metadata
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 on the Fakeddit dataset.
It combines post text with image captions generated by 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.
📄 References
This model builds on the following works:
- Fakeddit dataset: Nakamura et al., (2020) – A multimodal fake news dataset
- BLIP captioning model: Li et al. (2022) – Vision-language pretraining with BLIP
- BERT base model: Devlin et al. (2019) – Pretrained transformer for text understanding