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| JURISPRUDENCE_LAST_EXPORT_DATETIME = "2024-10-25 01:05:25" | ||
| JURISPRUDENCE_LAST_EXPORT_DATETIME = "2024-10-28 01:05:49" |
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| <p align="center"><img src="https://raw.githubusercontent.com/antoinejeannot/jurisprudence/artefacts/jurisprudence.svg" width=650></p> | ||
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| [](https://huggingface.co/datasets/antoinejeannot/jurisprudence) [](https://github.com/antoinejeannot/jurisprudence) | ||
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| # ✨ Jurisprudence, release v2024.10.28 🏛️ | ||
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| Jurisprudence is an open-source project that automates the collection and distribution of French legal decisions. It leverages the Judilibre API provided by the Cour de Cassation to: | ||
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| - Fetch rulings from major French courts (Cour de Cassation, Cour d'Appel, Tribunal Judiciaire) | ||
| - Process and convert the data into easily accessible formats | ||
| - Publish & version updated datasets on Hugging Face every few days. | ||
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| It aims to democratize access to legal information, enabling researchers, legal professionals and the public to easily access and analyze French court decisions. | ||
| Whether you're conducting legal research, developing AI models, or simply interested in French jurisprudence, this project might provide a valuable, open resource for exploring the French legal landscape. | ||
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| ## 📊 Exported Data | ||
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| | Jurisdiction | Jurisprudences | Oldest | Latest | Tokens | JSONL (gzipped) | Parquet | | ||
| |--------------|----------------|--------|--------|--------|-----------------|---------| | ||
| | Cour d'Appel | 396,317 | 1996-03-25 | 2024-10-22 | 1,981,675,335 | [Download (1.74 GB)](https://huggingface.co/datasets/antoinejeannot/jurisprudence/resolve/main/cour_d_appel.jsonl.gz?download=true) | [Download (2.90 GB)](https://huggingface.co/datasets/antoinejeannot/jurisprudence/resolve/main/cour_d_appel.parquet?download=true) | | ||
| | Tribunal Judiciaire | 82,085 | 2023-12-14 | 2024-10-22 | 291,028,506 | [Download (263.20 MB)](https://huggingface.co/datasets/antoinejeannot/jurisprudence/resolve/main/tribunal_judiciaire.jsonl.gz?download=true) | [Download (436.65 MB)](https://huggingface.co/datasets/antoinejeannot/jurisprudence/resolve/main/tribunal_judiciaire.parquet?download=true) | | ||
| | Cour de Cassation | 537,252 | 1860-08-01 | 2024-10-24 | 1,107,801,271 | [Download (932.25 MB)](https://huggingface.co/datasets/antoinejeannot/jurisprudence/resolve/main/cour_de_cassation.jsonl.gz?download=true) | [Download (1.58 GB)](https://huggingface.co/datasets/antoinejeannot/jurisprudence/resolve/main/cour_de_cassation.parquet?download=true) | | ||
| | **Total** | **1,015,654** | **1860-08-01** | **2024-10-24** | **3,380,505,112** | **2.90 GB** | **4.90 GB** | | ||
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| <i>Latest update date: 2024-10-28</i> | ||
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| <i># Tokens are computed using GPT-4 tiktoken and the `text` column.</i> | ||
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| ## 🤗 Hugging Face Dataset | ||
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| The up-to-date jurisprudences dataset is available at: https://huggingface.co/datasets/antoinejeannot/jurisprudence in JSONL (gzipped) and parquet formats. | ||
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| This allows you to easily fetch, query, process and index all jurisprudences in the blink of an eye! | ||
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| ### Usage Examples | ||
| #### HuggingFace Datasets | ||
| ```python | ||
| # pip install datasets | ||
| import datasets | ||
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| dataset = load_dataset("antoinejeannot/jurisprudence") | ||
| dataset.shape | ||
| >> {'tribunal_judiciaire': (58986, 33), | ||
| 'cour_d_appel': (378392, 33), | ||
| 'cour_de_cassation': (534258, 33)} | ||
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| # alternatively, you can load each jurisdiction separately | ||
| cour_d_appel = load_dataset("antoinejeannot/jurisprudence", "cour_d_appel") | ||
| tribunal_judiciaire = load_dataset("antoinejeannot/jurisprudence", "tribunal_judiciaire") | ||
| cour_de_cassation = load_dataset("antoinejeannot/jurisprudence", "cour_de_cassation") | ||
| ``` | ||
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| Leveraging datasets allows you to easily ingest data to [PyTorch](https://huggingface.co/docs/datasets/use_with_pytorch), [Tensorflow](https://huggingface.co/docs/datasets/use_with_tensorflow), [Jax](https://huggingface.co/docs/datasets/use_with_jax) etc. | ||
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| #### BYOL: Bring Your Own Lib | ||
| For analysis, using polars, pandas or duckdb is quite common and also possible: | ||
| ```python | ||
| url = "https://huggingface.co/datasets/antoinejeannot/jurisprudence/resolve/main/cour_de_cassation.parquet" # or tribunal_judiciaire.parquet, cour_d_appel.parquet | ||
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| # pip install polars | ||
| import polars as pl | ||
| df = pl.scan_parquet(url) | ||
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| # pip install pandas | ||
| import pandas as pd | ||
| df = pd.read_parquet(url) | ||
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| # pip install duckdb | ||
| import duckdb | ||
| table = duckdb.read_parquet(url) | ||
| ``` | ||
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| ## 🪪 Citing & Authors | ||
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| If you use this code in your research, please use the following BibTeX entry: | ||
| ```bibtex | ||
| @misc{antoinejeannot2024, | ||
| author = {Jeannot Antoine and {Cour de Cassation}}, | ||
| title = {Jurisprudence}, | ||
| year = {2024}, | ||
| howpublished = {\url{https://github.com/antoinejeannot/jurisprudence}}, | ||
| note = {Data source: API Judilibre, \url{https://www.data.gouv.fr/en/datasets/api-judilibre/}} | ||
| } | ||
| ``` | ||
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| This project relies on the [Judilibre API par la Cour de Cassation](https://www.data.gouv.fr/en/datasets/api-judilibre/), which is made available under the Open License 2.0 (Licence Ouverte 2.0) | ||
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| It scans the API every 3 days at midnight UTC and exports its data in various formats to Hugging Face, without any fundamental transformation but conversions. | ||
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| <p align="center"><a href="https://www.etalab.gouv.fr/licence-ouverte-open-licence/"><img src="https://raw.githubusercontent.com/antoinejeannot/jurisprudence/artefacts/license.png" width=50 alt="license ouverte / open license"></a></p> |