pytorch-rex 0.0.1

A toolkit for Relation Extraction and more...

A toolkit for Relation & Event eXtraction (REx) and more...

This project has not been finished yet, so be careful when using it, or wait until the first release comes out.

This project is suffering from the second-system effect. I would like to cut some features to make this going smoothly.

Accelerate seems to be a very sweet wrapper for multi-GPU, TPU training, we highly recommend you to use such frameworks, instead of adding hard codes on your own.

⚙️Installation

Make sure you have installed all the dependencies below.

• Python>=3.6
  • torch>=1.2.0 : project is developed with torch==1.7.1, should be compatable with >=1.2.0 versions
  • numpy>=1.19.0
  • scikit-learn>=0.21.3
  • click>=7.1.2
  • omegaconf>=2.0.6
  • loguru>=0.5.3
  • tqdm>=4.61.1
  • transformers>=4.8.2

$ git clone https://github.com/Spico197/REx.git
$ cd REx
$ pip install -e .

# or you can download and install from pypi, not recommend for now
$ pip install pytorch-rex -i https://pypi.org/simple

🚀QuickStart

Checkout the examples folder.

Name	Model	Dataset	Task
SentRE-MCML	PCNN	IPRE	Sentence-level Multi-class multi-label relation classification
BagRE	PCNN+ONE	NYT10	Bag-level relation classification (Multi-Instance Learning, MIL)
JointERE	CasRel	WebNLG	Jointly entity relation extraction

✈️Abilities

Dataset

• IPRE preprocess
• NYT10

Tasks

• Chinese sentence-level relation extraction
• English bag-level relation extraction

Modules & Models

• Piecewise CNN
• PCNN + ONE
• PCNN + ATT

🌴Development

Make sure you have installed the following packages:

• coverage
• flake8
• sphinx
• sphinx_rtd_theme

Build

$ make all

Test

pip install coverage
coverage run -m unittest -v && coverage report

# or just test without coverage report
make test

# or test with report
make test_report

Docs

cd docs
sphinx-apidoc -o . ..
make html

# or just
make docs

Update

• v0.0.1: change LabelEncoder.to_binary_labels into convert_to_multi_hot or convert_to_one_hot

🔑LICENCE

MIT