legmlai/les-audites-affaires-leadboard

Les Audits d'Affaires - Leaderboard

Performance dashboard for LLMs on French business law benchmark with HuggingFace Datasets integration.

🚀 Setup Complete!

HuggingFace Datasets

• —Requests: legmlai/laal-requests - tracks evaluation requests
• —Results: legmlai/laal-results - stores evaluation results

Current Models (with 0 scores)

1. 1.Qwen/Qwen3-14B (Alibaba)
2. 2.jpacifico/Chocolatine-2-14B-Instruct-v2.0.3 (jpacifico)
3. 3.meta-llama/Llama-3.1-8B-Instruct (Meta)

🏃‍♂️ Quick Start

Prerequisites

export HF_TOKEN=your_huggingface_token

Run Leaderboard

cd les-audites-affaires-leadboard
source venv/bin/activate
python app.py

The leaderboard will be available at: http://127.0.0.1:7860

📁 Project Structure

Core Files

• —app.py - Main leaderboard application with HuggingFace integration
• —dataset_manager.py - HuggingFace datasets management
• —requirements.txt - Python dependencies

Setup Scripts

• —create_datasets.py - Initialize HuggingFace datasets
• —setup_initial_models.py - Add initial models with 0 scores

✨ Features

Live Leaderboard

• —Real-time data from HuggingFace datasets
• —Automatic ranking and scoring
• —Category-wise performance breakdown
• —Interactive comparison charts

Model Submissions

• —Submit models directly through the UI
• —Automatic request tracking
• —Email notifications for updates

Pipeline Status Tracking

• —📊 Real-time status: Pending, Processing, Completed, Failed
• —📋 Recent evaluation requests table
• —🔄 Pipeline progress monitoring
• —⏳ Request status updates with emojis

Data Management

• —All data stored in HuggingFace datasets
• —Refresh button for live updates
• —Persistent across sessions
• —Status update automation ready

Token Management

• —Graceful handling when HF_TOKEN not configured
• —Clear instructions for Hugging Face Spaces deployment
• —Read-only mode when token unavailable

🔄 Next Steps

1. 1.Deploy to Hugging Face Spaces: Upload to HF Spaces with HF_TOKEN secret
2. 2.Connect Evaluation Harness: Use automation_example.py as integration guide
3. 3.Real Evaluations: Replace 0 scores with actual evaluation results
4. 4.Webhooks: Set up automatic updates from evaluation pipeline
5. 5.Enhanced Analytics: Add more detailed performance breakdowns

📊 Dataset Schema

Requests Dataset

• —request_id, model_name, model_provider, request_type
• —request_status, contact_email, request_timestamp

Results Dataset

• —result_id, request_id, model_name, model_provider
• —overall_score, score_action_requise, score_delai_legal
• —score_documents_obligatoires, score_impact_financier
• —score_consequences_non_conformite, evaluation_timestamp, is_published

Created by: Mohamad Alhajar (legml.ai) License: MIT