AzrilFahmiardi/sdd-topic-classification

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

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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

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Performance Metrics

Metric	Value
Accuracy	0.8167
Macro F1	0.7871
Latency (mean)	9.36 ms
Model Size	474.7 MB

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Usage

Load Model

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

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

{
  "topic": "Ekonomi",
  "confidence": 0.9523
}

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Input/Output

Parameter	Type	Example
Input	str	Indonesian news text, max 256 tokens
Output	dict	{"topic": "Ekonomi", "confidence": 0.95}