tmt3103/BioASQ-yesno-PudMedBERT

BioASQ Yes/No Question Classifier

Model Details

• —Model architecture: BERT
• —Pretrained base: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
• —Fine-tuned on: BioASQ Phase B Yes/No question dataset
• —Task type: Binary classification (Yes/No)
• —Input format: Concatenated question and supporting context passages
• —Output: Probability distribution over two classes ("Yes", "No")
• —Tokenizer: Depends on base model (WordPiece or SentencePiece) ---

Dataset

• —Name: BioASQ Task B Phase B Yes/No dataset
• —Domain: Biomedical question answering
• —Data format: Each sample consists of a yes/no question paired with one or more relevant context snippets extracted from biomedical abstracts
• —Split: Standard train/dev split from BioASQ ---

Performance

Evaluation performed on the BioASQ dev set. ---

Usage Example

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("tmt3103/BioASQ-yesno-PudMedBERT")
model = AutoModelForSequenceClassification.from_pretrained("tmt3103/BioASQ-yesno-PudMedBERT")

def predict_yesno(question: str, context: str) -> str:
    inputs = tokenizer(question, context, truncation=True, padding=True, return_tensors="pt")
    outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
    return "Yes" if probs[0][1] > probs[0][0] else "No"

# Example usage
question = "Does aspirin reduce inflammation?"
context = "Aspirin is widely used as an anti-inflammatory medication in clinical practice."
print(f"Question: {question}\nPredicted answer: {predict_yesno(question, context)}")

Future Work & Maintenance

• —Retrain regularly with updated BioASQ datasets to maintain relevance.
• —Implement uncertainty estimation for safer decision support.
• —Expand to multi-class or multi-label biomedical QA tasks.
• —Optimize for deployment efficiency and latency reduction. ---

Contact & Support

For questions, issues, or collaboration inquiries, please contact:

• —Author / Maintainer: Minh Tien
• —Email: tmt31032005@gmail.com
• —GitHub: TMTien31 ---