Create README.md

README.md

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+# Model Card: Topic Classification
+
+## Model Overview
+
+**Model Name:** sdd-topic-classification  
+**Base Model:** `indobenchmark/indobert-base-p2`  
+**Task:** Multi-class text classification (13 news categories)  
+**Language:** Indonesian
+
+---
+
+## Model Description
+
+Fine-tuned IndoBERT for classifying Indonesian news articles into 13 topic categories.
+
+**Categories:**
+Budaya, Ekonomi, Entertainment, HukumDanKriminal, Kesehatan, Lifestyle, Otomotif, Pendidikan, Politik, Sport, Tekno, Wisata, Lainnya
+
+---
+
+## Performance Metrics
+
+| Metric | Value |
+|---|---|
+| Accuracy | 0.8167 |
+| Macro F1 | 0.7871 |
+| Latency (mean) | 9.36 ms |
+| Model Size | 474.7 MB |
+
+---
+
+## Usage
+
+### Load Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "AzrilFahmiardi/sdd-topic-classification"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+```
+
+### Inference
+
+```python
+def classify_text(text: str) -> dict:
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256).to(device)
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+    
+    probabilities = torch.softmax(logits, dim=-1)[0].cpu()
+    predicted_class = logits.argmax(-1).item()
+    predicted_label = model.config.id2label[predicted_class]
+    confidence = probabilities[predicted_class].item()
+    
+    return {
+        "topic": predicted_label,
+        "confidence": confidence
+    }
+
+# Example
+text = "Bank Indonesia pertahankan suku bunga acuan di tengah tekanan inflasi global."
+result = classify_text(text)
+print(f"Topic: {result['topic']} ({result['confidence']:.2%})")
+```
+
+### Output Format
+
+```json
+{
+  "topic": "Ekonomi",
+  "confidence": 0.9523
+}
+```
+
+---
+
+## Input/Output
+
+| Parameter | Type | Example |
+|---|---|---|
+| **Input** | str | Indonesian news text, max 256 tokens |
+| **Output** | dict | `{"topic": "Ekonomi", "confidence": 0.95}` |