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