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README.md

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+# 🤖 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}")