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
Extra 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.
Click to expand
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("akdeniz27/roberta-large-cuad")
model = AutoModelForQuestionAnswering.from_pretrained("akdeniz27/roberta-large-cuad")
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