readme: # Description
  This model is Part of the NLP assignment for Fatima Fellowship.
  
  This model is a fine-tuned version of 'bert-base-uncased' on the below dataset: [Fake News Dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset). 
  
  It achieves the following results on the evaluation set:
  - Accuracy: 0.995
  - Precision: 0.995
  - Recall: 0.995
  - F_score: 0.995
  
  # Labels
  
  Fake news: 0
  
  Real news: 1
  
  # Using this model in your code
  To use this model, first download it from the hugging face website:
  
  
  ```python
  
  import transformers
  from transformers import AutoTokenizer
  class Fake_Real_Model_Arch_test(transformers.PreTrainedModel):
      def __init__(self,bert):
          super(Fake_Real_Model_Arch_test,self).__init__(config=AutoConfig.from_pretrained(MODEL_NAME))
          
          self.bert = bert        
          num_classes = 2  # number of targets to predict
          embedding_dim = 768   # length of embedding dim        
          self.fc1 = nn.Linear(embedding_dim, num_classes)
          self.softmax = nn.Softmax()
  
      def forward(self, text_id, text_mask):
          outputs= self.bert(text_id, attention_mask=text_mask)        
          outputs = outputs[1]  # get hidden layers       
          logit = self.fc1(outputs)
          return self.softmax(logit)
          
  tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
  model =  Fake_Real_Model_Arch_test(AutoModel.from_pretrained("rematchka/Bert_fake_news_detection"))
  
  ```
  
  
  