
# Fake news classifier

The project is submitted as a part of application for Fatima Fellowship. The problem statement is stated as below: 

### Train a text classification model to detect fake news articles!
* Download the dataset here: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
* Develop an NLP model for classification that uses a pretrained language model
* Finetune your model on the dataset, and generate an AUC curve of your model on the test set of your choice. 
* [Upload the the model to the Hugging Face Hub](https://huggingface.co/docs/hub/adding-a-model), and add a link to your model below.
* *Answer the following question*: Look at some of the news articles that were classified incorrectly. Please explain what you might do to improve your model's performance on these news articles in the future (you do not need to impelement these suggestions)

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{"language": ["python3"], "tags": ["NLP, classification"]}
region:us
TAGS
#region-us 


# Fake news classifier

The project is submitted as a part of application for Fatima Fellowship. The problem statement is stated as below: 

### Train a text classification model to detect fake news articles!
* Download the dataset here: URL
* Develop an NLP model for classification that uses a pretrained language model
* Finetune your model on the dataset, and generate an AUC curve of your model on the test set of your choice. 
* Upload the the model to the Hugging Face Hub, and add a link to your model below.
* *Answer the following question*: Look at some of the news articles that were classified incorrectly. Please explain what you might do to improve your model's performance on these news articles in the future (you do not need to impelement these suggestions)

---

# Fake news classifier

The project is submitted as a part of application for Fatima Fellowship. The problem statement is stated as below: ### Train a text classification model to detect fake news articles!
* Download the dataset here: URL
* Develop an NLP model for classification that uses a pretrained language model
* Finetune your model on the dataset, and generate an AUC curve of your model on the test set of your choice. 
* Upload the the model to the Hugging Face Hub, and add a link to your model below.
* *Answer the following question*: Look at some of the news articles that were classified incorrectly. Please explain what you might do to improve your model's performance on these news articles in the future (you do not need to impelement these suggestions)

---
TAGS
#region-us 
 # Fake news classifier

The project is submitted as a part of application for Fatima Fellowship. The problem statement is stated as below: ### Train a text classification model to detect fake news articles!
* Download the dataset here: URL
* Develop an NLP model for classification that uses a pretrained language model
* Finetune your model on the dataset, and generate an AUC curve of your model on the test set of your choice. 
* Upload the the model to the Hugging Face Hub, and add a link to your model below.
* *Answer the following question*: Look at some of the news articles that were classified incorrectly. Please explain what you might do to improve your model's performance on these news articles in the future (you do not need to impelement these suggestions)

---
passage: TAGS
#region-us 
# Fake news classifier

The project is submitted as a part of application for Fatima Fellowship. The problem statement is stated as below:### Train a text classification model to detect fake news articles!
* Download the dataset here: URL
* Develop an NLP model for classification that uses a pretrained language model
* Finetune your model on the dataset, and generate an AUC curve of your model on the test set of your choice. 
* Upload the the model to the Hugging Face Hub, and add a link to your model below.
* *Answer the following question*: Look at some of the news articles that were classified incorrectly. Please explain what you might do to improve your model's performance on these news articles in the future (you do not need to impelement these suggestions)

---