README.md

---
dataset_info:
  features:
  - name: tokens
    sequence: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': B-LOC
          '1': B-MISC
          '2': B-ORG
          '3': B-PER
          '4': I-LOC
          '5': I-MISC
          '6': I-ORG
          '7': I-PER
          '8': O
  - name: novel
    dtype: string
  splits:
  - name: train
    num_bytes: 21690413
    num_examples: 26785
  download_size: 3685914
  dataset_size: 21690413
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# 7-romans

This dataset contains 7 French novels, entirely annoted for the NER task. See the related [alias resolution dataset](https://huggingface.co/datasets/compnet-renard/7-romans-alias-resolution).

| **Novel**               | **Author**        | **Publication Year**     | **Number of tokens** | **Number of characters**  |
|-------------------------|-------------------|--------------------------|----------------------|---------------------------|
| Les Trois Mousquetaires | Alexandre Dumas   | 1849                     | 294 989              | 213                       |
| Le Rouge et le Noir     | Stendhal          | 1854                     | 216 445              | 318                       |
| Eugénie Grandet         | Honoré de Balzac  | 1855                     | 80 659               | 107                       |
| Germinal                | Émile Zola        | 1885                     | 220 273              | 102                       |
| Bel-Ami                 | Guy de Maupassant | 1901                     | 138 156              | 150                       |
| Notre-Dame de Paris     | Victor Hugo       | 1904                     | 221 351              | 536                       |
| Madame Bovary           | Gustave Flaubert  | 1910                     | 148 861              | 175                       |


This gold standard corpus was created in the context of a project at the ObTIC laboratory, Sorbonne University. The project was directed by Motasem Alrahabi, and annnotations were performed by Perrine Maurel, Una Faller and Romaric Parnasse.

The corpus was then used to train a [CamemBERT NER model](https://huggingface.co/compnet-renard/camembert-base-literary-NER-v2) in collaboration with Arthur Amalvy and Vincent Labatut, from Avignon University.


# Usage

```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("compnet-renard/7-romans-ner")
>>> dataset["train"][0]
{'tokens': ['Quand', 'la', 'caissière', 'lui', 'eut', 'rendu', 'la', 'monnaie', 'de', 'sa', 'pièce', 'de', 'cent', 'sous', ',', 'Georges', 'Duroy', 'sortit', 'du', 'restaurant', '.'], 'labels': [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 3, 7, 8, 8, 8, 8], 'novel': 'BelAmi'}
```


# Citation

If you use this dataset in your research, please cite:

```bibtex
@InProceedings{Maurel2025,
  authors = {Maurel, P. and Amalvy, A. and Labatut, V. and Alrahabi, M.},
  title = {Du repérage à l’analyse : un modèle pour la reconnaissance d’entités nommées dans les textes littéraires en français},
  booktitle = {Digital Humanities 2025},
  year = {2025},
}
```