Model Card for Model ID
This model is a binary text classification model fine-tuned on the WELFake dataset to detect whether a given news article or headline is real or fake.
It is built on top of the BERT-base-uncased transformer from Hugging Face and achieves around 90% accuracy with balanced precision, recall, and F1-scores.
The model is designed for research and educational purposes, providing a reliable baseline for misinformation detection tasks. It can be used directly through Hugging Face transformers pipelines or integrated into applications via API.
Model Details
Model Description
- Developed by: [Mouniesh]
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- Model type: [Text Classification (Binary)]
- Language(s) (NLP): [English]
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- Finetuned from model [optional]: [bert-base-uncased]
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Uses
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
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Training Details
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Training Procedure
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Summary
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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