://fairseq.readthedocs.io/en/latest/
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