---
license: apache-2.0
pipeline_tag: text-classification
language:
- en
base_model:
- google-bert/bert-base-uncased
tags:
- PyTorch
---

# 🤖 BERT for Fake News Detection (Fakeddit + BLIP Captions)

This model is a fine-tuned [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) on the **Fakeddit** dataset.  
It combines post text with **image captions generated** by [`Salesforce/blip-image-captioning-base`](https://huggingface.co/Salesforce/blip-image-captioning-base), rather than using raw image features.

## 🧠 Model Summary

- **Architecture**: BERT (uncased)
- **Inputs**: `[CLS] post text, BLIP image caption [SEP]`
- **Task**: Multi-class classification (6 labels)
- **Dataset**: Fakeddit (Nakamura et al., 2020)
- **Captioning Model**: `Salesforce/blip-image-captioning-base`

---

## 📊 Results

| Approach         | Accuracy | Macro F1-Score |
|------------------|----------|----------------|
| Text + Caption   | **0.87** | **0.83**       |

⚡️ Using captions instead of raw image features leads to state-of-the-art performance on Fakeddit, with simpler input and no vision backbone needed during inference.

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## 📄 References

This model builds on the following works:

- **Fakeddit dataset**: [Nakamura et al., (2020)](https://arxiv.org/abs/1911.03854) – A multimodal fake news dataset  
- **BLIP captioning model**: [Li et al. (2022)](https://arxiv.org/abs/2201.12086) – Vision-language pretraining with BLIP  
- **BERT base model**: [Devlin et al. (2019)](https://arxiv.org/abs/1810.04805) – Pretrained transformer for text understanding