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README.md

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+---
+license: apache-2.0
+task_categories:
+- text-generation
+- fill-mask
+language:
+- en
+tags:
+- legal
+- pretraining
+- cpt
+- continued-pretraining
+- legal-nlp
+- law
+size_categories:
+- 1K<n<10K
+---
+
+# Legal Document Chunks for Continued Pretraining
+
+This dataset contains **1281 legal document chunks** extracted from various legal documents across multiple jurisdictions. Each chunk is enriched with comprehensive metadata labels for filtering, analysis, and domain-specific training.
+
+## Dataset Information
+
+- **Total Chunks**: 1,281
+- **Format**: jsonl-text
+- **Sorted**: By document ID and chunk index (maintains document continuity)
+- **Source**: Legal documents processed through enhanced parser with OCR support
+- **Jurisdictions**: uk, us
+- **Practice Areas**: 3 areas (Legal Theory, contract law, employment law...)
+- **Document Types**: educational
+- **Difficulty Levels**: advanced, basic, foundational, intermediate
+
+## Metadata Labels
+
+Each chunk includes the following labels:
+
+| Field | Description | Example Values |
+|-------|-------------|----------------|
+| `chunk_number` | Sequential order (1-1281) | 1, 2, 3... |
+| `document_chunk_index` | Position within source document | 0, 1, 2... |
+| `document_id` | Unique document identifier | UUID |
+| `document_title` | Document name | "basic laws book 2016" |
+| `jurisdiction` | Legal jurisdiction | uk, us, eu, international |
+| `practice_area` | Legal domain | employment law, contract law |
+| `document_type` | Document classification | educational, case law, statute |
+| `topic` | Specific legal topic | "Employment Law - Discrimination" |
+| `sample_type` | Content structure type | statutory_interpretation, case_analysis |
+| `difficulty` | Complexity level | basic, intermediate, advanced, expert |
+| `classification_confidence` | Auto-classification confidence | 0.0 - 1.0 |
+| `original_filename` | Source file name | "basic-laws-book-2016.pdf" |
+
+## Format Details
+
+**JSONL Text Format**: Each line contains a JSON object with a `text` field (formatted content) and `meta` field (structured labels). Compatible with HuggingFace datasets, Axolotl, Unsloth, and other training frameworks.
+
+## Intended Use
+
+This dataset is designed for:
+- **Continued Pretraining (CPT)**: Domain adaptation for legal language models
+- **Legal NLP Research**: Training and evaluating legal text understanding models
+- **Domain Transfer Learning**: Fine-tuning general models for legal applications
+- **Legal AI Development**: Building specialized legal assistance systems
+
+## Example Usage
+
+```python
+from datasets import load_dataset
+
+# Load the full dataset
+dataset = load_dataset("YOUR_USERNAME/legal-chunks-pretraining")
+
+# Filter by jurisdiction
+uk_chunks = dataset.filter(lambda x: x['jurisdiction'] == 'uk')
+
+# Filter by difficulty
+advanced_chunks = dataset.filter(lambda x: x['difficulty'] == 'advanced')
+
+# Filter by practice area
+employment_chunks = dataset.filter(lambda x: x['practice_area'] == 'employment law')
+```
+
+## Data Quality
+
+- ✅ **Ordered**: Chunks are sorted by document ID and index for coherent reading
+- ✅ **Classified**: Automatic classification with confidence scores
+- ✅ **Diverse**: Multiple jurisdictions, practice areas, and difficulty levels
+- ✅ **Rich Metadata**: 12+ labels per chunk for advanced filtering
+- ✅ **OCR Support**: Enhanced parsing for scanned documents
+
+## Dataset Statistics
+
+- **Documents**: 13 unique source documents
+- **Average Chunk Length**: ~956 characters
+- **Jurisdictions**: 2 (uk, us)
+- **Practice Areas**: 3 legal domains
+- **Difficulty Distribution**: advanced, basic, foundational, intermediate
+
+## Citation
+
+Generated using the Global Legal AI Training Platform with enhanced document processing and automatic classification.
+
+**Platform Features**:
+- OCR-enhanced PDF parsing with PyMuPDF + EasyOCR
+- GPU-accelerated classification (MPS/CUDA support)
+- Quantized INT8 inference for efficient labeling
+- Multi-jurisdiction legal domain classification
+
+## License
+
+Apache 2.0
+
+## Acknowledgments
+
+This dataset was created using advanced NLP techniques for legal document processing, including:
+- Zero-shot classification for practice area detection
+- Difficulty estimation based on text complexity
+- Topic extraction using legal domain models