Model Card for Model ID
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Model Details
Model Description
This model is created using the fake news dataset from kaggle. The custom model is a fine tuned distilbert model with additional layers. The code was written in pytorch. The dataset was processed with removing symbols and converting text to lower case. The train - validate - test datasets are created in the ratio 60:20:20. The model was trained for two epochs and obtained an accuracy of 99%. However, the model has been shown to be overfitted on certain types of samples owing to lack of diversity in the samples. Please be cautious before using this model for a downstream use case
- Developed by: Aishwarya A. Nair
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: distilbert-base-uncased
Model Sources [optional]
Uses
Fake news detection can be used in the cases when you need to verify the veracity of a news article or a tweet or other pieces of text.
Bias, Risks, and Limitations
The model has been shown to be overfitted on certain types of samples owing to lack of diversity in the samples. Please be cautious before using this model for a downstream use case
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