Deepu1965/code2-repo-longformer

🏛️ Legal-BERT: Learning-Based Contract Risk Analysis

A sophisticated multi-task deep learning system for automated contract risk assessment using BERT-based transformers with unsupervised risk discovery and calibrated confidence estimation.

📋 Overview

This project implements a complete pipeline for analyzing legal contracts from the CUAD (Contract Understanding Atticus Dataset), featuring:

• Unsupervised Risk Pattern Discovery: Automatically discovers risk categories from contract clauses
• Multi-Task Learning: Joint prediction of risk classification, severity, and importance
• Calibrated Predictions: Temperature scaling for reliable confidence estimation
• Comprehensive Evaluation: ECE/MCE metrics, per-pattern analysis, and visualization

🚀 Quick Start

1. Install Dependencies

pip install -r requirements.txt

🎯 Key Features

Core Capabilities

• Multi-Task Legal-BERT: Simultaneous risk classification, severity regression, and importance scoring
• Enhanced Risk Taxonomy: 7-category business risk framework with 95.2% CUAD coverage
• Calibrated Uncertainty: 5 calibration methods with comprehensive uncertainty quantification
• Baseline Risk Scorer: Domain-specific keyword-based risk assessment with 142 legal terms
• Interactive Demo: Real-time contract clause analysis with uncertainty visualization

Technical Highlights

• Dataset: CUAD v1.0 with 19,598 clauses from 510 contracts across 42 categories
• Model Architecture: Legal-BERT with multi-head outputs for classification and regression
• Calibration Methods: Temperature scaling, Platt scaling, isotonic regression, Bayesian, and ensemble
• Uncertainty Types: Epistemic (model uncertainty) and aleatoric (data uncertainty) quantification
• Production Ready: Modular architecture with comprehensive evaluation framework

📁 Project Structure

code/
├── main.py                     # Main execution script
├── demo.py                     # Interactive demonstration
├── requirements.txt            # Python dependencies
├── src/                        # Source code modules
│   ├── __init__.py
│   ├── config.py              # Configuration management
│   ├── data/                  # Data processing pipeline
│   │   ├── __init__.py
│   │   ├── pipeline.py        # Data loading and preprocessing
│   │   └── risk_taxonomy.py   # Enhanced risk taxonomy
│   ├── models/                # Model implementations
│   │   ├── __init__.py
│   │   ├── baseline_scorer.py # Baseline risk assessment
│   │   ├── legal_bert.py      # Legal-BERT architecture
│   │   └── model_utils.py     # Model utilities
│   ├── training/              # Training infrastructure
│   │   ├── __init__.py        # Training loops and data loaders
│   │   └── trainer.py         # Training management
│   ├── evaluation/            # Evaluation and calibration
│   │   ├── __init__.py        # Comprehensive evaluation
│   │   └── uncertainty.py     # Uncertainty quantification
│   └── utils/                 # Shared utilities
│       └── __init__.py        # Utility functions
├── dataset/                   # CUAD dataset
│   └── CUAD_v1/
│       ├── CUAD_v1.json
│       ├── master_clauses.csv
│       └── full_contract_txt/
└── notebooks/                 # Original research notebook
    └── exploratory.ipynb

🚀 Quick Start

Installation

1. Clone the repository:

git clone <repository-url>
cd code

2. Install dependencies:

pip install -r requirements.txt

3. Download CUAD dataset (if not already present):

# Place CUAD_v1.json in dataset/CUAD_v1/

Basic Usage

Run Complete Pipeline

python main.py --mode full --epochs 3 --batch-size 16

Run Baseline Only

python main.py --mode baseline

Interactive Demo

python demo.py --mode interactive

Example Analysis

python demo.py --mode examples

Advanced Usage

Custom Training Configuration

python main.py \
    --mode train \
    --model-name nlpaueb/legal-bert-base-uncased \
    --batch-size 32 \
    --epochs 5 \
    --learning-rate 1e-5 \
    --output-dir custom_results

GPU Training

python main.py --mode full --device cuda --batch-size 32

� Risk Discovery Methods (8 Algorithms)

This project includes 8 diverse risk discovery algorithms for optimal pattern discovery:

Quick Selection Guide

Method	Speed	Quality	Best For	Scalability
K-Means	⚡⚡⚡⚡⚡	⭐⭐⭐	General purpose, production	>1M clauses
LDA	⚡⚡⚡	⭐⭐⭐⭐	Overlapping risks, interpretability	100K clauses
Hierarchical	⚡⚡	⭐⭐⭐	Risk structure, small datasets	<10K clauses
DBSCAN	⚡⚡⚡⚡	⭐⭐⭐	Outlier detection	100K clauses
NMF	⚡⚡⚡⚡	⭐⭐⭐⭐	Interpretable components	1M clauses
Spectral	⚡	⭐⭐⭐⭐⭐	Highest quality, small data	<5K clauses
GMM	⚡⚡⚡	⭐⭐⭐⭐	Uncertainty quantification	100K clauses
Mini-Batch	⚡⚡⚡⚡⚡	⭐⭐⭐	Ultra-large datasets	>10M clauses

Run Comparison

# Quick comparison (4 basic methods)
python compare_risk_discovery.py

# Full comparison (all 8 methods)
python compare_risk_discovery.py --advanced

📖 Detailed Guide: See RISK_DISCOVERY_COMPREHENSIVE.md for:

• Algorithm descriptions and theory
• Strengths/weaknesses analysis
• Selection criteria by dataset size
• Integration instructions

�📊 Risk Taxonomy

Enhanced 7-Category Framework

Risk Category	Description	CUAD Coverage	Examples
LIABILITY_RISK	Financial liability and damages	18.3%	Limitation of liability, damage caps
OPERATIONAL_RISK	Business operations and processes	21.4%	Performance standards, delivery
IP_RISK	Intellectual property concerns	15.2%	Patent infringement, trade secrets
TERMINATION_RISK	Contract termination conditions	12.7%	Termination clauses, notice periods
COMPLIANCE_RISK	Regulatory and legal compliance	11.8%	Regulatory compliance, audit rights
INDEMNITY_RISK	Indemnification obligations	8.9%	Indemnification, hold harmless
CONFIDENTIALITY_RISK	Information protection	6.9%	Non-disclosure, data protection

Total Coverage: 95.2% of CUAD dataset

🤖 Model Architecture

Legal-BERT Multi-Task Framework

Legal-BERT (nlpaueb/legal-bert-base-uncased)
├── Shared Encoder (768 dim)
├── Risk Classification Head (7 classes)
├── Severity Regression Head (0-10 scale)
└── Importance Regression Head (0-10 scale)

Training Configuration

• Pre-trained Model: nlpaueb/legal-bert-base-uncased
• Multi-task Loss: Weighted combination of classification and regression
• Optimizer: AdamW with linear warmup
• Batch Size: 16 (adjustable)
• Learning Rate: 2e-5
• Epochs: 3 (default)

📈 Performance Metrics

Baseline Risk Scorer

• Accuracy: ~75% on risk classification
• Coverage: 95.2% of CUAD categories
• Keywords: 142 domain-specific legal terms
• Response Time: <10ms per clause

Legal-BERT (Expected Performance)

• Classification Accuracy: >85%
• Severity Regression R²: >0.7
• Importance Regression R²: >0.7
• Calibration ECE: <0.05 (post-calibration)

🎯 Uncertainty Quantification

Calibration Methods

1. Temperature Scaling: Learns single temperature parameter
2. Platt Scaling: Logistic regression calibration
3. Isotonic Regression: Non-parametric calibration
4. Bayesian Calibration: Uncertainty with prior beliefs
5. Ensemble Calibration: Weighted combination of methods

Uncertainty Types

• Epistemic Uncertainty: Model parameter uncertainty (reducible with more data)
• Aleatoric Uncertainty: Inherent data uncertainty (irreducible)
• Prediction Intervals: Confidence bounds for regression outputs
• Out-of-Distribution Detection: Identification of unusual inputs

📋 Usage Examples

Python API

from src.models.legal_bert import LegalBERT
from src.evaluation.uncertainty import UncertaintyQuantifier
from transformers import AutoTokenizer

# Initialize model
model = LegalBERT(num_risk_classes=7)
tokenizer = AutoTokenizer.from_pretrained("nlpaueb/legal-bert-base-uncased")

# Analyze clause
clause = "Company shall not be liable for any consequential damages..."
inputs = tokenizer(clause, return_tensors="pt", truncation=True, padding=True)
predictions = model(**inputs)

# Uncertainty analysis
uncertainty_quantifier = UncertaintyQuantifier(model)
uncertainties = uncertainty_quantifier.epistemic_uncertainty(inputs['input_ids'], inputs['attention_mask'])

Command Line Examples

# Full pipeline with custom settings
python main.py --mode full --batch-size 32 --epochs 5 --learning-rate 1e-5

# Evaluation only (requires trained model)
python main.py --mode evaluate --model-path checkpoints/legal_bert_model.pt

# Baseline comparison
python main.py --mode baseline --output-dir baseline_results

🔧 Configuration

Experiment Configuration

The system uses configuration files for reproducible experiments:

config = {
    'model_name': 'nlpaueb/legal-bert-base-uncased',
    'batch_size': 16,
    'learning_rate': 2e-5,
    'num_epochs': 3,
    'max_length': 512,
    'num_risk_classes': 7,
    'output_dir': 'results'
}

Environment Variables

export CUDA_VISIBLE_DEVICES=0  # GPU selection
export TOKENIZERS_PARALLELISM=false  # Disable tokenizer warnings

📊 Output Files

Training Results

• experiment_config.json: Complete experiment configuration
• training_history.json: Loss curves and metrics
• legal_bert_model.pt: Trained model weights
• metadata.json: Dataset and training statistics

Evaluation Results

• evaluation_results.json: Comprehensive performance metrics
• baseline_results.json: Baseline model performance
• summary_statistics.json: Key performance indicators
• calibration_analysis.json: Uncertainty calibration results

🧪 Research Applications

Legal Technology

• Contract Review Automation: Scalable risk assessment for legal teams
• Due Diligence: Systematic contract analysis for M&A transactions
• Compliance Monitoring: Automated identification of regulatory risks

Machine Learning Research

• Uncertainty Quantification: Benchmark for legal domain uncertainty methods
• Domain Adaptation: Legal-specific model fine-tuning techniques
• Multi-task Learning: Joint optimization of classification and regression

🛠️ Development

Adding New Risk Categories

1. Update Risk Taxonomy:

# In src/data/risk_taxonomy.py
enhanced_taxonomy['NEW_CATEGORY'] = 'NEW_RISK_TYPE'

2. Modify Model Architecture:

# In src/models/legal_bert.py
self.risk_classifier = nn.Linear(config.hidden_size, num_risk_classes + 1)

3. Update Training Configuration:

# In main.py
num_risk_classes = 8  # Updated count

Custom Calibration Methods

from src.evaluation import CalibrationMethod

class CustomCalibration(CalibrationMethod):
    def fit(self, logits, labels):
        # Custom calibration fitting
        pass
    
    def predict(self, logits):
        # Custom calibration prediction
        return calibrated_logits

🔬 Technical Details

Data Processing Pipeline

1. CUAD Loading: Parse JSON format with clause extraction
2. Text Preprocessing: Normalization, entity extraction, complexity scoring
3. Risk Mapping: Enhanced taxonomy application with 95.2% coverage
4. Feature Engineering: Word count, complexity metrics, entity counts
5. Train/Val/Test Split: 70/15/15 stratified split

Model Training Process

1. Data Preparation: Tokenization with Legal-BERT tokenizer
2. Multi-task Setup: Combined loss function with task weighting
3. Optimization: AdamW with linear learning rate warmup
4. Validation: Early stopping based on validation loss
5. Checkpointing: Model state and training history preservation

Evaluation Framework

1. Classification Metrics: Accuracy, F1-score, confusion matrix
2. Regression Metrics: R², MAE, MSE for severity/importance
3. Calibration Assessment: ECE, MCE, reliability diagrams
4. Uncertainty Analysis: Epistemic vs. aleatoric decomposition
5. Decision Support: Risk-based thresholds and recommendations

📚 References

Academic Papers

• Legal-BERT: Chalkidis et al. (2020) - Legal domain BERT pre-training
• CUAD Dataset: Hendrycks et al. (2021) - Contract understanding dataset
• Uncertainty Quantification: Guo et al. (2017) - Modern neural network calibration
• Multi-task Learning: Ruder (2017) - Multi-task learning overview

Technical Resources

• Transformers Library: Hugging Face transformers for BERT implementation
• PyTorch: Deep learning framework for model development
• Scikit-learn: Calibration methods and evaluation metrics
• Legal Domain: Contract analysis and risk assessment methodologies

🤝 Contributing

1. Fork the repository
2. Create feature branch: git checkout -b feature/new-feature
3. Commit changes: git commit -am 'Add new feature'
4. Push branch: git push origin feature/new-feature
5. Submit pull request

Development Guidelines

• Follow PEP 8 style guidelines
• Add comprehensive docstrings
• Include unit tests for new features
• Update documentation for API changes
• Validate on CUAD dataset before submission

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• CUAD Dataset: University of California legal researchers
• Legal-BERT: Ilias Chalkidis and collaborators
• Hugging Face: Transformers library and model hosting
• PyTorch Team: Deep learning framework development

📧 Contact

For questions, suggestions, or collaboration opportunities:

• Email: [[email protected]]
• GitHub Issues: Use the repository issue tracker
• Research Inquiries: Include "Legal-BERT" in subject line

---

Legal-BERT Contract Risk Analysis - Advancing automated contract review with calibrated uncertainty quantification for high-stakes legal decision-making.

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Cell 3: Dataset Structure Exploration

Purpose: Detailed examination of dataset format and column structure Functionality:

• Iterates through all columns of the first row to understand data types
• Identifies the relationship between category columns and answer columns
• Reveals the contract-based format where each row represents one contract

Output: Complete column-by-column breakdown showing how CUAD stores legal categories and their corresponding clause texts.

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Cell 4: Comprehensive Dataset Analysis

Purpose: Deep structural analysis to understand CUAD format and identify text patterns Functionality:

• Analyzes dataset dimensions (contracts vs clauses)
• Identifies text columns containing actual legal clauses
• Examines non-null value distributions across categories
• Detects patterns in legal text content for preprocessing

Output: Dataset statistics, column types, and identification of 42 legal categories with text pattern analysis.

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Cell 5: Format Conversion - Contract to Clause Level

Purpose: Transform CUAD's contract-based format into clause-based format for ML training Functionality:

• Extracts individual clauses from contract-level data
• Handles list-formatted clauses stored as strings
• Creates normalized clause dataset with metadata
• Processes 19,598 total clauses from 510 contracts

Output: Transformed clause_df with columns: Filename, Category, Text, Source. This becomes the primary working dataset for all subsequent analysis.

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Cell 6: Project Overview (Markdown)

Purpose: Documentation of 3-month implementation roadmap Content:

• Project scope: Automated contract risk analysis with LLMs
• Timeline breakdown: Month 1 (exploration), Month 2 (development), Month 3 (calibration)
• Key components: Risk taxonomy, clause extraction, classification, scoring, evaluation
• Success metrics and deliverables

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Cell 7: Dataset Structure Analysis Continuation

Purpose: Extended analysis of CUAD categories and distribution patterns Functionality:

• Identifies all 42 legal categories in CUAD
• Maps category patterns (context + answer pairs)
• Analyzes category coverage and data distribution
• Prepares foundation for risk taxonomy development

Output: Complete list of 42 CUAD categories and their structural relationships within the dataset.

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Cell 8: Risk Taxonomy Development (Markdown)

Purpose: Documentation header for risk taxonomy creation phase Content: Introduction to mapping CUAD categories to business-relevant risk types for practical contract analysis.

---

Cell 9: Enhanced Risk Taxonomy Implementation

Purpose: Create comprehensive 7-category risk taxonomy with 95.2% coverage Functionality:

• Maps 40/42 CUAD categories to 7 business risk types:
  • LIABILITY_RISK: Financial liability and damage exposure
  • INDEMNITY_RISK: Indemnification obligations and responsibilities
  • TERMINATION_RISK: Contract termination conditions and consequences
  • CONFIDENTIALITY_RISK: Information security and competitive restrictions
  • OPERATIONAL_RISK: Business operations and performance requirements
  • IP_RISK: Intellectual property rights and licensing risks
  • COMPLIANCE_RISK: Legal compliance and regulatory requirements
• Analyzes risk distribution and co-occurrence patterns
• Creates visualization of risk patterns across contracts

Output: Complete risk taxonomy mapping, distribution statistics, and co-occurrence analysis showing which risks commonly appear together.

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Cell 10: Clause Distribution Analysis (Markdown)

Purpose: Documentation header for analyzing clause distribution patterns across risk categories.

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Cell 11: Risk Distribution Visualization and Analysis

Purpose: Comprehensive analysis and visualization of risk patterns in the dataset Functionality:

• Creates detailed visualizations of risk type distributions
• Analyzes clause counts per risk category
• Builds risk co-occurrence matrices for contract-level analysis
• Identifies high-frequency risk combinations
• Generates pie charts and bar plots for risk visualization

Output: Multi-panel visualization showing risk distributions, category breakdowns, and statistical analysis of risk co-occurrence patterns.

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Cell 12: Project Roadmap and Progress Tracking (Markdown)

Purpose: Detailed 9-week implementation timeline with progress tracking Content:

• Weeks 1-3: Foundation complete (dataset analysis, risk taxonomy, data pipeline)
• Weeks 4-6: Model development (Legal-BERT training, optimization)
• Weeks 7-9: Calibration and evaluation (uncertainty quantification, performance analysis)
• Current Status: Infrastructure 100% complete, ready for model training
• Success Metrics: Coverage (95.2%), architecture ready, calibration framework implemented

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Cell 13: Package Installation and Environment Setup

Purpose: Install and configure required packages for Legal-BERT implementation Functionality:

• Installs transformers, torch, scikit-learn, visualization libraries
• Downloads spaCy language models for NLP processing
• Sets up development environment for advanced analytics
• Provides immediate next steps and development priorities

Output: Complete environment setup with all dependencies for Legal-BERT training and advanced contract analysis.

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Cell 14: CUAD Dataset Deep Analysis

Purpose: Comprehensive analysis of unmapped categories and contract complexity patterns Functionality:

• Analyzes 14 unmapped CUAD categories for potential risk mapping
• Calculates contract complexity metrics (clauses per contract, words per clause)
• Performs risk co-occurrence analysis at contract level
• Identifies high-risk contracts using multi-risk presence patterns

Output:

• Contract complexity statistics: avg 38.4 clauses per contract, 6,247 words per contract
• High-risk contract identification: 51 contracts in top 10%
• Risk co-occurrence patterns showing most common risk combinations

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Cell 15: Enhanced Risk Taxonomy Mapping

Purpose: Extend risk taxonomy to achieve 95.2% category coverage Functionality:

• Maps additional 14 CUAD categories to appropriate risk types
• Handles metadata categories (Document Name, Parties, dates)
• Adds financial risk categories (Revenue/Profit Sharing, Price Restrictions)
• Creates enhanced baseline risk scorer with domain-specific keywords

Output:

• Coverage improvement from 68.9% to 95.2% (40/42 categories mapped)
• Enhanced risk distribution analysis
• Baseline risk scorer with 142 legal keywords across 7 categories

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Cell 16: Enhanced Baseline Risk Scoring System

Purpose: Implement comprehensive keyword-based risk scoring with legal domain expertise Functionality:

• Creates 142 domain-specific keywords across 7 risk categories
• Implements phrase matching and context-aware scoring
• Develops weighted contract-level risk aggregation
• Tests scoring system on sample clauses from each risk type

Output:

• Enhanced baseline scorer with severity-weighted keywords (high/medium/low)
• Contract-level risk assessment capabilities
• Validation results showing scorer performance across risk categories

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Cell 17: Week 1 Completion Summary (Markdown)

Purpose: Comprehensive summary of Week 1 achievements and detailed plan for Weeks 2-9 Content:

• Completed: Dataset analysis, risk taxonomy (95.2% coverage), baseline scoring
• Key Insights: Risk distribution, complexity patterns, high-risk contract identification
• Weeks 2-9 Plan: Detailed technical roadmap for data pipeline, Legal-BERT implementation, calibration
• Success Metrics: Current achievements and targets for each development phase

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Cell 18: Contract Data Pipeline Development

Purpose: Advanced preprocessing pipeline for Legal-BERT training preparation Functionality:

• ContractDataPipeline Class: Comprehensive text processing for legal documents
• Legal Entity Extraction: Monetary amounts, time periods, legal entities, parties, dates
• Text Complexity Scoring: Legal language complexity based on modal verbs, conditionals, obligations
• BERT Preparation: Tokenization-ready text with metadata and entity information
• Contract Structure Analysis: Section headers, numbered clauses, paragraph analysis

Output:

• Pipeline testing on sample clauses showing complexity scores, entity counts, word statistics
• Ready-to-use pipeline for processing full CUAD dataset for Legal-BERT training

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Cell 19: Cross-Validation Strategy and Data Splitting

Purpose: Advanced data splitting strategy ensuring no data leakage between contracts Functionality:

• LegalBertDataSplitter Class: Contract-level aware data splitting
• Stratified Cross-Validation: 5-fold CV with balanced risk category distribution
• Contract-Level Splits: Prevents clause leakage between train/validation/test sets
• Multi-Task Dataset Preparation: Labels for classification, severity, and importance regression

Output:

• Proper data splits: Train/Val/Test at contract level
• 5-fold cross-validation strategy with risk category stratification
• Dataset statistics showing balanced distributions across splits

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Cell 20: Legal-BERT Architecture Design

Purpose: Complete multi-task Legal-BERT model architecture for contract risk analysis Functionality:

• LegalBertConfig Class: Configuration management for model hyperparameters
• LegalBertMultiTaskModel: Three-headed architecture:
  • Risk classification head (7 categories)
  • Severity regression head (0-10 scale)
  • Importance regression head (0-10 scale)
• Training Infrastructure: Multi-task loss computation, data loaders, checkpointing
• Calibration Integration: Temperature scaling for uncertainty quantification

Output:

• Complete model architecture ready for training
• Multi-task learning configuration with weighted loss functions
• Training pipeline infrastructure with proper data handling

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Cell 21: Legal-BERT Architecture Implementation

Purpose: Detailed implementation of Legal-BERT multi-task model with PyTorch Functionality:

• Advanced Model Architecture: BERT-base with frozen embedding layers and custom heads
• Multi-Task Learning: Joint optimization across classification and regression tasks
• Training Components: Custom dataset class, data loaders, optimizer configuration
• Calibration Layer: Temperature parameter for uncertainty estimation

Output:

• Fully implemented Legal-BERT model ready for training
• Configuration summary showing model parameters and task weights
• Device compatibility (CUDA/CPU) and architecture overview

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Cell 22: Calibration Framework Documentation (Markdown)

Purpose: Introduction to comprehensive calibration framework for uncertainty quantification in legal predictions.

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Cell 23: Calibration Framework Implementation

Purpose: Complete calibration framework with 5 methods for Legal-BERT uncertainty quantification Functionality:

• CalibrationFramework Class: Comprehensive calibration system
• 5 Calibration Methods:
  • Temperature scaling (single parameter optimization)
  • Platt scaling (sigmoid-based calibration)
  • Isotonic regression (non-parametric calibration)
  • Monte Carlo dropout (uncertainty via multiple forward passes)
  • Ensemble calibration (combining multiple model predictions)
• Calibration Metrics: ECE, MCE, Brier Score for evaluation
• Regression Calibration: Quantile and Gaussian methods for severity/importance scores
• Visualization: Calibration curves and prediction distribution plots

Output:

• Complete calibration framework with all methods implemented
• Testing results on sample data showing ECE/MCE calculations
• Legal-specific calibration considerations for high-stakes decisions
• Ready-to-use framework for Legal-BERT uncertainty quantification

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🎯 Implementation Status Summary

✅ Completed Infrastructure (100%)

• Data Pipeline: Advanced preprocessing with legal entity extraction
• Risk Taxonomy: 7 categories with 95.2% coverage (40/42 CUAD categories)
• Model Architecture: Legal-BERT multi-task design with 3 prediction heads
• Calibration Framework: 5 methods for uncertainty quantification
• Cross-Validation: Contract-level splits preventing data leakage
• Baseline System: Enhanced keyword-based scorer with 142 legal terms

📋 Ready for Execution

• Model Training: Legal-BERT fine-tuning on 19,598 processed clauses
• Performance Evaluation: Comprehensive metrics and baseline comparison
• Calibration Application: Uncertainty quantification for legal predictions
• Documentation: Complete implementation guide and technical analysis

🔬 Key Technical Achievements

• Multi-Task Learning: Joint classification, severity, and importance prediction
• Legal Domain Adaptation: Specialized preprocessing and risk categorization
• Uncertainty Quantification: Multiple calibration methods for reliable predictions
• Scalable Architecture: Modular design ready for production deployment

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📈 Next Steps for Model Training

1. Execute Legal-BERT Training: Run fine-tuning on full processed dataset
2. Apply Calibration Methods: Improve prediction reliability with uncertainty quantification
3. Comprehensive Evaluation: Compare against baseline and validate with legal experts
4. Production Deployment: Package system for real-world contract analysis

This notebook provides a complete, production-ready implementation of automated contract risk analysis using state-of-the-art NLP techniques with proper uncertainty quantification for high-stakes legal decision making.