Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/mead-ml/mead-baseline
Deep-Learning Model Exploration and Development for NLP
https://github.com/mead-ml/mead-baseline
baseline bert classification convolutional-neural-networks deep-learning deep-learning-architectures experimentation hacktoberfest keras language-model machine-learning nlp nlp-tasks pytorch recurrent-neural-networks seq2seq tensorflow transformers visdom
Last synced: 6 days ago
JSON representation
Deep-Learning Model Exploration and Development for NLP
- Host: GitHub
- URL: https://github.com/mead-ml/mead-baseline
- Owner: mead-ml
- License: apache-2.0
- Created: 2016-04-24T14:23:02.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2023-10-13T19:42:21.000Z (about 1 year ago)
- Last Synced: 2024-12-05T20:23:03.150Z (16 days ago)
- Topics: baseline, bert, classification, convolutional-neural-networks, deep-learning, deep-learning-architectures, experimentation, hacktoberfest, keras, language-model, machine-learning, nlp, nlp-tasks, pytorch, recurrent-neural-networks, seq2seq, tensorflow, transformers, visdom
- Language: Python
- Homepage:
- Size: 11.2 MB
- Stars: 244
- Watchers: 20
- Forks: 73
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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.
[Documentation](https://github.com/dpressel/mead-baseline/blob/master/docs/main.md)
[Tutorials using Colab](https://github.com/dpressel/mead-tutorials)
[MEAD Hub](https://github.com/mead-ml/hub)
## 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](https://openreview.net/forum?id=r1xEb7J15Q)
### Acknowledgements
- Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)