README.md

---
language:
- fr
tags:
- france
- travail
- emploi
- embeddings
- open-data
- government
pretty_name: French Minister of Labor and Employment's website Dataset (Travail Emploi)
size_categories:
- 1K<n<10K
license: etalab-2.0
configs:
- config_name: latest
  data_files: "data/travail-emploi-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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# 🇫🇷 Travail Emploi website Dataset (French Minister of Labor and Employment)

This dataset is a processed and embedded version of public practical information sheets extracted from the official website of **Ministère du Travail et de l’Emploi** (Minister of Labor and Employment): [travail-emploi.gouv.fr](https://travail-emploi.gouv.fr/).
These datas are downloaded from the government [Social Gouv GitHub repository](https://github.com/SocialGouv/fiches-travail-data).

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.

---

## 🗂️ 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.                                       |
| `title`        | `str`            | Title of the article.                                                       |
| `surtitre`     | `str`            | Broader theme. (always "Travail-Emploi" in this dataset).                   |
| `source`       | `str`            | Dataset source label. (always "travail-emploi" in this dataset)             |
| `introduction` | `str`            | Introductory paragraph of the article.                                      |
| `date`         | `str`            | Publication or last update date (format: DD/MM/YYYY).                       |
| `url`          | `str`            | URL of the original article.                                                |
| `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 for embedding   |
| `embeddings_bge-m3`   | `str`            | Embedding vector of `chunk_text` using `BAAI/bge-m3`, stored as JSON array string.  |

---
## 🛠️ Data Processing Methodology

### 📥 1. Field Extraction

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

- **Basic fields**: `sid` (i.e. 'pubID'), `title`, `introduction` (i.e. 'intro'), `date`, `url` are directly extracted from JSON attributes.
- **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 "travail-emploi" here.
  - `surtitre`: is always "Travail-Emploi" 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`. 
    
Columns `source` and `surtitre` are fixed variables here because this dataset was built at the same time as the [Service Public dataset](https://huggingface.co/datasets/AgentPublic/service-public).
Both datasets were intended to be grouped together in a single vector collection, they then have differents `source` and `surtitre` 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` = 1500 (in order to maximize the compability of most LLMs context windows)
- `chunk_overlap` = 20
- `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 by 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/travail-emploi")
df = pd.DataFrame(dataset['train'])
df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads)
```

Otherwise, if you have already downloaded all parquet files from the `data/travail-emploi-latest/` folder : 
```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="travail-emploi-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 : 
- [Travail Emploi official website](https://travail-emploi.gouv.fr/)
- [Government official Social Gouv GitHub repository](https://github.com/SocialGouv/fiches-travail-data)

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