🚀 RoBERTa Large Model fine-tuned with CUAD dataset
This model is a fine-tuned version of "RoBERTa Large" using the CUAD dataset, designed for legal contract review.
🚀 Quick Start
Use the code below to get started with the model.
Click to expand
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("akdeniz27/roberta-large-cuad")
model = AutoModelForQuestionAnswering.from_pretrained("akdeniz27/roberta-large-cuad")
✨ Features
- Based on the "RoBERTa Large" model, fine-tuned using the CUAD dataset for legal contract review.
- Can serve as a challenging research benchmark for the broader NLP community.
📚 Documentation
Model Details
Model Description
The Contract Understanding Atticus Dataset (CUAD), pronounced "kwad", is a dataset for legal contract review curated by the Atticus Project.
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.
Property |
Details |
Developed by |
TheAtticusProject |
Shared by [Optional] |
HuggingFace |
Model Type |
Language model |
Language(s) (NLP) |
en |
License |
More information needed |
Related Models |
RoBERTA |
Parent Model |
RoBERTA Large |
Resources for more information |
GitHub Repo Associated Paper |
Uses
Direct Use
Legal contract review
Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
⚠️ Important Note
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Training Details
Training Data
See cuad dataset card for further details
Evaluation
Testing Data
Extra Data
Researchers may be interested in several gigabytes of unlabeled contract pretraining data, which is available here.
Results
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).
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Technical Specifications [optional]
Software
The HuggingFace Transformers library. It was tested with Python 3.8, PyTorch 1.7, and Transformers 4.3/4.4.
Citation
BibTeX:
@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}
}
More Information [optional]
For more details about CUAD and legal contract review, see the Atticus Project website.
Model Card Authors [optional]
TheAtticusProject
Model Card Contact
TheAtticusProject, in collaboration with the Ezi Ozoani and the HuggingFace Team