kesavanguru/XLM_roberta_finetuned

🎯 XLM-RoBERTa Clickbait Detector

Model Overview

This model is a fine-tuned version of christinacdl/XLM_RoBERTa-Clickbait-Detection-new trained to classify headlines into Clickbait and Legitimate News categories.

The model achieves state-of-the-art performance on clickbait detection:

Metric	Value
Accuracy	99.90%
F1-Score	0.9990
Validation Loss	0.0068

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📊 Model Details

• Model Type: Sequence Classification (Binary)
• Base Model: XLM-RoBERTa (Cross-lingual RoBERTa)
• Language: English (with multilingual capabilities via XLM-RoBERTa)
• Task: Clickbait Detection
• Output Classes: 2 (Clickbait, Legitimate News)
• Model Size: ~270M parameters
• License: MIT

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🚀 Intended Uses

Primary Use Cases:

• 🔍 Automated clickbait detection in news feeds and social media
• 📱 Browser extensions and browser plugins for user warnings
• 📰 News aggregator platforms for content filtering
• 🤖 Content moderation systems for social platforms
• 📊 Media analytics and trend detection

Intended Audience:

• News organizations and publishers
• Social media platforms
• Content moderation teams
• Researchers studying misinformation
• Browser extension developers

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⚠️ Limitations

Model-Specific Limitations:

• Language Scope: Optimized for English headlines. While built on XLM-RoBERTa which supports 100+ languages, performance on non-English content may vary significantly
• Domain Bias: Trained on news and media headlines; may not generalize well to other domains (scientific papers, technical blogs, legal documents)
• Context Dependency: Classifies headlines in isolation without full article context
• Emerging Patterns: May struggle with new or evolving clickbait tactics not present in training data
• Sarcasm & Irony: Can be challenged by figurative language and subtle linguistic tricks

Recommendations:

• Use primarily for English-language headlines
• Validate on domain-specific data before production deployment
• Combine with contextual analysis for edge cases
• Monitor performance on new clickbait patterns
• Consider ensemble approaches for critical applications

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📚 Training and Evaluation Data

Dataset Information

• Dataset Type: News headlines with clickbait binary labels
• Language: English
• Train/Eval Split: Not specified
• Preprocessing: Standard tokenization via XLM-RoBERTa tokenizer

Data Characteristics

• Headlines from news sources and social media
• Binary labels: Clickbait (0) and Legitimate News (1)
• Diverse linguistic patterns and sensationalism levels
• Representative of modern digital media language

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🛠️ Training Procedure

Training Hyperparameters

Parameter	Value
Base Model	christinacdl/XLM_RoBERTa-Clickbait-Detection-new
Learning Rate	2e-05
Train Batch Size	32
Eval Batch Size	32
Gradient Accumulation Steps	2
Effective Batch Size	64
Epochs	2
Optimizer	AdamW (Fused)
Optimizer Betas	(0.9, 0.999)
Optimizer Epsilon	1e-08
LR Scheduler	Linear warmup
Mixed Precision	Native AMP (FP16)
Random Seed	42

Training Optimization Strategy

• Mixed Precision Training: FP16 with Native AMP for memory efficiency
• Gradient Accumulation: 2 steps to simulate larger batch size (64) with memory constraints
• Optimizer: AdamW Fused implementation for faster computation
• Learning Rate Schedule: Linear warmup followed by linear decay

Training Results

Epoch	Training Loss	Step	Validation Loss	Accuracy	F1 Score
1.0	—	400	0.0067	0.9984	0.9984
2.0	0.0167	800	0.0068	0.9990	0.9990

Key Observations:

• Rapid convergence to near-perfect accuracy
• Minimal overfitting (validation loss stable across epochs)
• F1-Score indicates well-balanced precision and recall
• Peak performance achieved at epoch 2

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📦 Framework Versions

Library	Version
Transformers	4.57.3
PyTorch	2.9.0+cu126
Datasets	4.0.0
Tokenizers	0.22.2

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💻 How to Use

Basic Usage

from transformers import pipeline

# Load the model
classifier = pipeline("text-classification", 
                     model="kesavanguru/XLM_roberta_finetuned")

# Classify a headline
headline = "You Won't Believe What Happened Next! Click Here!"
result = classifier(headline)

print(result)
# Output: [{'label': 'LABEL_0', 'score': 0.9998}]

Advanced Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "kesavanguru/XLM_roberta_finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Batch classification
headlines = [
    "Scientists Make Shocking Discovery - You Won't Believe!",
    "New Climate Study Released by UN Scientists",
    "This One Trick Will Change Your Life Forever"
]

inputs = tokenizer(headlines, padding=True, truncation=True, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predictions = torch.argmax(logits, dim=-1)

for headline, pred in zip(headlines, predictions):
    label = "Clickbait" if pred.item() == 0 else "Legitimate"
    print(f"{headline} → {label}")

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🔄 Model Architecture

XLM-RoBERTa Base (270M parameters)
        ↓
    [CLS] Token Representation
        ↓
Sequence Classification Head
        ↓
Binary Output (Softmax)

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📈 Performance Analysis

• Accuracy: 99.90% - Excellent for binary classification
• F1-Score: 0.9990 - Indicates balanced precision and recall
• Loss: 0.0068 - Very low validation loss, minimal overfitting
• Training Efficiency: 2 epochs sufficient for convergence

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🤝 Contributing

Contributions, issues, and feature requests are welcome!

To contribute:

1. Open an issue to discuss proposed changes
2. Submit a pull request with improvements
3. Share feedback on model performance

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📝 Citation

If you use this model in your research or application, please cite:

@model{xlm_roberta_clickbait_2024,
  title={XLM-RoBERTa Fine-tuned for Clickbait Detection},
  author={Kesavanguru},
  year={2024},
  publisher={Hugging Face},
  howpublished={https://huggingface.co/kesavanguru/XLM_roberta_finetuned}
}

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📄 License

This model is licensed under the MIT License. See LICENSE file for details.

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✨ Acknowledgments

• Built on XLM-RoBERTa by Facebook
• Base model from christinacdl/XLM_RoBERTa-Clickbait-Detection-new
• Developed with Hugging Face Transformers library

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Model Card Updated: January 2026 | Last Training: 2 epochs | Status: Production Ready

Developed by Kesavanguru | Model Repository