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SubhaL/biobert-research-insights

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
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Model Card

BioBERT Research Insights

This model is a fine-tuned BioBERT on the PubMed 20k RCT dataset. It classifies sentences from biomedical abstracts into one of five categories:

  • —BACKGROUND
  • —OBJECTIVE
  • —METHODS
  • —RESULTS
  • —CONCLUSIONS

Usage

python
from transformers import pipeline

classifier = pipeline("text-classification", model="SubhaL/biobert-research-insights")

example = "The trial demonstrated significant improvement in patient survival rates."
result = classifier(example)

print(result)

Evaluation Metrics

The model was evaluated on the PubMed 20k RCT test dataset, which contains 5 sentence classes:

  • —0: BACKGROUND
  • —1: OBJECTIVE
  • —2: METHODS
  • —3: RESULTS
  • —4: CONCLUSIONS
MetricScore
Accuracy86.6%
Precision (weighted)86.7%
Recall (weighted)86.6%
F1-score (weighted)86.6%

Class-wise performance highlights:

  • —METHODS and RESULTS classes achieve high precision and recall (~93-94%), indicating strong performance in identifying these sections.
  • —Lower scores on BACKGROUND and OBJECTIVE suggest these categories are more challenging to distinguish, likely due to overlapping language.