| | --- |
| | language: |
| | - fr |
| | tags: |
| | - france |
| | - public-sector |
| | - embeddings |
| | - directory |
| | - open-data |
| | - government |
| | - etalab |
| | pretty_name: French State Administrations Directory |
| | size_categories: |
| | - 1K<n<10K |
| | license: etalab-2.0 |
| | configs: |
| | - config_name: latest |
| | data_files: "data/state-administrations-directory-latest/*.parquet" |
| | default: true |
| | --- |
| | --------------------------------------------------------------------------------------------------- |
| | ### 📢 Sondage 2026 : Utilisation des datasets publiques de MediaTech |
| | Vous utilisez ce dataset ou d’autres datasets de notre collection [MediaTech](https://huggingface.co/collections/AgentPublic/mediatech) ? Votre avis compte ! |
| | Aidez-nous à améliorer nos datasets publiques en répondant à ce sondage rapide (5 min) : 👉 https://grist.numerique.gouv.fr/o/albert/forms/gF4hLaq9VvUog6c5aVDuMw/11 |
| | Merci pour votre contribution ! 🙌 |
| |
|
| | --------------------------------------------------------------------------------------------------- |
| | # 🇫🇷 French State Administrations Directory Dataset |
| |
|
| | This dataset is a processed and embedded version of the public data **Référentiel de l’organisation administrative de l’État** (French State Administrations Directory), published by **DILA** (Direction de l'information légale et administrative) on [data.gouv.fr](https://www.data.gouv.fr/fr/datasets/referentiel-de-lorganisation-administrative-de-letat/). |
| | 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 state 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. | |
| | | `doc_id` | `str` | Document identifier. Identical to `chunk_id` as each document only has 1 chunk. | |
| | | `chunk_xxh64` | `str` | XXH64 hash of the `chunk_text` value. | |
| | | `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`, `doc_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. |
| | - `chunk_xxh64`: is the xxh64 hash of the `chunk_text` value. It is useful to determine if the `chunk_text` value has changed from a version to another. |
| |
|
| | ### ✂️ 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. |
| |
|
| | ## 🎓 Tutorials |
| |
|
| | ### 🤖 1. How to load MediaTech's datasets from Hugging Face and use them in a RAG pipeline ? |
| |
|
| | To learn how to load MediaTech's datasets from Hugging Face and integrate them into a Retrieval-Augmented Generation (RAG) pipeline, check out our [step-by-step RAG tutorial available on our GitHub repository !](https://github.com/etalab-ia/mediatech/blob/main/docs/hugging_face_rag_tutorial.ipynb) |
| |
|
| | ### 📌 2. 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. |
| |
|
| | #### Using the `datasets` library: |
| |
|
| | ```python |
| | import pandas as pd |
| | import json |
| | from datasets import load_dataset |
| | # The Pyarrow library must be installed in your Python environment for this example. By doing => pip install pyarrow |
| | |
| | dataset = load_dataset("AgentPublic/state-administrations-directory") |
| | df = pd.DataFrame(dataset['train']) |
| | df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads) |
| | ``` |
| | #### Using downloaded local Parquet files: |
| |
|
| | ```python |
| | import pandas as pd |
| | import json |
| | # The Pyarrow library must be installed in your Python environment for this example. By doing => pip install pyarrow |
| | |
| | df = pd.read_parquet(path="state-administrations-directory-latest/") # Assuming that all parquet files are located into this folder |
| | df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads) |
| | ``` |
| |
|
| | You can then use the dataframe as you wish, such as by inserting the data from the dataframe into the vector database of your choice. |
| |
|
| | ## 🐱 GitHub repository : |
| | The project MediaTech is open source ! You are free to contribute or see the complete code used to build the dataset by checking the [GitHub repository](https://github.com/etalab-ia/mediatech) |
| |
|
| | ## 📚 Source & License |
| |
|
| | ### 🔗 Source : |
| | - [Lannuaire.Service-Public.fr](https://lannuaire.service-public.fr/) |
| | - [Data.Gouv.fr : Référentiel de l’organisation administrative de l’État](https://www.data.gouv.fr/fr/datasets/referentiel-de-lorganisation-administrative-de-letat/) |
| |
|
| | ### 📄 Licence : |
| | **Open License (Etalab)** — This dataset is publicly available and can be reused under the conditions of the Etalab open license. |
