mead-baseline 2.0.1

Strong Deep-Learning Baseline algorithms for NLP

Baseline

Baseline is a library for reproducible deep learning research and fast model development for NLP. The library provides easily extensible abstractions and implementations for data loading, model development, training and export of deep learning architectures. It also provides implementations for high-performance, deep learning models for various NLP tasks, against which newly developed models can be compared. Deep learning experiments are hard to reproduce, Baseline provides functionalities to track them. The goal is to allow a researcher to focus on model development, delegating the repetitive tasks to the library.

Documentation

Installation

Pip

Baseline can be installed as a Python package.

pip install mead-baseline

If you are using tensorflow 2 as your deep learning back end you will need to have tensorflow_addons already installed or have it get installed with mead via

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 (8 mile) locally and then mead-baseline

A Note About Versions

Deep Learning Frameworks are evolving quickly, and changes are not always backwards compatible. We recommend recent versions of each framework. Baseline is known to work on most versions of TensorFlow, and is currently being run on versions between 1.13 and and 2.1 .

The PyTorch backend requires at least version 1.3.0.

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/Baseline was selected for a Spotlight Poster at the NeurIPS MLOSS workshop in 2018. OpenReview link