readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: model_from_berturk_1401_v4
    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. -->
  
  # model_from_berturk_1401_v4
  
  This model is a fine-tuned version of [Buseak/model_from_berturk_1401_v3](https://huggingface.co/Buseak/model_from_berturk_1401_v3) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0070
  - Precision: 0.9983
  - Recall: 0.9981
  - F1: 0.9982
  - Accuracy: 0.9985
  
  ## 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: 2e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 244  | 0.1500          | 0.9425    | 0.9362 | 0.9393 | 0.9575   |
  | No log        | 2.0   | 488  | 0.1115          | 0.9570    | 0.9519 | 0.9544 | 0.9681   |
  | 0.1776        | 3.0   | 732  | 0.0801          | 0.9700    | 0.9665 | 0.9683 | 0.9774   |
  | 0.1776        | 4.0   | 976  | 0.0577          | 0.9792    | 0.9772 | 0.9782 | 0.9841   |
  | 0.1024        | 5.0   | 1220 | 0.0490          | 0.9818    | 0.9809 | 0.9814 | 0.9866   |
  | 0.1024        | 6.0   | 1464 | 0.0346          | 0.9882    | 0.9864 | 0.9873 | 0.9907   |
  | 0.0901        | 7.0   | 1708 | 0.0254          | 0.9920    | 0.9910 | 0.9915 | 0.9935   |
  | 0.0901        | 8.0   | 1952 | 0.0205          | 0.9935    | 0.9922 | 0.9928 | 0.9947   |
  | 0.0617        | 9.0   | 2196 | 0.0157          | 0.9954    | 0.9947 | 0.9951 | 0.9963   |
  | 0.0617        | 10.0  | 2440 | 0.0126          | 0.9965    | 0.9959 | 0.9962 | 0.9970   |
  | 0.0438        | 11.0  | 2684 | 0.0110          | 0.9969    | 0.9965 | 0.9967 | 0.9975   |
  | 0.0438        | 12.0  | 2928 | 0.0091          | 0.9976    | 0.9974 | 0.9975 | 0.9980   |
  | 0.033         | 13.0  | 3172 | 0.0080          | 0.9979    | 0.9977 | 0.9978 | 0.9982   |
  | 0.033         | 14.0  | 3416 | 0.0073          | 0.9982    | 0.9979 | 0.9981 | 0.9984   |
  | 0.0264        | 15.0  | 3660 | 0.0070          | 0.9983    | 0.9981 | 0.9982 | 0.9985   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  