spam-detection-xlm-roberta-v1

Spam Detection for Social Media Text

Multilingual Indonesian & English | XLM-RoBERTa

This model is a fine-tuned XLM-RoBERTa designed to detect Spam vs Ham content in social media text.
It supports Indonesian regional languages, Malay, and English, making it suitable for multi-platform moderation use cases such as Twitter/X, Instagram, TikTok, Facebook, and online forums.

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✨ Key Features

• ✅ Spam vs Ham classification
• 🌏 Multilingual support (Indonesian & English + regional languages)
• 🧠 Based on XLM-RoBERTa (multilingual transformer)
• ⚡ Ready-to-use with Hugging Face pipeline
• 📊 Strong performance on noisy social media text

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🌍 Supported Languages

• 🇮🇩 Bahasa Indonesia
• Bahasa Melayu
• Basa Jawa
• Bahasa daerah Indonesia (Aceh, Banjar, Bugis, Minang, Sunda, dll.)
• 🇬🇧 English

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🧪 Model Performance

Evaluated on held-out validation data with balanced spam/ham distribution.

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🚀 Quick Start

Installation

pip install transformers torch

Single Prediction

from transformers import pipeline

classifier = pipeline(
    task="text-classification",
    model="nahiar/spam-detection-xlm-roberta-v1"
)

result = classifier("PASTI DIJAMIN WDP 100%")
print(result)

Output

[{'label': 'LABEL_1', 'score': 0.9876}]

Label Mapping

LABEL_0 → SPAM
LABEL_1 → HAM

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📦 Batch Inference Example

"texts": [
        "साइबर हमले के बाद JLR का बड़ा बयान - जानें कंपनी ने क्या कहा | Tata Motors के शेयर पर दिखेगा असर?

#TataMotors #JLR #CyberAttack 

https://t.co/6WlGS77UUp",
        "Kita sudah Ready skrg ini bagi yang memerlukan jasa pemulihan akun & Hapus All akun 

 Lacak lokasi / sadap wa / Hack Akun / Revengeporn - korban pemerasan vcs / terror

TIKTOK,GMAIL,TWITER,TELEGRAM,
FACEBOOK,INSTAGRAM 
#revengeporn #zonauangᅠᅠᅠ 
 ☎️ https://t.co/K0AbW08qnU https://t.co/4IpWNA7a0z",
        "💥Slot Gacor Hari ini Rute303
💥Jaminan Jackpot Maxwin malam ini

LINK SLOT GACOR HARI INI : https://t.co/QvxjCAnt8o

Tags:
Jumbo #timsekop Jumat gratis ongkir Like Crazy PSIM https://t.co/ukuRdlvgGA"
    ]

results = classifier(texts)

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

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🏗️ Training Configuration

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🎯 Intended Use Cases

• Social media spam moderation
• Comment & post filtering
• Content quality control
• Pre-filtering for sentiment or topic analysis pipelines

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⚠️ Limitations

• Binary classification only (Spam / Ham)
• Not optimized for non-social-media formal text
• Performance may degrade on very short or ambiguous messages

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📜 License

Released under the Apache 2.0 License. Free for commercial and research use.

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📚 Citation

If you use this model in your work, please cite:

@misc{djunaedi2025spam,
  author    = {Raihan Hidayatullah Djunaedi},
  title     = {Spam Detection for Social Media Text},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/nahiar/spam-detection-xlm-roberta-v1}
}

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🙌 Acknowledgements

• Hugging Face Transformers
• Facebook AI Research — XLM-RoBERTa