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mSatashi/
mentilinSafe-IndoDiscourse-harm-Multiclass

Name: mentilinSafe-IndoDiscourse-harm-Multiclass
Availability: InStock

text-classification

Exqrch/IndoDiscourse-ToxicityClassifier

finetune

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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

mSatashi/mentilinSafe-IndoDiscourse-harm-Multiclass