README.md

metadata

license: mit
tags:
  - roberta
  - text-classification
  - healthcare
  - biomedical
  - adverse-drug-reaction
  - nlp
datasets:
  - custom
language:
  - en
model-index:
  - name: RoBERTa ADR Severity Classifier
    results:
      - task:
          name: Text Classification
          type: text-classification
        metrics:
          - type: accuracy
            value: 0.891
          - type: f1
            value: 0.891
          - type: auc
            value: 0.956

🤖 RoBERTa ADR Severity Classifier

This is a fine-tuned RoBERTa model that detects Adverse Drug Reactions (ADRs) and classifies them as either severe (1) or not severe (0). It is trained on annotated ADR text data and is part of a broader NLP pipeline that extracts symptoms, diseases, and medications from biomedical reports.

---

🧠 Model Details

• Base Model: roberta-base
• Task: Binary Text Classification (Severe vs Not Severe)
• Training Data: 3,000+ annotated ADR descriptions
• Framework: Hugging Face Transformers + PyTorch

---

🔬 Intended Use

This model is intended for research and educational purposes in biomedical NLP. It can be used to:

• Flag potentially dangerous side effects in user-reported ADRs
• Prioritize ADR cases based on severity
• Serve as a backend for medical QA systems or healthcare apps

---

📈 Performance

Evaluated on a balanced test set of 1,623 samples:

Metric	Class 0 (Not Severe)	Class 1 (Severe)
Precision	0.904	0.880
Recall	0.865	0.915
F1-Score	0.884	0.897
Accuracy	0.891
AUC	0.956

---

🚀 Example Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained("calerio-uva/roberta-adr-model")
tokenizer = AutoTokenizer.from_pretrained("calerio-uva/roberta-adr-model")

text = "Severe migraine with vision loss and vomiting after taking ibuprofen."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)

with torch.no_grad():
    logits = model(**inputs).logits
    probs = torch.softmax(logits, dim=1)

print(f"Not Severe: {probs[0][0]:.3f}, Severe: {probs[0][1]:.3f}")