---
language:
- id
base_model:
- Exqrch/IndoDiscourse-ToxicityClassifier
pipeline_tag: text-classification
---
# mentilinSafe-BERT-Multiclass

Model ini adalah fine-tuned version dari `Exqrch/IndoDiscourse-ToxicityClassifier` (BERT) yang dikonfigurasi ulang untuk **Multi-class Classification** guna mendeteksi 19 jenis kategori bahaya (harm) dalam bahasa Indonesia berdasarkan dataset IndoSafety.

## Detail Model
- **Base Model:** BERT (IndoDiscourse-ToxicityClassifier)
- **Tugas:** Multi-class Text Classification
- **Jumlah Kategori:** 19 Kategori
- **Akurasi Evaluasi:** 95.39%

## Kategori yang Didukung
Model ini dapat mengklasifikasikan teks ke dalam kategori berikut:
- Adult Content
- Assisting illegal activities
- Causing material harm by disseminating misinformation e.g. in medicine or law
- Compromise privacy by leaking or inferring private information (person/individual)
- Disseminating false or misleading information
- Ethnicities and Cultural Practices
- Historical Controversies
- Indonesian Entities
- Mental Health or Overreliance Crisis
- Nudging or advising users to perform unethical or unsafe actions
- Pancasila Misinterpretation and Corruption
- Reducing the cost of disinformation campaigns
- Regional Separatism Advocacy
- Religions and Beliefs
- Risks from leaking or inferring sensitive information (organization/gov)
- Social stereotypes and unfair discrimination
- Supernatural
- Toxic language (hate speech)
- Treat Chatbot as a Human

## Cara Penggunaan

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "mSatashi/mentilinSafe-BERT-Multiclass"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
        pred_id = torch.argmax(probs, dim=-1).item()
    return model.config.id2label[pred_id], probs[0][pred_id].item()

text = "Masukkan kalimat di sini"
label, score = predict(text)
print(f"Kategori: {label} ({score:.4f})")
```

## Hasil Evaluasi
Berdasarkan pengujian pada dataset `IndoSafety-Eval-1`:
- **Accuracy:** 0.9539
- **Loss:** 0.1924