To run weight decay experiments and ALICE experiments, see run_{alice,weight_decay}.sh.
In addition to the standard requirements for CUAD, also install pip install vat-pytorch==0.0.6.
This repository contains code for the Contract Understanding Atticus Dataset (CUAD), pronounced "kwad", a dataset for legal contract review curated by the Atticus Project. It is part of the associated paper CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review by Dan Hendrycks, Collin Burns, Anya Chen, and Spencer Ball.
Contract review is a task about "finding needles in a haystack." We find that Transformer models have nascent performance on CUAD, but that this performance is strongly influenced by model design and training dataset size. Despite some promising results, there is still substantial room for improvement. As one of the only large, specialized NLP benchmarks annotated by experts, CUAD can serve as a challenging research benchmark for the broader NLP community.
For more details about CUAD and legal contract review, see the Atticus Project website.
We provide checkpoints for three of the best models fine-tuned on CUAD: RoBERTa-base (~100M parameters), RoBERTa-large (~300M parameters), and DeBERTa-xlarge (~900M parameters).
Researchers may be interested in several gigabytes of unlabeled contract pretraining data, which is available here.
This repository requires the HuggingFace Transformers library. It was tested with Python 3.8, PyTorch 1.7, and Transformers 4.3/4.4.
If you find this useful in your research, please consider citing:
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={NeurIPS},
year={2021}
}