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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 score
  • contains_bias: Bias detection score
  • writing_quality: Text quality assessment
  • terminology_precision: Medical terminology accuracy
  • content_richness: Information density score
  • Plus others: age_group, assertion_type, certainty_level, etc.

Classification Tasks (5)

  • medical_subfield: 45 medical specialties
  • content_type: 9 content categories
  • writing_style: 5 writing styles
  • text_type: meaningful vs incomplete
  • interactive_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