rmtariq/malay_claim_classifier_v2

# Malay Claim Classifier

This model is fine-tuned on a dataset of Malaysian claims to classify them into different categories for fact-checking purposes. It's specifically designed to categorize claims in Bahasa Malaysia into 9 main categories.

Model Description

• Model Type: BERT-based sequence classification
• Language: Malay/Bahasa Malaysia
• Base Model: rmtariq/malay_classification
• Number of Labels: 9
• Labels: agama, alam sekitar, ekonomi, kesihatan, pendidikan, pengguna, politik, sosial, teknologi
• Model Size: 178M parameters
• Tensor Type: F32

Category Descriptions

• agama: Religious claims, including halal/haram issues
• alam sekitar: Environmental claims, climate, weather, natural disasters
• ekonomi: Economic claims, business, finance, trade
• kesihatan: Health claims, diseases, treatments, mental health
• pendidikan: Education claims, schools, universities, exams
• pengguna: Consumer product claims, brands, quality, safety
• politik: Political claims, government, policies, elections
• sosial: Social claims, culture, entertainment, sports, crime
• teknologi: Technology claims, digital, internet, innovations

Usage

from transformers import BertTokenizer, BertForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = BertTokenizer.from_pretrained("rmtariq/malay_claim_classifier_v2")
model = BertForSequenceClassification.from_pretrained("rmtariq/malay_claim_classifier_v2")

# Prepare input
example_claim = "Benarkah pewarna merah yang digunakan dalam makanan ringan dihasilkan daripada serangga dan tidak halal?"
inputs = tokenizer(example_claim, return_tensors="pt", padding=True, truncation=True, max_length=128)

# Get predictions
with torch.no_grad():
    outputs = model(**inputs)
    predictions = outputs.logits
    predicted_class = torch.argmax(predictions, dim=1).item()
    label = model.config.id2label[predicted_class]

print(f"Claim: {example_claim}")
print(f"Predicted Category: {label}")