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---
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](https://huggingface.co/roberta-base) 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
```python
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}") |