Register
Login
Resources
Docs Blog Datasets Glossary Case Studies Tutorials & Webinars
Product
Data Engine LLMs Platform Enterprise
Pricing Explore
Connect to our Discord channel
Tolstoyevsky da03dbe866
Fixed eval.dvc and moved its command to a script
5 years ago
48ca497c9d
Init DVC + downloaded and tracking data
5 years ago
b41c74dc5b
Add code for "Pay Less Attention with Lightweight and Dynamic Convolutions" (#473)
5 years ago
da03dbe866
Fixed eval.dvc and moved its command to a script
5 years ago
b41c74dc5b
Add code for "Pay Less Attention with Lightweight and Dynamic Convolutions" (#473)
5 years ago
c92f8261fc
Evaluation pipeline built
5 years ago
42be3ebd41
Merge internal changes (#483)
5 years ago
bbb4120b00
Support custom Dictionary implementations in 'preprocess.py' (#448)
5 years ago
b15f5f5384
New command line option '--user-dir' (#440)
5 years ago
a15acdb062
Architecture settings and readme updates
6 years ago
e734b0fa58
Initial commit
6 years ago
e734b0fa58
Initial commit
6 years ago
7d7f706f0c
Change logo size
5 years ago
829bd8ce5f
Add standalone binaries
5 years ago
e734b0fa58
Initial commit
6 years ago
5 years ago
c92f8261fc
Evaluation pipeline built
5 years ago
829bd8ce5f
Add standalone binaries
5 years ago
cea0e4b9ea
stitch preprocessing pipeline
5 years ago
f1c69c3245
Small things - hyperparam yaml, script to clean corrupted unicode
5 years ago
42be3ebd41
Merge internal changes (#483)
5 years ago
829bd8ce5f
Add standalone binaries
5 years ago
c92f8261fc
Evaluation pipeline built
5 years ago
Storage Buckets
Data Pipeline
Legend
DVC Managed File
Git Managed File
Metric
Stage File
External File

README.md

You have to be logged in to leave a comment. Sign In

Introduction

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. It provides reference implementations of various sequence-to-sequence models, including:

Fairseq features:

  • multi-GPU (distributed) training on one machine or across multiple machines
  • fast generation on both CPU and GPU with multiple search algorithms implemented:
  • large mini-batch training even on a single GPU via delayed updates
  • fast half-precision floating point (FP16) training
  • extensible: easily register new models, criterions, tasks, optimizers and learning rate schedulers

We also provide pre-trained models for several benchmark translation and language modeling datasets.

Model

Requirements and Installation

Currently fairseq requires PyTorch version >= 1.0.0. Please follow the instructions here: https://github.com/pytorch/pytorch#installation.

If you use Docker make sure to increase the shared memory size either with --ipc=host or --shm-size as command line options to nvidia-docker run.

After PyTorch is installed, you can install fairseq with:

pip install -r requirements.txt
python setup.py build develop

Getting Started

The full documentation contains instructions for getting started, training new models and extending fairseq with new model types and tasks.

Pre-trained models and examples

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:

Join the fairseq community

License

fairseq(-py) is BSD-licensed. The license applies to the pre-trained models as well. We also provide an additional patent grant.

Credits

This is a PyTorch version of fairseq, a sequence-to-sequence learning toolkit from Facebook AI Research. The original authors of this reimplementation are (in no particular order) Sergey Edunov, Myle Ott, and Sam Gross.

Tip!

Press p or to see the previous file or, n or to see the next file

About

A fork for fairseq, migrated to DVC and used for NLP research.

Collaborators 1

Comments

Loading...