---
license: mit
language: en
tags:
  - text-classification
  - fake-news-detection
  - transformer-ensemble
  - bert
  - deberta
library_name: transformers
datasets:
  - custom
model_name: BertAndDeberta
---

# BertAndDeberta + ViT — Transformer Ensemble for Fake News Detection

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.


##  Text Models — Fake News Detection
- **Architecture**: Ensemble of BERT-base and DeBERTa-base
- **Task**: Binary Text Classification (`REAL` vs `FAKE`)
- **Training Framework**: PyTorch using 🤗 Transformers
- **License**: MIT

    
## Vision Model — AI-Generated Image Detection
- **Architecture**: Vision Transformer (ViT-base, vit-base-patch16-224)
- **Task**: Binary Image Classification ('REAL' vs 'AI-GENERATED')
- **Training Framework**: TensorFlow/Keras or PyTorch using Transformers
- **License**: MIT

## Dataset

The dataset is a custom collection combining:
- News content (title + body)
- Labels: `0 = FAKE`, `1 = REAL`

## ❗Disclaimer
- This project is for **educational and experimental purposes only**.
- It is **not suitable for real-world fact-checking** or serious decision-making.
- The model uses a simple binary classifier and does not verify factual correctness.
- Model may sometimes misclassify text with unclear or misleading context, and images that are abstract, artistic, or difficult to distinguish from real content.

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("fauxNeuz/BertAndDeberta")
model = AutoModelForSequenceClassification.from_pretrained("fauxNeuz/BertAndDeberta")

text = "Government confirms policy updates in healthcare sector."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
pred = outputs.logits.argmax(dim=-1).item()
print("REAL" if pred == 1 else "FAKE")