SubhaL/biobert-research-insights

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

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

Metric	Score
Accuracy	86.6%
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.