BERT model for spam detection in Indonesian with 95% accuracy. This v3 model has been fine-tuned from v2 model with email dataset for optimal performance on Indonesian content.

Quick Start

python

from transformers import pipeline

# The easiest way to use the model
classifier = pipeline("text-classification",
                     model="newreyy/spam-detection-v4",
                     tokenizer="newreyy/spam-detection-v4")

# Test with text
texts = [
    "lacak hp hilang by no hp / imei lacak penipu/scammer/tabrak lari/terror/revengeporn sadap / hack / pulihkan akun",
    "Senin, 21 Juli 2025, Samapta Polsek Ngaglik melaksanakan patroli stasioner balong jalan palagan donoharjo",
    "Mari berkontribusi terhadap gerakan rakyat dengan membeli baju ini seharga Rp 160.000. Hubungi kami melalui WA 08977472296"
]

results = classifier(texts)
for text, result in zip(texts, results):
    print(f"Text: {text}")
    print(f"Result: {result['label']} (confidence: {result['score']:.4f})")
    print("---")

Model Details

Base Model: nahiar/spam-detection-bert-v2
Task: Binary Text Classification (Spam vs Ham)
Language: Indonesian (Bahasa Indonesia)
Model Size: ~110M parameters
Max Sequence Length: 512 tokens
Training Epochs: 3
Batch Size: 16
Learning Rate: 2e-5

Performance

Metric	HAM	SPAM	Overall
Precision	98%	77%	95%
Recall	96%	85%	95%
F1-Score	97%	81%	95%
Overall Accuracy	-	-	95%

Confusion Matrix

True HAM correctly predicted: 953/988 (96%)
True SPAM correctly predicted: 115/135 (85%)
False Positives (HAM predicted as SPAM): 35
False Negatives (SPAM predicted as HAM): 20

Key Features

Fine-tuned from v2 model with email dataset
Good accuracy (95%) on spam detection with Indonesian context
Better handling for spam email content
Enhanced performance on Indonesian email text
Optimized for Indonesian email and social media spam detection

Label Mapping

0: "HAM" (not spam)
1: "SPAM" (spam)

Training Process

This model was retrained using:

Optimizer: AdamW
Learning Rate: 2e-5
Epochs: 3
Batch Size: 16
Max Length: 128 tokens
Train/Validation Split: 80/20

Usage Example

python

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("nahiar/spam-detection-bert-v3")
model = AutoModelForSequenceClassification.from_pretrained("nahiar/spam-detection-bert-v3")

def predict_spam(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=1)
    predicted_label = torch.argmax(probs, dim=1).item()
    confidence = probs[0][predicted_label].item()
    label_map = {0: "HAM", 1: "SPAM"}
    return label_map[predicted_label], confidence

# Test
text = "Dapatkan uang dengan mudah! Klik link ini sekarang!"
result, confidence = predict_spam(text)
print(f"Prediksi: {result} (Confidence: {confidence:.4f})")

Citation

bibtex

@misc{nahiar_spam_detection_bert,
  title={Indonesian Spam Detection BERT},
  author={Raihan Hidayatullah Djunaedi},
  year={2025},
  url={https://huggingface.co/nahiar/spam-detection-bert-v3}
}

Changelog

Current Version v4 (Oktober 2025)

Fine-tuned from v2 model with email dataset (mail_data.csv)
Enhanced handling for Indonesian spam email content
Good performance (95% accuracy) on email spam detection
Optimized for Indonesian email and social media content
Improved with GPU-accelerated training using RTX 3080