FineMed Subdomain Classifier (FR)
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📚 Introduction
This is the medical-subdomain classifier used to annotate 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 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.
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 labels 1M documents (high-volume supervision).
- Stage 2: 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.
⚠️ 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.
⚖️ 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.