task_categories:text-classification size_categories:1K<n<10K language:fr license:apache-2.0 medical biology region:us
This article presents MORFITT, the first multi-label corpus in French annotated in
specialties in the medical field. MORFITT is composed of 3~624 abstracts of scientific
articles from PubMed, annotated in 12 specialties for a total of 5,116 annotations. 
We detail the corpus, the experiments and the preliminary results obtained using a 
classifier based on the pre-trained language model CamemBERT. These preliminary results
demonstrate the difficulty of the task, with a weighted average F1-score of 61.78%.
ddd
---
license: apache-2.0
task_categories:
- text-classification
language:
- fr
tags:
- medical
- biology
pretty_name: MORFITT
size_categories:
- 1K<n<10K
---

# MORFITT

## Data ([Zenodo](https://zenodo.org/record/7893841#.ZFLFDnZBybg)) | Publication ([HAL](https://hal.science/hal-04131591/)) 
[Yanis LABRAK](https://www.linkedin.com/in/yanis-labrak-8a7412145/), [Richard DUFOUR](https://cv.hal.science/richard-dufour), [Mickaël ROUVIER](https://cv.hal.science/mickael-rouvier)

[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/115EixHBcjf-se6xQeaTwZWE1i4idTNbm?usp=sharing) or [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/qanastek/MORFITT/blob/main/TrainTransformers.py)

We introduce MORFITT, the first multi-label corpus for the classification of specialties in the medical field, in French. MORFITT is composed of 3,624 summaries of scientific articles from PubMed, annotated in 12 specialties. The article details the corpus, the experiments and the preliminary results obtained using a classifier based on the pre-trained language model CamemBERT.

For more details, please refer to our paper:

**MORFITT: A multi-label topic classification for French Biomedical literature** ([HAL](https://hal.science/hal-04131591/))


# Key Features

## Documents distribution

| Train |  Dev  | Test  |
|-------|-------|-------|
| 1,514 | 1,022 | 1,088 |

## Multi-label distribution

|               | Train |  Dev  |  Test | Total |
|:----------------------:|:--------------:|:--------------:|:--------------:|:--------------:|
|  Vétérinaire  |       320      |       250      |       254      |  824  |
|   Étiologie   |       317      |       202      |       222      |  741  |
|  Psychologie  |       255      |       175      |       179      |  609  |
|   Chirurgie   |       223      |       169      |       157      |  549  |
|   Génétique   |       207      |       139      |       159      |  505  |
|  Physiologie  |       217      |       125      |       148      |  490  |
| Pharmacologie |       112      |       84       |       103      |  299  |
| Microbiologie |       115      |       72       |       86       |  273  |
|  Immunologie  |       106      |       86       |       70       |  262  |
|     Chimie    |       94       |       53       |       65       |  212  |
|   Virologie   |       76       |       57       |       67       |  200  |
| Parasitologie |       68       |       34       |       50       |  152  |
|     Total     | 2,110 | 1,446 | 1,560 | 5,116 |


## Number of labels per document distribution

<p align="left">
  <img src="https://github.com/qanastek/MORFITT/raw/main/images/distributions_nbr_elements_colors.png" alt="drawing" width="400"/>
</p>

## Co-occurences distribution

<p align="left">
  <img src="https://github.com/qanastek/MORFITT/raw/main/images/distributions_co-references-fixed.png" alt="drawing" width="400"/>
</p>

# If you use HuggingFace Transformers

```python
from datasets import load_dataset
dataset = load_dataset("qanastek/MORFITT")
print(dataset)
```

or

```python
from datasets import load_dataset
dataset_base = load_dataset(
    'csv',
    data_files={
        'train': f"./train.tsv",
        'validation': f"./dev.tsv",
        'test': f"./test.tsv",
    },
    delimiter="\t",
)
```

# License and Citation

The code is under [Apache-2.0 License](./LICENSE).

The MORFITT dataset is licensed under *Attribution-ShareAlike 4.0 International* ([CC BY-SA 4.0](https://creativecommons.org/licenses/by/4.0/)).
If you find this project useful in your research, please cite the following papers:

```plain
Labrak, Y., Rouvier, M., & Dufour, R. (2023). MORFITT : Un corpus multi-labels d’articles scientifiques français dans le domaine biomédical. In F. Boudin, B. Daille, R. Dufour, O. Khettari, M. Houbre, L. Jourdan, & N. Kooli (Eds.), 18e Conférence en Recherche d’Information et Applications – 16e Rencontres Jeunes Chercheurs en RI – 30e Conférence sur le Traitement Automatique des Langues Naturelles – 25e Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (pp. 66–70). ATALA. https://hal.science/hal-04131591
```

or using the bibtex:

```bibtex
@inproceedings{labrak:hal-04131591,
  TITLE = {{MORFITT : Un corpus multi-labels d'articles scientifiques fran{\c c}ais dans le domaine biom{\'e}dical}},
  AUTHOR = {Labrak, Yanis and Rouvier, Mickael and Dufour, Richard},
  URL = {https://hal.science/hal-04131591},
  BOOKTITLE = {{18e Conf{\'e}rence en Recherche d'Information et Applications -- 16e Rencontres Jeunes Chercheurs en RI -- 30e Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles -- 25e Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues}},
  ADDRESS = {Paris, France},
  EDITOR = {Boudin, Florian and Daille, B{\'e}atrice and Dufour, Richard and Khettari, Oumaima and Houbre, Ma{\"e}l and Jourdan, L{\'e}ane and Kooli, Nihel},
  PUBLISHER = {{ATALA}},
  PAGES = {66-70},
  YEAR = {2023},
  KEYWORDS = {Analyse de documents scientifiques ; Jeux de donn{\'e}es compos{\'e}s des textes scientifiques},
  PDF = {https://hal.science/hal-04131591/file/1465546.pdf},
  HAL_ID = {hal-04131591},
  HAL_VERSION = {v1},
}
```