Xlm Roberta Base Fake News Detection Tl

🚀 Tagalog Fake News Detection Model

This project develops a fake news detection model for Tagalog/Filipino, leveraging the XLM - RoBERTa base model. It achieves an accuracy of 95.46%, offering a reliable solution for identifying fake news in Tagalog and Filipino texts.

🚀 Quick Start

The model is designed to classify text as either real or fake news. With high - performance metrics, it can be readily integrated into applications requiring fake news detection.

✨ Features

• Multilingual Support: Capable of handling both Tagalog (tl) and English (en).
• High Accuracy: Achieves an accuracy of 95.46% on the evaluation set.
• Comprehensive Metrics: Evaluated using multiple metrics including F1 score, precision, and recall.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

Dataset

• Total Size: 18,522 samples
• Composition: A 50/50 split of real and fake news
• Languages: Filipino, English

Dataset Split

• Train Set: Approximately 12,968 samples
• Validation Set: Approximately 2,784 samples
• Test Set: Approximately 2,770 samples

Performance Metrics (on Evaluation Set)

Data Sources

The model was trained on a combined dataset from two primary sources:

1. Fake News Filipino Dataset

   • 3,206 rows used

2. Philippine Fake News Corpus

   • 15,312 rows used out of 22,458 available

🔧 Technical Details

The model is based on the FacebookAI/xlm - roberta - base model. It is used for text classification tasks, specifically for detecting fake news in Tagalog and Filipino texts. The training data is sourced from two datasets, and the model shows high performance on multiple evaluation metrics.

📄 License

No license information is provided in the original document, so this section is skipped.