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+ # Tagalog Fake News Detection Model
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+ ## Overview
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+ This project implements a fake news detection model for Tagalog/Filipino using the XLM-RoBERTa base model with an accuracy of 95.46%.
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+
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+ ### Dataset
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+ - Total Size: 18,522 samples
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+ - Composition: 50/50 split of real and fake news
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+ - Languages: Filipino and English
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+ -
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+ #### Dataset Split
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+ - Train Set: ~12,968 samples
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+ - Validation Set: ~2,784 samples
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+ - Test Set: ~2,770 samples
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+ ### Performance Metrics (on Evaluation Set)
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+ - Accuracy: 95.46%
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+ - F1 Score: 95.40%
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+ - Precision: 95.40%
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+ - Recall: 95.40%
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+
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+ ## Data Sources
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+ The model was trained on a combined dataset from two primary sources:
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+ 1. [Fake News Filipino Dataset](https://huggingface.co/datasets/jcblaise/fake_news_filipino)
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+ - 3,206 rows used
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+ 2. [Philippine Fake News Corpus](https://github.com/aaroncarlfernandez/Philippine-Fake-News-Corpus)
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+ - 15,312 rows used out of 22,458 available