File size: 2,116 Bytes
84d1543 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | # 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}` |
|