.class_implementation_ledgar_dataset.txt

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

LEDGAR Dataset Implementation Instructions
========================================

Class: LedgarDataset
-------------------

Purpose:
Handle the LEDGAR dataset processing and management for contract clause analysis.

Implementation Details:

1. Data Loading
--------------
- Load JSON format contract clauses
- Parse metadata and labels
- Handle multi-label classification

2. Preprocessing
---------------
- Clean text (remove special characters, normalize whitespace)
- Tokenization using ContractBERT tokenizer
- Handle maximum sequence length

3. Data Splits
-------------
- Create stratified splits for training/validation
- Maintain label distribution
- Handle imbalanced classes

4. Features
----------
- Extract clause embeddings
- Create attention masks
- Generate label encodings

Code Structure:
```python
class LedgarDataset:
    def __init__(self, data_path, max_length=512):
        self.data_path = data_path
        self.max_length = max_length
        self.tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
        
    def load_data(self):
        """Load and parse LEDGAR JSON data"""
        pass
        
    def preprocess_clauses(self):
        """Clean and preprocess clause text"""
        pass
        
    def create_splits(self):
        """Create stratified data splits"""
        pass
        
    def get_clause_embeddings(self):
        """Generate clause embeddings"""
        pass
```

Key Considerations:
- Memory efficient data loading
- Proper handling of long sequences
- Robust error handling
- Caching mechanisms for processed data