---
task_categories:
- text-classification
- question-answering
language:
- fr
pretty_name: alloprof
size_categories:
- 1K<n<10K
configs:
- config_name: documents
  data_files:
  - split: test
    path: documents.json
- config_name: queries
  data_files:
  - split: test
    path: queries-test.json
  - split: train
    path: queries-train.json
license: apache-2.0
---

This is a re-edit from the Alloprof dataset (which can be found here : https://huggingface.co/datasets/antoinelb7/alloprof).

For more information about the data source and the features, please refer to the original dataset card made by the authors, along with their paper available here : https://arxiv.org/abs/2302.07738

This re-edition of the dataset is a preprocessed version of the original, **in a more ready-to-use format**. Essentially, the texts have been cleaned, and data not usable for retrieval has been discarded.

### Why a re-edition ?

It has been made for easier usage in the MTEB benchmarking pipeline in order to contribute in the MTEB leaderboard : https://huggingface.co/spaces/mteb/leaderboard.

For more information about the project, please refer to the associated paper : https://arxiv.org/pdf/2210.07316.pdf

### Usage

To use the dataset, you need to specify the subset you want (documents or queries) when calling the load_dataset() method.
For example, to get the queries use :
```py
from datasets import load_dataset
dataset = load_dataset("lyon-nlp/alloprof", "queries")
```

### Citation
If you use this dataset in your work, please consider citing:
```
@misc{ciancone2024extending,
      title={Extending the Massive Text Embedding Benchmark to French}, 
      author={Mathieu Ciancone and Imene Kerboua and Marion Schaeffer and Wissam Siblini},
      year={2024},
      eprint={2405.20468},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{lefebvrebrossard2023alloprof,
      title={Alloprof: a new French question-answer education dataset and its use in an information retrieval case study}, 
      author={Antoine Lefebvre-Brossard and Stephane Gazaille and Michel C. Desmarais},
      year={2023},
      eprint={2302.07738},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```