README.md

metadata

license: cc-by-nc-sa-4.0
language:
  - fr
multilinguality:
  - monolingual
tags:
  - administrative documents
  - named entity recognition
  - BIO
  - information extraction
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
dataset_info:
  features:
    - name: tokens
      sequence: string
    - name: ner_tags
      sequence: string
  splits:
    - name: train
      num_bytes: 672494
      num_examples: 729
    - name: validation
      num_bytes: 96406
      num_examples: 85
  download_size: 197180
  dataset_size: 768900

Adminset-NER the first dataset of French Administrative documents for Named Entity Recognition

Adminset-NER is a dataset dedicated for NER extract from French adminstrative documents produce by municipalities, communes, metropolises, départements, regions, prefectures and ministries. It have been amnually annotated by 5 non expert annotators using Label Studio.

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."
}