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distilbert-fnf-taglish-v1
renshhhh/distilbert-fnf-taglish-v1
distilbert-fnf-taglish-v1 is a text classification model from renshhhh. Use it when you need a label for a piece of text. The card lists the license as mit.
This model is based on distilbert-base-multilingual-cased and fine‑tuned on:
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At a glance
- Task
- Text Classification
- License
- mit
- Model type
- distilbert
- Access
- Public
- Created
- Nov 30, 2025
- Updated
- Nov 30, 2025
- SHA
- 862546c5
Model card
From the Hugging Face model README
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
- Task
- Text Classification
- Type
- distilbert
- License
- mit
- Languages
- tl, en
- Created
- Nov 30, 2025
- Updated
- Nov 30, 2025