🇫🇷 CNIL Deliberations Dataset

This dataset is a processed and embedded version of the official deliberations and decisions published by the CNIL (Commission Nationale de l’Informatique et des Libertés), the French data protection authority.
It includes a variety of legal documents such as opinions, recommendations, simplified norms, general authorizations, and formal decisions.

The original data is downloaded from the dedicated DILA open data repository and the dataset is also available in data.gouv.fr (Les délibérations de la CNIL) . The dataset provides semantic-ready, structured and chunked data making the dataset suitable for semantic search, AI legal assistants, or RAG pipelines for example. These chunks have then been embedded using the BAAI/bge-m3 model.

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🗂️ Dataset Contents

The dataset is provided in Parquet format and includes the following columns:

Column Name	Type	Description
chunk_id	str	Unique identifier for each chunk.
cid	str	Internal identifier of the deliberation.
chunk_number	int	Index of the chunk within the same deliberation document.
nature	str	Type of act (e.g., deliberation, decision...).
status	str	Status of the document (e.g., vigueur, vigueur_diff).
nature_delib	str	Specific nature of the deliberation.
title	str	Title of the deliberation or decision.
full_title	str	Full title of the deliberation or decision.
number	str	Official reference number.
date	str	Date of publication (format: YYYY-MM-DD).
text	str	Raw text content of the chunk extracted from the deliberation or decision
chunk_text	str	Formatted text chunk used for embedding (includes title + content).
embeddings_bge-m3	str	Embedding vector of chunk_text using BAAI/bge-m3, stored as JSON string.

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🛠️ Data Processing Methodology

1. 📥 Field Extraction

Data was extracted from the the dedicated DILA open data repository.
The following transformations were applied:

• Basic fields: cid, title, full_title, number, date, nature, status, nature_delib, were taken directly from the source XML file.
• Generated fields:
  • chunk_id: A unique hash for each text chunk.
  • chunk_number: Indicates the order of a chunk within a same deliberation.
• Textual fields:
  • text: Chunk of the main text content.
  • chunk_text: Combines title and the main text body to maximize embedding relevance.

2. ✂️ Text Chunking

The value includes the title and the textual content chunk text. This strategy is designed to improve semantic search for document search use cases on administrative procedures.

The Langchain's RecursiveCharacterTextSplitter function was used to make these chunks (text value). The parameters used are :

• chunk_size = 1500 (in order to maximize the compability of most LLMs context windows)
• chunk_overlap = 200
• length_function = len

🧠 3. Embeddings Generation

Each chunk_text was embedded using the BAAI/bge-m3 model. The resulting embedding vector is stored in the embeddings_bge-m3 column as a string, but can easily be parsed back into a list[float] or NumPy array.

📌 Embedding Use Notice

⚠️ The embeddings_bge-m3 column is stored as a stringified list of floats (e.g., "[-0.03062629,-0.017049594,...]"). To use it as a vector, you need to parse it into a list of floats or NumPy array. For example, if you want to load the dataset into a dataframe :

import pandas as pd
import json

df = pd.read_parquet("cnil-latest.parquet")
df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads)

📚 Source & License

🔗 Source :

• DILA open data repository
• Data.gouv.fr : Les délibérations de la CNIL

📄 Licence :

Open License (Etalab) — This dataset is publicly available and can be reused under the conditions of the Etalab open license.