LegalEval Large Language Model: Understanding Legal Texts
Stack
Here are the technologies used in this project:
Client
- Client: Semantic Evaluation ACL
Key Contributions
Data Management and Model Optimization
- Developed a custom PyTorch data loader to efficiently process and manage large-scale datasets.
- Optimized LLaMA2 model performance for log probability analysis, achieving a 15% increase in accuracy.
Advanced Model Training
- Implemented model parallelism techniques using:
- Hugging Face’s Accelerate.
- DeepSpeed ZeRO 3.
- Achieved a 30% reduction in training time on A100 GPUs.
Model Fine-Tuning and Experiment Tracking
- Engineered and fine-tuned a Hugging Face decoder model for NLP tasks.
- Utilized Weights & Biases for experiment tracking, leading to:
- 20% improvement in Precision, Recall, and F1 scores.
Distributed Computing and Deployment
- Set up and configured a distributed computing cluster, enhancing:
- Data processing capabilities.
- Seamless model deployment across multiple nodes.
Legal Text Classification
- Executed tasks for SemEval 2023 LegalEval, including:
- Rhetorical Roles Labeling.
- Court Judgment Prediction.
- Contributed to a top 10% performance among 26 participating teams.
Impact
- Demonstrated significant improvements in model accuracy, efficiency, and deployment capabilities.
- Enhanced performance on complex NLP tasks for legal text classification, positioning the project at a competitive level.