roberta-base-on-cuad
| Property | Value |
|---|---|
| License | MIT |
| Language | English |
| Framework | PyTorch (Transformers) |
| Paper | View Paper |
What is roberta-base-on-cuad?
roberta-base-on-cuad is a specialized question-answering model designed for legal contract analysis. Developed by Mohammed Rakib, this model builds upon the RoBERTa architecture and is specifically fine-tuned on the Contract Understanding Atticus Dataset (CUAD). It achieved a significant improvement in performance with an AUPR score of 46.6% compared to the original RoBERTa-base's 42.6%.
Implementation Details
The model is implemented using the Transformers library and PyTorch framework. It was trained using V100/P100 GPUs on Google Colab Pro, making it an accessible solution for legal document analysis. The model's development focused on overcoming the challenges of processing lengthy contract documents in a low-resource environment.
- Built on RoBERTa-base architecture
- Optimized for processing both digital and scanned contracts
- Integrated with Tesseract OCR for handling non-searchable documents
- Features custom preprocessing pipeline for legal text
Core Capabilities
- Contract clause identification and extraction
- Legal document question answering
- Support for both searchable and scanned contract analysis
- Efficient contract review automation
- Legal jargon interpretation assistance
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized optimization for legal contract analysis and its superior performance on the CUAD dataset. It bridges the gap between legal professionals and non-experts by making contract review more accessible and efficient.
Q: What are the recommended use cases?
The model is ideal for legal due diligence, contract review automation, clause extraction, and helping non-legal professionals understand complex contract terms. It's particularly useful for law firms, corporate legal departments, and business professionals who regularly deal with contracts.
