Datasets:
Modalities:
Text
Formats:
parquet
Languages:
French
Size:
10M - 100M
Tags:
administrative documents
whole word masking
text-mining
text generation
information extraction
License:
File size: 3,282 Bytes
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---
license: cc-by-nc-4.0
language:
- fr
multilinguality:
- monolingual
tags:
- administrative documents
- whole word masking
- text-mining
- text generation
- information extraction
dataset_info:
features:
- name: ID
dtype: int64
- name: Sentences
dtype: string
splits:
- name: train
num_bytes: 14666274831.145327
num_examples: 40261727
- name: test
num_bytes: 3666568798.8546734
num_examples: 10065432
download_size: 11212509607
dataset_size: 18332843630.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Adminset the first dataset of French Administrative documents
Adminset is a dataset of more than 50 millions sentences or text fragments, extract from French adminstrative documents produce by municipalities, communes, metropolises, départements, regions, prefectures and ministries.
## Citation
If you use this dataset, please cite the following paper:
```
@inproceedings{sebbag-etal-2025-adminset,
title = "{A}dmin{S}et and {A}dmin{BERT}: a Dataset and a Pre-trained Language Model to Explore the Unstructured Maze of {F}rench Administrative Documents",
author = "Sebbag, Thomas and
Quiniou, Solen and
Stucky, Nicolas and
Morin, Emmanuel",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.27/",
pages = "392--406",
abstract = "In recent years, Pre-trained Language Models(PLMs) have been widely used to analyze various documents, playing a crucial role in Natural Language Processing (NLP). However, administrative texts have rarely been used in information extraction tasks, even though this resource is available as open data in many countries. Most of these texts contain many specific domain terms. Moreover, especially in France, they are unstructured because many administrations produce them without a standardized framework. Due to this fact, current language models do not process these documents correctly. In this paper, we propose AdminBERT, the first French pre-trained language models for the administrative domain. Since interesting information in such texts corresponds to named entities and the relations between them, we compare this PLM with general domain language models, fine-tuned on the Named Entity Recognition (NER) task applied to administrative texts, as well as to a Large Language Model (LLM) and to a language model with an architecture different from the BERT one. We show that taking advantage of a PLM for French administrative data increases the performance in the administrative and general domains, on these texts. We also release AdminBERT as well as AdminSet, the pre-training corpus of administrative texts in French and the subset AdminSet-NER, the first NER dataset consisting exclusively of administrative texts in French."
}
```
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