Create README.md

README.md

@@ -0,0 +1,45 @@
+---
+datasets:
+- fake_news_english
+language:
+- en
+library_name: transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## 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]
+
+<!-- Provide the basic links for the model. -->
+
+- **Notebook:** [https://colab.research.google.com/drive/1G65Ye1UC-QeQXAJN9WPkGHvM0Qgo4Mf5?usp=sharing]
+
+## 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
+