---
language:
- tl
- en
tags:
- fake-news
- misinformation
- taglish
- code-switching
- filipino
- text-classification
pipeline_tag: text-classification
license: mit
---

# DistilBERT Taglish Fake News Detector (v1)

This model is based on **distilbert-base-multilingual-cased** and fine‑tuned on:

- **Fake News Filipino (FNF)** dataset  
- **Cluster‑disjoint splits** to prevent topic leakage  
- **Taglish paraphrase augmentation** (LLM‑generated Filipino–English code-mixed variants)  
- Evaluated on:
  - Full FNF test set
  - Topic-balanced subset
  - Mixed Taglish adversarial noise
  - **Real Taglish social-media posts (50 samples)**

## 🔥 Why this model?

Among all tested models (RoBERTa-Tagalog, mBERT, XLM‑R, DistilBERT), **DistilBERT + Taglish Paraphrasing** achieved the **highest real-world Taglish performance**, making it ideal for browser extensions and on-device inference.

### **Real Taglish Weighted F1 Scores:**

| Model Variant | Real Taglish F1 |
|--------------|------------------|
| RoBERTa Baseline | ~0.23 |
| RoBERTa + Paraphrasing | ~0.31 |
| RoBERTa + Adv | ~0.36 |
| mBERT | ~0.32 |
| XLM-R | ~0.35 |
| DistilBERT Baseline | ~0.36 |
| **DistilBERT + Paraphrase (THIS MODEL)** | **~0.41 (BEST)** |

This indicates stronger robustness to **noisy, code-switched Taglish** found on social media platforms.

---

## 🚀 Intended Use
- Chrome browser extension  
- Real-time fake news detection  
- Noisy Taglish text (Facebook, TikTok, IG comments, etc.)
- Code-switched Filipino–English environments  

Not a substitute for professional fact-checking.

## 📦 Labels
- **0 = real**
- **1 = fake**