readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: roberta-news-classifier
    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. -->
  
  # roberta-news-classifier
  
  This model is a fine-tuned version of [russellc/roberta-news-classifier](https://huggingface.co/russellc/roberta-news-classifier) on the custom(Kaggle) dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1043
  - Accuracy: 0.9786
  - F1: 0.9786
  - Precision: 0.9786
  - Recall: 0.9786
  
  ## 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: 1e-05
  - train_batch_size: 32
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.1327        | 1.0   | 123  | 0.1043          | 0.9786   | 0.9786 | 0.9786    | 0.9786 |
  | 0.1103        | 2.0   | 246  | 0.1157          | 0.9735   | 0.9735 | 0.9735    | 0.9735 |
  | 0.102         | 3.0   | 369  | 0.1104          | 0.9735   | 0.9735 | 0.9735    | 0.9735 |
  | 0.0825        | 4.0   | 492  | 0.1271          | 0.9714   | 0.9714 | 0.9714    | 0.9714 |
  | 0.055         | 5.0   | 615  | 0.1296          | 0.9724   | 0.9724 | 0.9724    | 0.9724 |
  
  ### Evaluation results
  
  ***** Running Prediction *****  
    Num examples = 980  
    Batch size = 64  
                
                precision    recall  f1-score   support
  
        dunya        0.99      0.96      0.97       147  
      ekonomi        0.96      0.96      0.96       141  
       kultur        0.97      0.99      0.98       142  
       saglik        0.99      0.98      0.98       148  
       siyaset       0.98      0.98      0.98       134  
       spor          1.00      1.00      1.00       139  
       teknoloji     0.96      0.98      0.97       129  
       accuracy      --        --        0.98       980
       macro avg     0.98      0.98      0.98       980  
       weighted avg  0.98      0.98      0.98       980 
  
         
  
    
  
     
    
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  