JuDDGES Project Overview¶

What is JuDDGES?¶

JuDDGES (Judicial Decision Data Gathering, Encoding, and Sharing) is a comprehensive research platform designed to revolutionize the accessibility and analysis of judicial decisions across varied legal systems using advanced Natural Language Processing and Human-In-The-Loop technologies.

Mission Statement¶

Overcome barriers related to resources, language, data, and format inhomogeneity in legal research by creating Europe's most comprehensive legal research repository while advancing empirical legal research through Open Science principles.

Key Features¶

🌍 Multilingual Support¶

Polish Legal System: Full coverage of Polish court decisions, Supreme Administrative Court (NSA)
English Legal System: England & Wales court appeals
Expandable: Architecture supports addition of new jurisdictions

🤖 Advanced NLP Capabilities¶

Semantic Search: Vector-based search across millions of legal documents
Information Extraction: Automated extraction of structured information from legal texts
Fine-tuned LLMs: Domain-specific language models trained on legal corpora
Multilingual Embeddings: Legal-specialized embeddings for semantic understanding

📊 Comprehensive Evaluation¶

N-gram Metrics: ROUGE, exact match, precision/recall for structured data
LLM-as-Judge: AI-powered qualitative assessment
Multi-seed Testing: Statistical robustness through repeated evaluations

🔬 Open Science¶

All software, tools, and datasets openly available
Reproducible pipelines using DVC
Public HuggingFace repositories
Comprehensive documentation

Target Domains¶

Swiss Franc Loan Litigation: Comprehensive analysis of Polish court decisions on Swiss franc-denominated loans
Personal Rights Cases: Polish legal rights extraction and analysis
English Appeals: Court of Appeal decisions from England & Wales
Administrative Law: Polish Supreme Administrative Court decisions
Tax Interpretations: Polish Ministry of Finance tax rulings

Research Vision¶

JuDDGES aims to democratize access to legal knowledge by:

Breaking down language barriers in cross-jurisdictional legal research
Automating labor-intensive legal document analysis
Enabling large-scale empirical legal studies
Providing tools for comparative law research
Supporting evidence-based policy making

Project Consortium¶

A tri-national collaboration between:

Poland: Legal AI research and data acquisition
United Kingdom: Court system analysis and appeals data
France: Research coordination and methodology

Technology Stack¶

Vector Database: Weaviate for semantic search
Embeddings: sdadas/mmlw-roberta-large multilingual legal model
Language Models: Llama 3.1/3.2, Mistral, Bielik, Qwen, Phi-4
Training: PEFT/LoRA fine-tuning with DeepSpeed
Orchestration: DVC for reproducible ML pipelines
Infrastructure: Docker, Prefect, MongoDB, HuggingFace Hub

Architecture Documentation¶

For comprehensive visual guides to the system architecture, see:

System Architecture - High-level component overview with interactive diagrams
Data Flow Pipeline - Complete data transformation journey from raw documents to insights
DVC Pipeline - Reproducible ML workflows and pipeline stages
Weaviate Integration - Vector database schema and operations
Model Training Flow - Fine-tuning and inference workflows
Component Relationships - Module dependencies and interactions

These documents provide detailed Mermaid diagrams showing:

System component interactions
Data transformation stages and formats
Training and inference pipelines
Vector database architecture
Configuration management
Error handling strategies

Getting Started¶

See our comprehensive guides:

License¶

Open source under [appropriate license] - see LICENSE file for details.

Contact¶

For questions, issues, or collaboration inquiries:

GitHub Issues: [repository issues page]
Project Website: [if available]
Email: [contact email]