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--- |
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license: mit |
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language: en |
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tags: |
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- text-classification |
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- fake-news-detection |
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- transformer-ensemble |
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- bert |
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- deberta |
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library_name: transformers |
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datasets: |
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- custom |
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model_name: BertAndDeberta |
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--- |
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# BertAndDeberta + ViT — Transformer Ensemble for Fake News Detection |
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This repository hosts a multimodal fake news detection system that combines **BERT**, **DeBERTa**, and **ViT** models. The *BERT* and *DeBERTa* models handle textual data to classify news as either real or fake, while the *ViT* model is trained separately to detect AI-generated vs real images, helping assess the authenticity of visual content. |
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## Text Models — Fake News Detection |
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- **Architecture**: Ensemble of BERT-base and DeBERTa-base |
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- **Task**: Binary Text Classification (`REAL` vs `FAKE`) |
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- **Training Framework**: PyTorch using 🤗 Transformers |
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- **License**: MIT |
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## Vision Model — AI-Generated Image Detection |
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- **Architecture**: Vision Transformer (ViT-base, vit-base-patch16-224) |
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- **Task**: Binary Image Classification ('REAL' vs 'AI-GENERATED') |
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- **Training Framework**: TensorFlow/Keras or PyTorch using Transformers |
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- **License**: MIT |
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## Dataset |
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The dataset is a custom collection combining: |
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- News content (title + body) |
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- Labels: `0 = FAKE`, `1 = REAL` |
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## ❗Disclaimer |
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- This project is for **educational and experimental purposes only**. |
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- It is **not suitable for real-world fact-checking** or serious decision-making. |
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- The model uses a simple binary classifier and does not verify factual correctness. |
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- Model may sometimes misclassify text with unclear or misleading context, and images that are abstract, artistic, or difficult to distinguish from real content. |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("fauxNeuz/BertAndDeberta") |
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model = AutoModelForSequenceClassification.from_pretrained("fauxNeuz/BertAndDeberta") |
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text = "Government confirms policy updates in healthcare sector." |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model(**inputs) |
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pred = outputs.logits.argmax(dim=-1).item() |
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print("REAL" if pred == 1 else "FAKE") |
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