Datasets:
language:
- fr
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- text-classification
- question-answering
pretty_name: alloprof
configs:
- config_name: documents
data_files:
- split: test
path: documents/test-*
- config_name: queries
data_files:
- split: test
path: queries-test.json
- split: train
path: queries-train.json
dataset_info:
config_name: documents
features:
- name: topic
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: uuid
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: test
num_bytes: 9531951
num_examples: 2556
download_size: 5040817
dataset_size: 9531951
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 :
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}
}