Model Card for RoBERTa Large Model fine-tuned with CUAD dataset

This model is the fine-tuned version of "RoBERTa Large" using CUAD dataset

Model Details

Model Description

The Contract Understanding Atticus Dataset (CUAD) , pronounced "kwad", 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.

• 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

Downstream Use [Optional]

More information needed

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.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.

Training Details

Training Data

See cuad dataset card for further details

Training Procedure

More information needed

Preprocessing

More information needed

Speeds, Sizes, Times

More information needed

Evaluation

Testing Data, Factors & Metrics

Testing Data

Researchers may be interested in several gigabytes of unlabeled contract pretraining data, which is available here .

Factors

More information needed

Metrics

More information needed

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).

Model Examination

More information needed

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019) .

• Hardware Type: More information needed
• Hours used: More information needed
• Cloud Provider: More information needed
• Compute Region: More information needed
• Carbon Emitted: More information needed

Technical Specifications [optional]

Model Architecture and Objective

More information needed

Compute Infrastructure

More information needed

Hardware

More information needed

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} }

Glossary [optional]

More information needed

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

How to Get Started with the Model

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")