readme: ---
  language:
  - tr
  tags:
  - zero-shot-classification
  - nli
  - pytorch
  pipeline_tag: zero-shot-classification
  license: apache-2.0
  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. -->
  
  # convbert-base-turkish-mc4-cased_allnli_tr
  
  This model is a fine-tuned version of [dbmdz/convbert-base-turkish-mc4-cased](https://huggingface.co/dbmdz/convbert-base-turkish-mc4-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5541
  - Accuracy: 0.8111
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.7338        | 0.03  | 1000  | 0.6722          | 0.7236   |
  | 0.603         | 0.07  | 2000  | 0.6465          | 0.7399   |
  | 0.5605        | 0.1   | 3000  | 0.5801          | 0.7728   |
  | 0.55          | 0.14  | 4000  | 0.5994          | 0.7626   |
  | 0.529         | 0.17  | 5000  | 0.5720          | 0.7697   |
  | 0.5196        | 0.2   | 6000  | 0.5692          | 0.7769   |
  | 0.5117        | 0.24  | 7000  | 0.5725          | 0.7785   |
  | 0.5044        | 0.27  | 8000  | 0.5532          | 0.7787   |
  | 0.5016        | 0.31  | 9000  | 0.5546          | 0.7812   |
  | 0.5031        | 0.34  | 10000 | 0.5461          | 0.7870   |
  | 0.4949        | 0.37  | 11000 | 0.5725          | 0.7826   |
  | 0.4894        | 0.41  | 12000 | 0.5419          | 0.7933   |
  | 0.4796        | 0.44  | 13000 | 0.5278          | 0.7914   |
  | 0.4795        | 0.48  | 14000 | 0.5193          | 0.7953   |
  | 0.4713        | 0.51  | 15000 | 0.5534          | 0.7771   |
  | 0.4738        | 0.54  | 16000 | 0.5098          | 0.8039   |
  | 0.481         | 0.58  | 17000 | 0.5244          | 0.7958   |
  | 0.4634        | 0.61  | 18000 | 0.5215          | 0.7972   |
  | 0.465         | 0.65  | 19000 | 0.5129          | 0.7985   |
  | 0.4624        | 0.68  | 20000 | 0.5062          | 0.8047   |
  | 0.4597        | 0.71  | 21000 | 0.5114          | 0.8029   |
  | 0.4571        | 0.75  | 22000 | 0.5070          | 0.8073   |
  | 0.4602        | 0.78  | 23000 | 0.5115          | 0.7993   |
  | 0.4552        | 0.82  | 24000 | 0.5085          | 0.8052   |
  | 0.4538        | 0.85  | 25000 | 0.5118          | 0.7974   |
  | 0.4517        | 0.88  | 26000 | 0.5036          | 0.8044   |
  | 0.4517        | 0.92  | 27000 | 0.4930          | 0.8062   |
  | 0.4413        | 0.95  | 28000 | 0.5307          | 0.7964   |
  | 0.4483        | 0.99  | 29000 | 0.5195          | 0.7938   |
  | 0.4036        | 1.02  | 30000 | 0.5238          | 0.8029   |
  | 0.3724        | 1.05  | 31000 | 0.5125          | 0.8082   |
  | 0.3777        | 1.09  | 32000 | 0.5099          | 0.8075   |
  | 0.3753        | 1.12  | 33000 | 0.5172          | 0.8053   |
  | 0.367         | 1.15  | 34000 | 0.5188          | 0.8053   |
  | 0.3819        | 1.19  | 35000 | 0.5218          | 0.8046   |
  | 0.363         | 1.22  | 36000 | 0.5202          | 0.7993   |
  | 0.3794        | 1.26  | 37000 | 0.5240          | 0.8048   |
  | 0.3749        | 1.29  | 38000 | 0.5026          | 0.8054   |
  | 0.367         | 1.32  | 39000 | 0.5198          | 0.8075   |
  | 0.3759        | 1.36  | 40000 | 0.5298          | 0.7993   |
  | 0.3701        | 1.39  | 41000 | 0.5072          | 0.8091   |
  | 0.3742        | 1.43  | 42000 | 0.5071          | 0.8098   |
  | 0.3706        | 1.46  | 43000 | 0.5317          | 0.8037   |
  | 0.3716        | 1.49  | 44000 | 0.5034          | 0.8052   |
  | 0.3717        | 1.53  | 45000 | 0.5258          | 0.8012   |
  | 0.3714        | 1.56  | 46000 | 0.5195          | 0.8050   |
  | 0.3781        | 1.6   | 47000 | 0.5004          | 0.8104   |
  | 0.3725        | 1.63  | 48000 | 0.5124          | 0.8113   |
  | 0.3624        | 1.66  | 49000 | 0.5040          | 0.8094   |
  | 0.3657        | 1.7   | 50000 | 0.4979          | 0.8111   |
  | 0.3669        | 1.73  | 51000 | 0.4968          | 0.8100   |
  | 0.3636        | 1.77  | 52000 | 0.5075          | 0.8079   |
  | 0.36          | 1.8   | 53000 | 0.4985          | 0.8110   |
  | 0.3624        | 1.83  | 54000 | 0.5125          | 0.8070   |
  | 0.366         | 1.87  | 55000 | 0.4918          | 0.8117   |
  | 0.3655        | 1.9   | 56000 | 0.5051          | 0.8109   |
  | 0.3609        | 1.94  | 5700