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.