Create README.md

README.md

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+---
+language:
+- fr
+tags:
+- france
+- public-sector
+- embeddings
+- directory
+- open-data
+- government
+- etalab
+pretty_name: French Local Administrations Directory
+size_categories:
+- 10K<n<100K
+license: etalab-2.0
+---
+
+# 🇫🇷 French Local Administrations Directory Dataset
+
+This dataset is a processed and embedded version of the public data **Annuaire de l’administration - Base de données locales** (French Local Administrations Directory), published by **DILA** (Direction de l'information légale et administrative) on [data.gouv.fr](https://www.data.gouv.fr/datasets/service-public-fr-annuaire-de-l-administration-base-de-donnees-locales/).  
+This information is also available on the official directory website of Service-Public.fr: https://lannuaire.service-public.fr/
+
+The dataset provides semantic-ready, structured and chunked data of French **local** public entities, including organizational details, missions, contact information, and hierarchical links. Each chunk of text is vectorized using the [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) embedding model to enable semantic search and retrieval tasks.
+
+---
+
+## 🗂️ Dataset Contents
+
+The dataset is provided in **Parquet format** and contains the following columns:
+
+| Column Name             | Type                        | Description                                                                 |
+|------------------------|-----------------------------|-----------------------------------------------------------------------------|
+| `chunk_id`             | `str`                       | Unique source based identifier of the chunk                                 |
+| `types`                | `str`                       | Type(s) of administrative entity.                                           |
+| `name`                 | `str`                       | Name of the organization or service.                                        |
+| `mission_description`  | `str`                       | Description of the entity's mission.                                        |
+| `addresses`            | `list[dict]`                | List of address objects (street, postal code, city, etc.).                  |
+| `phone_numbers`        | `list[str]`                 | List of telephone numbers.                                                  |
+| `mails`                | `list[str]`                 | List of contact email addresses.                                            |
+| `urls`                 | `list[str]`                 | List of related URLs.                                                       |
+| `social_medias`        | `list[str]`                 | Social media accounts.                                                      |
+| `mobile_applications`  | `list[str]`                 | Related mobile applications.                                                |
+| `opening_hours`        | `str`                       | Opening hours.                                                              |
+| `contact_forms`        | `list[str]`                 | Contact form URLs.                                                          |
+| `additional_information` | `str`                     | Additional information.                                                     |
+| `modification_date`    | `str`                       | Last update date.                                                           |
+| `siret`                | `str`                       | SIRET number.                                                               |
+| `siren`                | `str`                       | SIREN number.                                                               |
+| `people_in_charge`     | `list[dict]`                | List of responsible persons.                                                |
+| `organizational_chart` | `list[str]`                 | Organization chart references.                                              |
+| `hierarchy`            | `list[dict]`                | Links to parent or child entities.                                          |
+| `directory_url`        | `str`                       | Source URL from the official state directory website.                       |
+| `chunk_text`           | `str`                       | Textual content of the administrative chunk.                                |
+| `embeddings_bge-m3`    | `str` (stringified list)    | Embeddings of `chunk_text` using `BAAI/bge-m3`. Stored as a JSON array string. |
+
+---
+
+## 🛠️ Data Processing Methodology
+
+### 📥 1. Field Extraction
+
+The following fields were extracted and/or transformed from the original JSON:
+
+- **Basic fields**: `chunk_id`, `name`, `types`, `mission_description`, `additional_information`, `siret`, `siren`, `directory_url`, `modification_date` are directly extracted from JSON attributes.
+- **Structured lists**:
+  - `addresses`: list of dictionaries with `adresse`, `code_postal`, `commune`, `pays`, `longitude`, and `latitude`.
+  - `phone_numbers`, `mails`, `urls`, `social_medias`, `mobile_applications`, `contact_forms`: derived from their respective fields with formatting.
+- **People and structure**:
+  - `people_in_charge`: list of dictionaries representing staff members or leadership (title, name, rank, etc.).
+  - `organizational_chart`, `hierarchy`: structural information within the administration.
+- **Other fields**:
+  - `opening_hours`: built using a custom function that parses declared time slots into readable strings.
+
+### ✂️ 2. Generation of `chunk_text`
+
+A synthetic text field called `chunk_text` was created to summarize key aspects of each administrative body. This field is designed for semantic search and embedding generation. It includes:
+
+- The entity’s name : `name`
+- Its mission statement (if available) : `mission_description`
+- Key responsible individuals (formatted using role, title, name, and rank) : `people_in_charge`
+
+There was no need here to split characters here.
+
+### 🧠 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.
+
+## 📌 Embeddings Notice
+
+⚠️ The `embeddings_bge-m3` column is stored as a stringified list (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:
+
+```python
+import pandas as pd
+import json
+
+df = pd.read_parquet("local-administrations-directory-latest.parquet")
+df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads)
+```
+
+## 📚 Source & License
+
+## 🔗 Source : 
+- [Lannuaire.Service-Public.fr](https://lannuaire.service-public.fr/)
+- [Data.Gouv.fr : Service-public.fr - Annuaire de l’administration - Base de données locales](https://www.data.gouv.fr/datasets/service-public-fr-annuaire-de-l-administration-base-de-donnees-locales/)
+
+## 📄 Licence : 
+**Open License (Etalab)** — This dataset is publicly available and can be reused under the conditions of the Etalab open license.