region:us
---
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: input
    dtype: string
  - name: output
    dtype: string
  splits:
  - name: train
    num_bytes: 22875696
    num_examples: 10599
  - name: validation
    num_bytes: 765012
    num_examples: 361
  - name: test
    num_bytes: 250054
    num_examples: 168
  download_size: 5811471
  dataset_size: 23890762
---

# qacr-instruction-format Dataset

This dataset is based on the same format as the Stanford Alpaca dataset, but with a focus on the French language.
I took the data we collected with the annotation campaign and organise it on three task types which are reformulations of the following three tasks:

- "Etant donné le texte suivant, génères une question ayant un intérêt pédagogique" (Given the following text, generate a question that is of pedagogical interest).
- "Utilise le texte suivant pour donner une réponse à la question associée" (Use the following text to provide an answer to the associated question).
- "Génères une question et réponds-y à l'aide du texte suivant" (Generate a question and answer it using the following text).

The reformulations were generated using GPT-4 API, resulting in 200 reformulations for each task. We then added our own annotated dataset of French-language on educational materials, including school books and encyclopedias, resulting in a total of over 10,000 text, question, and answer pairs.

This dataset is more complex than other Q&A datasets in French or even in English. Each task uses the data as input and output, depending on the task.

I splited the dataset on train, val and test set, where the documents are not shared between sets, to not biased the validation or the test set.

## Citation

[Paper citation here]

[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)