Roberta Large Cuad

🚀 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.

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.

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