task_categories:text-generation task_categories:text2text-generation task_categories:text-retrieval task_categories:question-answering size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif datacraft
---
size_categories: n<1K
task_categories:
- text-generation
- text2text-generation
- text-retrieval
- question-answering
dataset_info:
  features:
  - name: context
    dtype: string
  - name: question
    dtype: string
  - name: response
    dtype: string
  splits:
  - name: train
    num_bytes: 10904
    num_examples: 10
  download_size: 13754
  dataset_size: 10904
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>

# Dataset Card for my-distiset-3d6680f8

This dataset has been created with [distilabel](https://distilabel.argilla.io/).



## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/Bruno2023/my-distiset-3d6680f8/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/Bruno2023/my-distiset-3d6680f8/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "context": "MARC-AUR\u00c8LE LIVRE \u00cf E \u2014 De: mon grand-p\u00e8re V\u00e9rus? : la bont\u00e9 coutu- mi\u00e8re, le calme inalt\u00e9rable, II. \u2014\u2014 De la r\u00e9putation et du souvenir que laissa mon p\u00e8re : la r\u00e9serve et la force virile. IL. \u2014 De ma m\u00e8re # : la pi\u00e9t\u00e9, la lib\u00e9ralit\u00e9, l\u2019habi- tude de s\u0027abstenir non seulement de mal faire, mais de s\u0027arr\u00eater encore sur une pens\u00e9e mauvaise. De plus : la simplicit\u00e9 du r\u00e9gime de vie, et l\u2019aversion pour le train d\u0027existence que m\u00e8nent les riches. IV. \u2014 De mon bisaleulf : n\u2019avoir point fr\u00e9quent\u00e9 les",
    "question": "Quels sont les traits de caract\u00e8re de votre grand-p\u00e8re V\u00e9rus, de votre p\u00e8re, de votre m\u00e8re et de votre oncle qui vous ont influenc\u00e9?",
    "response": "D\u0027apr\u00e8s le document, les traits de caract\u00e8re des personnages mentionn\u00e9s sont les suivants :\n\n- De votre grand-p\u00e8re V\u00e9rus : \n  - La bont\u00e9\n  - La bont\u00e9 extr\u00eame (coutumi\u00e8re)\n  - Le calme inalt\u00e9rable\n\n- De votre p\u00e8re :\n  - La r\u00e9serve\n  - La force virile\n\n- De votre m\u00e8re :\n  - La pi\u00e9t\u00e9\n  - La lib\u00e9ralit\u00e9\n  - La simplicit\u00e9 du r\u00e9gime de vie\n  - L\u0027aversion pour le train d\u0027existence des riches\n  - L\u0027habitude de s\u0027abstenir non seulement de mal faire, mais de s\u0027arr\u00eater sur une pens\u00e9e mauvaise\n\n- De votre oncle (bisaleulf) :\n  - Ne fr\u00e9quentant pas les... (le document ne mentionne pas les qualit\u00e9s ou les d\u00e9fauts de votre oncle, mais plut\u00f4t ce qu\u0027il n\u0027a pas fait)"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("Bruno2023/my-distiset-3d6680f8", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`: 

```python
from datasets import load_dataset

ds = load_dataset("Bruno2023/my-distiset-3d6680f8")
```


</details>




