Create README.md

README.md

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+---
+language:
+- fr
+tags:
+- france
+- cnil
+- loi
+- deliberations
+- decisions
+- embeddings
+- open-data
+- government
+pretty_name: CNIL Deliberations Dataset
+size_categories:
+- 10K<n<100K
+license: etalab-2.0
+---
+
+# 🇫🇷 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](https://echanges.dila.gouv.fr/OPENDATA/CNIL/) and the dataset is also [available in data.gouv.fr (Les délibérations de la CNIL)](https://www.data.gouv.fr/datasets/les-deliberations-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`](https://huggingface.co/BAAI/bge-m3) model.
+
+---
+
+## 🗂️ 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.|
+
+---
+
+## 🛠️ Data Processing Methodology
+
+### 1. 📥 Field Extraction
+
+Data was extracted from the [the dedicated **DILA** open data repository](https://echanges.dila.gouv.fr/OPENDATA/CNIL/).  
+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`**](https://huggingface.co/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 :
+
+```python
+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](https://echanges.dila.gouv.fr/OPENDATA/CNIL)
+- [Data.gouv.fr : Les délibérations de la CNIL](https://www.data.gouv.fr/datasets/les-deliberations-de-la-cnil/)
+
+## 📄 Licence : 
+**Open License (Etalab)** — This dataset is publicly available and can be reused under the conditions of the Etalab open license.