nahiar/hatespeech-abusive-xlm-roberta-v1

Hate Speech & Abusive Language Detection (Multilabel)

Multilingual Indonesian & English — XLM-RoBERTa

This repository provides a fine-tuned XLM-RoBERTa model for MULTILABEL HATE CONTENT DETECTION in social media text.
The model is designed to identify Hate Speech and Abusive Language simultaneously across Indonesian, regional Indonesian languages, and English, particularly in noisy and informal online conversations.

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🚀 Highlights

• Multilabel classification: Hate Speech & Abusive Language
• Supports overlapping labels in a single text
• Multilingual (Indonesia + English)
• Robust on informal and user-generated content
• Ready-to-use with Hugging Face pipeline
• Suitable for content moderation and safety systems

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🌍 Supported Languages

• 🇮🇩 Bahasa Indonesia
• Bahasa Melayu
• Indonesian regional languages (Aceh, Banjar, Bugis, Jawa, Madura, Minang, Sunda, dll.)
• 🇬🇧 English

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📊 Model Performance

Performance metrics are reported on a held-out validation set.

Metric	Score
Precision	0.9249
Recal	0.9300
F1 (Macro)	0.9274
F1 (Weighted)	0.9269
Training Loss	0.1181
Validation Loss	0.2070

(Exact scores may vary depending on evaluation split and threshold.)

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⚙️ Usage

Installation

pip install transformers torch

Single Prediction

from transformers import pipeline

classifier = pipeline(
    task="text-classification",
    model="nahiar/hatespeech-abusive-xlm-roberta-v1",
    return_all_scores=True
)

result = classifier("Dasar bodoh, otak udang!")
print(result)

Output

[
  {'label': 'HATESPEECH', 'score': 0.9123},
  {'label': 'ABUSIVE', 'score': 0.9841}
]

Because this is a multilabel model, more than one label can be active for a single input.

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🏷️ Label Definitions

HATESPEECH → Content that attacks or demeans a group based on identity
ABUSIVE    → Insulting, offensive, or aggressive language without protected targets

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📦 Batch Inference

texts = [
    "Dasar kaum ini selalu bikin rusuh",
    "Kamu memang bodoh dan tidak berguna",
    "Saya tidak setuju dengan pendapat kamu"
]

results = classifier(texts)

for text, preds in zip(texts, results):
    labels = [(p["label"], round(p["score"], 4)) for p in preds]
    print(text, "→", labels)

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🏗️ Training Configuration

Parameter	Value
Base Model	xlm-roberta-base
Task Type	Multilabel Classification
Training Strategy	Fine-tuning
Epochs	Multiple
Learning Rate	2e-5
Batch Size	16
Training Date	2025-12-18

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🎯 Intended Use

• Hate speech & abusive language moderation
• Content safety and compliance systems
• Social media monitoring dashboards
• Pre-filtering before sentiment or topic analysis

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⚠️ Limitations

• Limited to Hate Speech and Abusive Language labels
• Does not identify specific hate targets or protected attributes
• Context-dependent sarcasm may be misclassified
• Not suitable for legal or policy enforcement without human review

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📜 License

This model is released under the Apache License 2.0 Free for research and commercial use.

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📚 Citation

@misc{djunaedi2025hatespeech_multilabel,
  author    = {Raihan Hidayatulloh Djunaedi},
  title     = {Multilabel Hate Speech and Abusive Language Detection for Social Media Text},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/nahiar/hatespeech-xlmr-v4}
}

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🙌 Acknowledgements

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