README.md

---
language:
- fr
tags:
- france
- service-public
- demarches
- embeddings
- administration
- open-data
- government
pretty_name: Service-Public.fr practical sheets dataset
size_categories:
- 10K<n<100K
license: etalab-2.0
configs:
- config_name: latest
  data_files: "data/service-public-latest/*.parquet"
  default: true
---
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### 📢 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 ! 🙌

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# 🇫🇷 Service-Public.fr practical sheets dataset (Administrative Procedures)

This dataset is derived from the official [Service-Public.fr](https://www.service-public.fr/) platform and contains practical information sheets and resources targeting both individuals (Particuliers) and entrepreneurs (Entreprendre). 
The purpose of these sheets is to provide information on administrative procedures relating to a number of themes.
The data is publicly available on [data.gouv.fr](https://www.data.gouv.fr) and has been processed and chunked for optimized semantic retrieval and large-scale embedding use.

The dataset provides semantic-ready, structured and chunked data of official content related to employment, labor law and administrative procedures. 
These chunks have been vectorized using the [`BAAI/bge-m3`](https://huggingface.co/BAAI/bge-m3) embedding model to enable semantic search and retrieval tasks.



Each record represents a semantically coherent text fragment (chunk) from an original sheet, enriched with metadata and a precomputed embedding vector suitable for search and retrieval applications (e.g., RAG pipelines).

---

## 🗂️ Dataset Contents

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

| Column Name        | Type             | Description                                                                 |
|--------------------|------------------|-----------------------------------------------------------------------------|
| `chunk_id`         | `str`            | Unique generated and encoded hash of each chunk.                            |
| `doc_id`           | `str`            | Document identifier from the source site.                                   |
| `chunk_index`      | `int`            | Index of the chunk within its original document. Starting from 1.           |
| `chunk_xxh64`      | `str`            | XXH64 hash of the `chunk_text` value.                                       |
| `audience`         | `str`            | Target audience: `Particuliers` and/or `Professionnels`.                    |
| `theme`            | `str`            | Thematic categories (e.g., `Famille - Scolarité, Travail - Formation`).     |
| `title`            | `str`            | Title of the article.                                                       |
| `surtitre`         | `str`            | Higher-lever theme of article structure.                                    |
| `source`           | `str`            | Dataset source label. (always "service-public" in this dataset).            |
| `introduction`     | `str`            | Introductory paragraph of the article.                                      |
| `url`              | `str`            | URL of the original article.                                                |
| `related_questions`| `list[dict]`     | List of related questions, including their sid and URLs.                    |
| `web_services`     | `list[dict]`     | Associated web services (if any).                                           |
| `context`          | `list[str]`      | Section names related to the chunk.                                         |
| `text`             | `str`            | Textual content extracted and chunked from a section of the article.        |
| `chunk_text`       | `str`            | Formated text including `title`, `context`, `introduction` and `text` values. Used for embedding.  |
| `embeddings_bge-m3`| `str`            | Embedding vector of `chunk_text` using `BAAI/bge-m3` (length of 1024), stored as JSON array string |

---
## 🛠️ Data Processing Methodology

### 1. 📥 Field Extraction

The following fields were extracted and/or transformed from the original XML files:

- **Basic fields**: `doc_id`, `theme`, `title`,`surtitre`, `introduction`, `url`, `related_questions`, `web_services`  are directly extracted from the XML files, with some processing when needed.
- **Generated fields**:
  - `chunk_id`: is an unique generated and encoded hash for each chunk.
  - `chunk_index`: is the index of the chunk of a same document. Each document has an unique `doc_id`.
  - `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.
  - `source`: is always "service-public" here.
- **Textual fields**:
  - `context`: Optional contextual hierarchy (e.g., nested sections).
  - `text`: Textual content of the article chunk.
This is the value which corresponds to a semantically coherent fragment of textual content extracted from the XML document structure for a same `sid`. 

    
Column `source` is a fixed variable here because this dataset was built at the same time as the [Travail Emploi Dataset](https://huggingface.co/datasets/AgentPublic/travail-emploi).
Both datasets were intended to be grouped together in a single vector collection, they then have differents `source` values.

### 2. ✂️ Generation of 'chunk_text'

The value includes the `title` and `introduction` of the article, the `context` values of the chunk 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` = 1024
- `chunk_overlap` = 0
- `length_function` = bge_m3_tokenizer

### 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. The chunking doesn't fit your use case?

If you need to reconstitute the original, un-chunked dataset, you can follow [this tutorial notebook available on our GitHub repository](https://github.com/etalab-ia/mediatech/blob/main/docs/reconstruct_vector_database.ipynb).

⚠️ The tutorial is only relevant for datasets that were chunked **without overlap**.

### 🤖 2. 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)

### 📌 3. 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/service-public")
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="service-public-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 : 
- [Service-Public.fr official website](https://www.service-public.fr/)
- [Data.Gouv.fr : Fiches pratiques et ressources de Service-Public.fr Particuliers ](https://www.data.gouv.fr/fr/datasets/fiches-pratiques-et-ressources-de-service-public-fr-particuliers/)
- [Data.Gouv.fr : Fiches pratiques et ressources Entreprendre - Service-Public.fr ](https://www.data.gouv.fr/fr/datasets/fiches-pratiques-et-ressources-entreprendre-service-public-fr/)

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