Strong Deep-Learning Baseline algorithms for NLP
Project description
MEAD
MEAD is a library for reproducible deep learning research and fast model development for NLP. It provides easily extensible abstractions and implementations for data loading, model development, training, experiment tracking and export to production.
It also provides implementations of high-performance deep learning models for various NLP tasks, against which newly developed models can be compared. Deep learning experiments are hard to reproduce, MEAD provides functionalities to track them. The goal is to allow a researcher to focus on model development, delegating the repetitive tasks to the library.
Installation
Pip
Baseline can be installed as a Python package.
pip install mead-baseline
You will need to have
tensorflow_addons
already installed or have it get installed directly with:
pip install mead-baseline[tf2]
From the repository
If you have a clone of this repostory and want to install from it:
cd layers
pip install -e .
cd ../
pip install -e .
This first installs mead-layers
AKA 8 mile, a tiny layers API containing PyTorch and TensorFlow primitives, locally and then mead-baseline
Dockerhub
We use Github CI/CD to automatically release TensorFlow and PyTorch via this project:
https://github.com/mead-ml/mead-gpu
Links to the latest dockerhub images can be found there
A Note About Versions
Deep Learning Frameworks are evolving quickly and changes are not always backwards compatible. We recommend recent versions of whichever framework is being used underneath. We currently test on TF versions 2.1.0 and 2.4.1. The PyTorch backend requires at least version 1.3.0, though we recommend using a more recent version.
Citing
If you use the library, please cite the following paper:
@InProceedings{W18-2506,
author = "Pressel, Daniel
and Ray Choudhury, Sagnik
and Lester, Brian
and Zhao, Yanjie
and Barta, Matt",
title = "Baseline: A Library for Rapid Modeling, Experimentation and
Development of Deep Learning Algorithms targeting NLP",
booktitle = "Proceedings of Workshop for NLP Open Source Software (NLP-OSS)",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "34--40",
location = "Melbourne, Australia",
url = "http://aclweb.org/anthology/W18-2506"
}
MEAD was selected for a Spotlight Poster at the NeurIPS MLOSS workshop in 2018. OpenReview link
Acknowledgements
- Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)
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