---
language:
  - fr
license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
base_model: almanach/moderncamembert-base
tags:
  - medical
  - healthcare
  - french
  - finemed
  - text-classification
---

# FineMed Subdomain Classifier (FR)

<center>
  <img src="assets/logo_finemed.png" width="100%" alt="FineMed">
</center>

<p align="center">
<a href="https://huggingface.co/blog/bofenghuang/doctobert-fr-release">🤗 Blog</a> |
<a href="https://arxiv.org/abs/2606.22079">📄 Paper</a> |
<a href="https://github.com/doctolib-lab/doctobert">💻 Code</a> |
<a href="https://huggingface.co/collections/doctolib-lab/finemed-fr">🌐 FineMed</a> |
<a href="https://huggingface.co/collections/doctolib-lab/doctobert-fr">🩺 DoctoBERT</a>
</p>

## 📚 Introduction

This is the **medical-subdomain classifier** used to annotate [FineMed-fr](https://huggingface.co/datasets/doctolib-lab/finemed-fr). Given a French medical document, it predicts one of **15 medical subdomains** (e.g. *Clinical guidelines & pathways*, *Patient education & lifestyle*, *Biomedical & mechanistic science*).

It is a [ModernCamemBERT-base](https://huggingface.co/almanach/moderncamembert-base) classifier distilled from LLM teachers, one of the three lightweight annotators behind FineMed-fr (subdomain, educational quality, medical-term density).

## 🚀 How to Use

The classifier reads the document text with its URL prepended (`url + "\n\n" + text`), up to 8192 tokens.

```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

repo = "doctolib-lab/finemed-subdomain-classifier-fr"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSequenceClassification.from_pretrained(repo).eval()

url = "https://www.example.fr/article"
text = "Le diabète de type 2 est une maladie chronique ..."
inputs = tok(url + "\n\n" + text, return_tensors="pt", truncation=True, max_length=8192)

with torch.inference_mode():
    probs = model(**inputs).logits.softmax(-1)[0]
idx = probs.argmax().item()
print(model.config.id2label[idx], round(probs[idx].item(), 3))
```

## 🏷️ Subdomain Taxonomy

`best_class` is one of these 15 values:

| subdomain | description |
| --------- | ----------- |
| Clinical cases & vignettes | Single-patient narratives: presentation, evaluation, management, outcomes; case-based teaching. |
| Clinical guidelines & pathways | Non-patient-specific recommendations, algorithms, and standards; named guidelines or consensus statements. |
| Patient education & lifestyle | Consumer-facing explanations and how-to advice on prevention, self-care, symptoms, diet, fitness, mental well-being. |
| Wellness, supplements & CAM | Botanicals, vitamins, supplements, complementary or alternative therapies outside mainstream clinical guidance. |
| Public health, policy & programs | Population surveillance, epidemiology, screening, laws and regulation, financing and insurance, community guidance. |
| Commercial & promotional | Marketing or sales content: pricing, booking, calls-to-action, affiliate/SEO, comparative ads, testimonials. |
| Drugs, trials & regulation | Drug development and evaluation: clinical trials, approvals and labels, PK/PD, safety monitoring, pharmacovigilance. |
| Biomedical & mechanistic science | Experimental or preclinical research: labs, omics, pathways, cell/animal models, assays, mechanisms. |
| Medical devices, diagnostics & imaging | Device or modality descriptions and clinical use; diagnostics, wearables, sensors, imaging. |
| Health IT, telemedicine & operations | EHR/EMR, data standards, interoperability, analytics, telemedicine, workflow, staffing, procurement, logistics. |
| Occupational health & safety | Workplace hazards, exposures, PPE, training, and compliance with occupational regulations. |
| Health workforce education & training | Professional curricula, CME, certification, simulation, residency/fellowship information. |
| Health services & facilities | Neutral descriptions of care-delivery models, service lines, facility capabilities, long-term/residential care. |
| Other health | Health-related content that is unclear or insufficient to classify under the other subdomains. |
| Others | Not clearly health-related, too brief, or lacking detail (e.g. navigation/boilerplate). |

## 🔧 Training

The classifier is distilled from LLM teachers under a two-stage schedule, fine-tuning ModernCamemBERT-base at 8192-token input (document content + URL):

- **Stage 1**: [Qwen3-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct) labels 1M documents (high-volume supervision).
- **Stage 2**: [Qwen3-235B-A22B-Instruct](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct) labels 490k documents (high-quality supervision).

The 15-class taxonomy was built through three rounds of LLM-driven iteration; class order is shuffled during annotation to mitigate position bias. The full annotation prompt is in [`subdomain_annotation_prompt.txt`](https://huggingface.co/doctolib-lab/finemed-subdomain-classifier-fr/blob/main/assets/subdomain_annotation_prompt.txt).

## ⚠️ Intended Use & Limitations

Built to annotate French medical web text at corpus scale (to build FineMed-fr), not for clinical decision-making. Predictions are noisier on short or boilerplate documents, which the *Others* / *Other health* classes are meant to absorb.

<!--
## 📖 Citation

```bibtex
@misc{doctobert2026,
  title         = {Where Does the Signal Live? A Web Data Recipe for Medical Encoder Pretraining},
  author        = {Huang, Bofeng and Sun, Jacques and Bouchacourt, Diane and Barascud, Nicolas and Fogel, Fajwel},
  year          = {2026},
  eprint        = {2606.XXXXX},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL}
}
```
-->

## ⚖️ License

Apache-2.0.

## 🏛️ Acknowledgments

This work was granted access to the HPC resources of IDRIS (Jean Zay) under the allocations 2025-AD011016291 and 2026-A0200617487 made by GENCI.