Datasets:
metadata
license: mit
language:
- fr
size_categories:
- 10K<n<100K
task_categories:
- text-classification
- text-regression
tags:
- medical
- french
- biomedical
- clinical
- annotations
- high-quality
pretty_name: Biomed-FR-v3 Top 20% Quality Dataset
Biomed-FR-v3 Top 20% Quality Dataset
This dataset contains French biomedical text annotated with 20 different classification and regression tasks using the rntc/biomed-fr-v2-classifier
model.
Dataset Summary
- Total samples: 325
- Total columns: 41
- Annotation tasks: 25
- Language: French
- Domain: Biomedical/Clinical
- Filter criteria: Top 20% quality: educational_score, writing_quality, content_richness, terminology_precision all >= 80th percentile
Key Features
- ✅ Complete annotation coverage: All 20 tasks from biomed-fr-v2-classifier
- ✅ Includes
rewriting_needed
: Critical regression task for content quality - ✅ Quality metrics: Educational scores, terminology precision, content richness
- ✅ Clinical focus: Medical subfield classification, clinical case detection
- ✅ Proper column order: Original educational_score preserved (1-5 scale)
Annotation Tasks
Regression Tasks (15)
rewriting_needed
: Content rewriting necessity scorecontains_bias
: Bias detection scorewriting_quality
: Text quality assessmentterminology_precision
: Medical terminology accuracycontent_richness
: Information density score- Plus others: age_group, assertion_type, certainty_level, etc.
Classification Tasks (5)
medical_subfield
: 45 medical specialtiescontent_type
: 9 content categorieswriting_style
: 5 writing stylestext_type
: meaningful vs incompleteinteractive_elements
: 4 interaction types
Usage
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("rntc/bb-tt-3-s4")
# Access key annotations
texts = dataset["train"]["text"]
rewriting_scores = dataset["train"]["rewriting_needed"]
educational_scores = dataset["train"]["educational_score"] # Original 1-5 scale
medical_fields = dataset["train"]["medical_subfield"]
Data Quality
- All samples processed with consistent batch processing
- Original educational_score preserved (0.58-5.10 scale)
- Regression outputs clearly separated (e.g., educational_score_predicted)
- Dimension mismatches handled for classification tasks
- Complete 20-task coverage including previously missing regression tasks
Model Information
Annotations generated using:
- Model:
rntc/biomed-fr-v2-classifier
- Base model:
almanach/camembertv2-base
- Tasks: 20 multi-task classification and regression heads
- Key fix: Restored original educational_score column
Citation
@dataset{biomed_fr_v3_annotated,
title={Biomed-FR-v3 Top 20% Quality Dataset},
author={RNTC Research Team},
year={2024},
url={https://huggingface.co/datasets/rntc/bb-tt-3-s4},
note={French biomedical corpus with complete 20-task annotations}
}
License
MIT License - see LICENSE file for details.
Related Datasets
- Full dataset:
rntc/bb-tt-3
- Pretraining subset:
rntc/bb-tt-3-pretrain