---
license: apache-2.0
dataset_info:
  features:
  - name: xml_sha1
    dtype: string
  - name: declaration_xml
    dtype: string
  - name: declaration_json
    dtype: string
  - name: text_index
    dtype: string
  - name: text_index_embedding
    sequence: float64
  splits:
  - name: train
    num_bytes: 438508943
    num_examples: 10944
  download_size: 204548460
  dataset_size: 438508943
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# HATVP Declaration Dataset

This dataset contains declarations from the HATVP (Haute Autorité pour la Transparence de la Vie Publique), converted into a structured format suitable for use in retrieval-augmented generation (RAG) tasks.

## Dataset Information

- **License**: `apache-2.0`
- **Configurations**: This dataset has one configuration (`default`).
- **Download size**: ~205 MB
- **Dataset size**: ~439 MB

## Features

- `xml_sha1`: SHA1 hash of the `declaration_xml`, useful for version comparison. Computed on 24-04-2024.
- `declaration_xml`: Original XML declaration as provided by the HATVP.
- `declaration_json`: JSON format of the XML, converted using the XMLTODICT Python library. This format reduces the character count by 25%.
- `text_index`: Name, surname, and current job title of the declaring person.
- `text_index_embedding`: Embedding vector from the OpenAI `text-embedding-3-large` model, optimized for French language text.

## Data Splits

- **Train**: 10,944 examples, occupying about 438.51 MB.

## Configuration: Default

- **Data Files**:
  - `train-*`: Contains the training split data files.

## Usage

This dataset facilitates RAG by using a language model to answer questions about individuals based on their HATVP declarations. It is particularly useful for generating factual answers about public figures.

### Example Usage

```python
# Example of how to use this dataset with a RAG model
question = "Où travaille le conjoint de Damien Abad?"
# Assume `rag_model` is a pre-trained model loaded and ready to answer
answer = rag_model.answer(question)
print(answer)  # Outputs: "Le conjoint de Damien Abad travaille au Centre Hospitalier du Haut-Bugey."
```

## Example Interactions

1. **Question**: "Où travaille le conjoint de Damien Abad?"
   **Answer**: "Le conjoint de Damien Abad travaille au Centre Hospitalier du Haut-Bugey."

2. **Question**: "Combien est payé Damien Abad en 2019?"
   **Answer**: "According to the provided context, Damien Abad was paid 71,105 euros in 2019."

3. **Question**: "Le conjoint de Damien Abad est-il un homme ou une femme?"
   **Answer**: "Le conjoint de Damien Abad est une femme, car il est mentionné que son conjoint est une infirmière."

(Answers vary between French and English depending on the language model used for answering.)