Datasets:

Cour-de-cassation
/

alpaca_ccass_motivations_sommaires_titres

license: apache-2.0
task_categories:
  - summarization
  - text-generation
  - feature-extraction
tags:
  - legal
  - droit
  - jurisprudence
  - cassation
  - judilibre
pretty_name: >-
  Training dataset for summarizing and titling decisions of the French Court of
  Cassation
size_categories:
  - 10K<n<100K
language:
  - fr

Dataset Description

Repository: https://huggingface.co/datasets/maurya/alpaca_ccass_motivations_sommaires_titres
Leaderboard: N/A
Point of Contact: Amaury Fouret

Training dataset for summarizing and titling decisions of the French Court of cassation based on motivations

This alpaca-format dataset is designed to train models for summarizing and titling French Supreme Court decisions based on the grounds of them. Created with a view to producing metadata for decisions not published in the bulletin, this dataset aims to simplify the development of annotation and categorization tools, and is positioned as a facilitator for jurisprudential processing and information organization.

Dataset Structure

The dataset includes hierarchical keyword extraction task that generates structured legal taxonomies from court decisions. The hierarchical keywords follow a specific format using special tokens to denote a hierarchical relationship between keywords of different levels.

Special Tokens

When fine-tuning models on this dataset, you will need to add the following special tokens to your tokenizer to properly handle the hierarchical keyword structure:

Required Special Tokens

additional_special_tokens = [
    "<start_hierarchy>", "<end_hierarchy>",
    "<start_level1>", "<end_level1>",
    "<start_level2>", "<end_level2>",
    "<start_level3>", "<end_level3>",
    "<start_level4>", "<end_level4>",
    "<start_level5>", "<end_level5>",
    "<start_level6>", "<end_level6>",
    "<start_level7>", "<end_level7>",
    "<start_level8>", "<end_level8>",
    "<start_level9>", "<end_level9>",
    "<start_level10>", "<end_level10>",
    "<start_level11>", "<end_level11>",
    "<start_level12>", "<end_level12>",
    "<start_level13>", "<end_level13>"
]

Example Output Format

The hierarchical keywords are structured as follows:

<start_hierarchy><start_level1>REPRESENTATION DES SALARIES<end_level1><start_level2>Comité d'entreprise<end_level2><start_level3>Fonctionnement<end_level3><start_level4>Réunion<end_level4><start_level5>Présidence<end_level5><end_hierarchy>

Implementation

Adding Special Tokens to Your Tokenizer

from transformers import AutoTokenizer

# Load your base tokenizer
tokenizer = AutoTokenizer.from_pretrained("your-base-model")

# Add the special tokens
tokenizer.add_tokens(additional_special_tokens, special_tokens=True)

# Don't forget to resize your model's token embeddings
model.resize_token_embeddings(len(tokenizer))

Configuration File Setup

For YAML configuration files:

special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
  pad_token: "[PAD]"

additional_special_tokens:
  - "<start_hierarchy>"
  - "<end_hierarchy>"
  - "<start_level1>"
  - "<end_level1>"
  # ... (include all tokens up to level 13)

Use Cases

Legal Text Summarization: Generate concise summaries of court decisions
Hierarchical Classification: Extract structured legal taxonomies
Metadata Generation: Create standardized metadata for unpublished decisions
Legal Research Tools: Facilitate automated categorization and search

Dataset Features

Alpaca instruction format for easy integration with popular training frameworks
Hierarchical keyword structures with up to 14 levels of legal classification
Specialized for French Court of Cassation decisions and legal reasoning patterns

Feedback

If you have any feedback, please reach out at Amaury Fouret.