readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: fatimah_fake_news_bert
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # fatimah_fake_news_bert
  
  This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on [Fake and real dataset on kaggle ]([distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english))
  It achieves the following results on the evaluation set:
  - Loss: 0.0010
  - Accuracy: 0.9998
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 10
  - eval_batch_size: 20
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 100
  - num_epochs: 1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.3298        | 0.06  | 200  | 0.0094          | 0.9987   |
  | 0.0087        | 0.11  | 400  | 0.0091          | 0.9988   |
  | 0.0126        | 0.17  | 600  | 0.0132          | 0.9965   |
  | 0.0081        | 0.22  | 800  | 0.0100          | 0.9987   |
  | 0.0132        | 0.28  | 1000 | 0.0086          | 0.9990   |
  | 0.0131        | 0.33  | 1200 | 0.0070          | 0.9986   |
  | 0.0086        | 0.39  | 1400 | 0.0079          | 0.9990   |
  | 0.0041        | 0.45  | 1600 | 0.0057          | 0.9991   |
  | 0.0069        | 0.5   | 1800 | 0.0083          | 0.9989   |
  | 0.0052        | 0.56  | 2000 | 0.0043          | 0.9993   |
  | 0.0           | 0.61  | 2200 | 0.0047          | 0.9993   |
  | 0.003         | 0.67  | 2400 | 0.0052          | 0.9994   |
  | 0.0126        | 0.72  | 2600 | 0.0028          | 0.9997   |
  | 0.0047        | 0.78  | 2800 | 0.0018          | 0.9996   |
  | 0.0           | 0.84  | 3000 | 0.0027          | 0.9996   |
  | 0.0001        | 0.89  | 3200 | 0.0029          | 0.9996   |
  | 0.0079        | 0.95  | 3400 | 0.0010          | 0.9998   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  