.class_implementation_contract_bert.txt

[Reference: See .index.txt for complete file listing]

ContractBERT Implementation Instructions
=====================================

Class: ContractBERT
------------------

Purpose:
Fine-tune and utilize BERT for contract clause understanding.

Implementation Details:

1. Model Architecture
-------------------
- Base: BERT architecture
- Additional layers for contract-specific tasks
- Custom attention mechanisms

2. Fine-tuning Process
--------------------
- Masked Language Modeling (MLM)
- Next Sentence Prediction (NSP)
- Contract-specific objectives

3. Encoding Methods
-----------------
- Clause encoding
- Contextual embeddings
- Attention patterns

Code Structure:
```python
class ContractBERT:
    def __init__(self, model_path, device='cuda'):
        self.device = device
        self.model = AutoModel.from_pretrained(model_path)
        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
        
    def fine_tune(self, train_data, val_data):
        """Fine-tune model on contract data"""
        pass
        
    def encode_clauses(self, clauses):
        """Encode contract clauses"""
        pass
        
    def compute_similarity(self, clause1, clause2):
        """Compute semantic similarity"""
        pass
        
    def save_model(self, path):
        """Save fine-tuned model"""
        pass
```

Key Considerations:
- Gradient accumulation for large batches
- Mixed precision training
- Model compression techniques
- Evaluation metrics