HF for Legal - Notebooks Collection

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This collection of notebooks explores AI techniques for legal practice and research, focusing on French legal systems and taxation.

Key Objectives:

  1. Develop advanced natural language processing models for legal text analysis

  2. Implement efficient retrieval systems for large-scale legal databases

  3. Create benchmarks for evaluating AI models in legal and taxation domains

Featured Notebooks:

  1. Transformer-based Denoising AutoEncoder: Specializes sentence embedding models for legal knowledge.

  2. LegalKit Retrieval: Implements fast binary search with scalar rescoring for French legal codes.

  3. Massive Text Embedding Benchmark: Extends MTEB framework with a French taxation retrieval task.

These notebooks demonstrate innovative approaches to handling complex legal language, efficient information retrieval, and performance evaluation in the legal AI field.