File size: 2,079 Bytes
75c402b 4115ad5 75c402b c24d9fa 10bc098 19661cc c24d9fa 4115ad5 19661cc 75c402b 77f7e27 75c402b 9915006 77f7e27 75c402b 7503cb7 75c402b 19661cc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
---
dataset_info:
splits:
- name: test
task_categories:
- question-answering
language:
- fr
pretty_name: Syntec dataset for information retrieval
configs:
- config_name: documents
data_files:
- split: test
path: documents.json
- config_name: queries
data_files:
- split: test
path: queries.json
---
# Syntec dataset for information retrieval
This dataset has been built from the Syntec Collective bargaining agreement. Its purpose is information retrieval.
## Dataset Details
The dataset is rather small. It is intended to be used only as a test set, for fast evaluation of models.
It is split into 2 subsets :
- **queries** : it features 100 manually created questions. Each question is mapped to the article that contains the answer.
- **documents** : corresponds to the 90 articles from the collective bargaining
### Usage
```py
import datasets
# Download the documents (corpus)
corpus_raw = datasets.load_dataset("lyon-nlp/mteb-fr-retrieval-syntec-s2p", "documents")
# Download the queries
queries_raw = datasets.load_dataset("lyon-nlp/mteb-fr-retrieval-syntec-s2p", "queries")
```
### Dataset Description
The collective bargaining agreement is applicable to employees of Technical Design Offices, Consulting Engineering Firms and Consulting Companies.
The dataset contains 100 questions, each having their answer in 1 of the 90 articles of the documents. The dataset was manually annotated. It's small size allows for quick prototyping.
- **Curated by:** Wikit AI (https://www.wikit.ai/)
- **Language(s) (NLP):** French
- **License:** [More Information Needed]
### Dataset Sources
https://www.syntec.fr/
### 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}
}
```
### Contact
mathieu@wikit.ai
marion@wikit.ai
|