Model Type:
Multi-task model combining multi-label classification and regression.
Description:
This model was fine-tuned to classify paragraphs from biomedical texts for their domain and document type, and predict an educational quality score via regression.
Training
The model was trained on a set of 400,000 paragraphs from PubMed Central, which were annotated by the
Llama 3.1 70B Instruct
model.
Purpose
This classifier was created to scale the initial high-quality annotations to the entire PubMed Open Access dataset. This distillation process enabled the creation of the large-scale Biomed-Enriched dataset while maintaining annotation consistency.
Model Outputs
The model predicts the following outputs:
Domain (Classification)
Clinical
Biomedical
Other
Document Type (Classification)
Clinical Case
Study
Review
Other
Educational Quality (Regression)
A regression score from
1
(low quality) to
5
(high quality).
Runs of almanach Biomed-Enriched-classifier on huggingface.co
25
Total runs
0
24-hour runs
5
3-day runs
6
7-day runs
15
30-day runs
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