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--- |
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datasets: |
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- jcblaise/fake_news_filipino |
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- SEACrowd/ph_fake_news_corpus |
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language: |
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- tl |
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- en |
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base_model: |
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- FacebookAI/xlm-roberta-base |
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pipeline_tag: text-classification |
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tags: |
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- fake-news-detection |
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- text-classification |
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- tagalog |
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- filipino |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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--- |
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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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### 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, English |
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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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## 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://huggingface.co./datasets/SEACrowd/ph_fake_news_corpus) |
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- 15,312 rows used out of 22,458 available |
