Tagalog Fake News Detection Model
Overview
This project implements a fake news detection model for Tagalog/Filipino using the XLM-RoBERTa base model with an accuracy of 95.46%.
Dataset
- Total Size: 18,522 samples
- Composition: 50/50 split of real and fake news
- Languages: Filipino, English
Dataset Split
- Train Set: ~12,968 samples
- Validation Set: ~2,784 samples
- Test Set: ~2,770 samples
Performance Metrics (on Evaluation Set)
- Accuracy: 95.46%
- F1 Score: 95.40%
- Precision: 95.40%
- Recall: 95.40%
Data Sources
The model was trained on a combined dataset from two primary sources:
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- 3,206 rows used
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- 15,312 rows used out of 22,458 available
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Model tree for iceman2434/xlm-roberta-base-fake-news-detection-tl
Base model
FacebookAI/xlm-roberta-base