readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: berturk-base-for-ordered-classification
    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. -->
  
  # berturk-base-for-ordered-classification
  
  This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4201
  - Accuracy: {'accuracy': 0.8592592592592593}
  - F1: {'f1': 0.87248322147651}
  - Precision: {'precision': 0.8333333333333334}
  - Recall: {'recall': 0.9154929577464789}
  
  It achieves the following results on the test set:
  - Accuracy: {'accuracy': 0.8667}
  - F1: {'f1': 0.8767}
  - Precision: {'precision': 0.8534}
  - Recall: {'recall': 0.9014}
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy                         | F1                         | Precision                         | Recall                         |
  |:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|:---------------------------------:|:------------------------------:|
  | 0.5255        | 1.0   | 40   | 0.4744          | {'accuracy': 0.7851851851851852} | {'f1': 0.8176100628930818} | {'precision': 0.7386363636363636} | {'recall': 0.9154929577464789} |
  | 0.3723        | 2.0   | 80   | 0.3938          | {'accuracy': 0.8666666666666667} | {'f1': 0.8767123287671234} | {'precision': 0.8533333333333334} | {'recall': 0.9014084507042254} |
  | 0.3527        | 3.0   | 120  | 0.4380          | {'accuracy': 0.8296296296296296} | {'f1': 0.8496732026143791} | {'precision': 0.7926829268292683} | {'recall': 0.9154929577464789} |
  | 0.303         | 4.0   | 160  | 0.4216          | {'accuracy': 0.8592592592592593} | {'f1': 0.87248322147651}   | {'precision': 0.8333333333333334} | {'recall': 0.9154929577464789} |
  | 0.2825        | 5.0   | 200  | 0.4201          | {'accuracy': 0.8592592592592593} | {'f1': 0.87248322147651}   | {'precision': 0.8333333333333334} | {'recall': 0.9154929577464789} |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  