readme: ---
  language:
  - tr
  tags:
  - zero-shot-classification
  - nli
  - pytorch
  pipeline_tag: zero-shot-classification
  license: mit
  datasets:
  - nli_tr
  metrics:
  - accuracy
  widget:
  - text: "Dolar yükselmeye devam ediyor."
    candidate_labels: "ekonomi, siyaset, spor"
  - text: "Senaryo çok saçmaydı, beğendim diyemem."
    candidate_labels: "olumlu, olumsuz"
  ---
  
  <!-- 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. -->
  
  # bert-base-turkish-cased_allnli_tr
  
  This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5771
  - Accuracy: 0.7978
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.8559        | 0.03  | 1000  | 0.7577          | 0.6798   |
  | 0.6612        | 0.07  | 2000  | 0.7263          | 0.6958   |
  | 0.6115        | 0.1   | 3000  | 0.6431          | 0.7364   |
  | 0.5916        | 0.14  | 4000  | 0.6347          | 0.7407   |
  | 0.5719        | 0.17  | 5000  | 0.6317          | 0.7483   |
  | 0.5575        | 0.2   | 6000  | 0.6034          | 0.7544   |
  | 0.5521        | 0.24  | 7000  | 0.6148          | 0.7568   |
  | 0.5393        | 0.27  | 8000  | 0.5931          | 0.7610   |
  | 0.5382        | 0.31  | 9000  | 0.5866          | 0.7665   |
  | 0.5306        | 0.34  | 10000 | 0.5881          | 0.7594   |
  | 0.5295        | 0.37  | 11000 | 0.6120          | 0.7632   |
  | 0.5225        | 0.41  | 12000 | 0.5620          | 0.7759   |
  | 0.5112        | 0.44  | 13000 | 0.5641          | 0.7769   |
  | 0.5133        | 0.48  | 14000 | 0.5571          | 0.7798   |
  | 0.5023        | 0.51  | 15000 | 0.5719          | 0.7722   |
  | 0.5017        | 0.54  | 16000 | 0.5482          | 0.7844   |
  | 0.5111        | 0.58  | 17000 | 0.5503          | 0.7800   |
  | 0.4929        | 0.61  | 18000 | 0.5502          | 0.7836   |
  | 0.4923        | 0.65  | 19000 | 0.5424          | 0.7843   |
  | 0.4894        | 0.68  | 20000 | 0.5417          | 0.7851   |
  | 0.4877        | 0.71  | 21000 | 0.5514          | 0.7841   |
  | 0.4818        | 0.75  | 22000 | 0.5494          | 0.7848   |
  | 0.4898        | 0.78  | 23000 | 0.5450          | 0.7859   |
  | 0.4823        | 0.82  | 24000 | 0.5417          | 0.7878   |
  | 0.4806        | 0.85  | 25000 | 0.5354          | 0.7875   |
  | 0.4779        | 0.88  | 26000 | 0.5338          | 0.7848   |
  | 0.4744        | 0.92  | 27000 | 0.5277          | 0.7934   |
  | 0.4678        | 0.95  | 28000 | 0.5507          | 0.7871   |
  | 0.4727        | 0.99  | 29000 | 0.5603          | 0.7789   |
  | 0.4243        | 1.02  | 30000 | 0.5626          | 0.7894   |
  | 0.3955        | 1.05  | 31000 | 0.5324          | 0.7939   |
  | 0.4022        | 1.09  | 32000 | 0.5322          | 0.7925   |
  | 0.3976        | 1.12  | 33000 | 0.5450          | 0.7920   |
  | 0.3913        | 1.15  | 34000 | 0.5464          | 0.7948   |
  | 0.406         | 1.19  | 35000 | 0.5406          | 0.7958   |
  | 0.3875        | 1.22  | 36000 | 0.5489          | 0.7878   |
  | 0.4024        | 1.26  | 37000 | 0.5427          | 0.7925   |
  | 0.3988        | 1.29  | 38000 | 0.5335          | 0.7904   |
  | 0.393         | 1.32  | 39000 | 0.5415          | 0.7923   |
  | 0.3988        | 1.36  | 40000 | 0.5385          | 0.7962   |
  | 0.3912        | 1.39  | 41000 | 0.5383          | 0.7950   |
  | 0.3949        | 1.43  | 42000 | 0.5415          | 0.7931   |
  | 0.3902        | 1.46  | 43000 | 0.5438          | 0.7893   |
  | 0.3948        | 1.49  | 44000 | 0.5348          | 0.7906   |
  | 0.3921        | 1.53  | 45000 | 0.5361          | 0.7890   |
  | 0.3944        | 1.56  | 46000 | 0.5419          | 0.7953   |
  | 0.3959        | 1.6   | 47000 | 0.5402          | 0.7967   |
  | 0.3926        | 1.63  | 48000 | 0.5429          | 0.7925   |
  | 0.3854        | 1.66  | 49000 | 0.5346          | 0.7959   |
  | 0.3864        | 1.7   | 50000 | 0.5241          | 0.7979   |
  | 0.385         | 1.73  | 51000 | 0.5149          | 0.8002   |
  | 0.3871        | 1.77  | 52000 | 0.5325          | 0.8002   |
  | 0.3819        | 1.8   | 53000 | 0.5332          | 0.8022   |
  | 0.384         | 1.83  | 54000 | 0.5419          | 0.7873   |
  | 0.3899        | 1.87  | 55000 | 0.5225          | 0.7974   |
  | 0.3894        | 1.9   | 56000 | 0.5358          | 0.7977   |
  | 0.3838        | 1.94  | 57000 | 0.5264          | 0.7988   