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. -->
  
  # distilbert-base-turkish-cased_allnli_tr
  
  This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6481
  - Accuracy: 0.7381
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.94          | 0.03  | 1000  | 0.9074          | 0.5813   |
  | 0.8102        | 0.07  | 2000  | 0.8802          | 0.5949   |
  | 0.7737        | 0.1   | 3000  | 0.8491          | 0.6155   |
  | 0.7576        | 0.14  | 4000  | 0.8283          | 0.6261   |
  | 0.7286        | 0.17  | 5000  | 0.8150          | 0.6362   |
  | 0.7162        | 0.2   | 6000  | 0.7998          | 0.6400   |
  | 0.7092        | 0.24  | 7000  | 0.7830          | 0.6565   |
  | 0.6962        | 0.27  | 8000  | 0.7653          | 0.6629   |
  | 0.6876        | 0.31  | 9000  | 0.7630          | 0.6687   |
  | 0.6778        | 0.34  | 10000 | 0.7475          | 0.6739   |
  | 0.6737        | 0.37  | 11000 | 0.7495          | 0.6781   |
  | 0.6712        | 0.41  | 12000 | 0.7350          | 0.6826   |
  | 0.6559        | 0.44  | 13000 | 0.7274          | 0.6897   |
  | 0.6493        | 0.48  | 14000 | 0.7248          | 0.6902   |
  | 0.6483        | 0.51  | 15000 | 0.7263          | 0.6858   |
  | 0.6445        | 0.54  | 16000 | 0.7070          | 0.6978   |
  | 0.6467        | 0.58  | 17000 | 0.7083          | 0.6981   |
  | 0.6332        | 0.61  | 18000 | 0.6996          | 0.7004   |
  | 0.6288        | 0.65  | 19000 | 0.6979          | 0.6978   |
  | 0.6308        | 0.68  | 20000 | 0.6912          | 0.7040   |
  | 0.622         | 0.71  | 21000 | 0.6904          | 0.7092   |
  | 0.615         | 0.75  | 22000 | 0.6872          | 0.7094   |
  | 0.6186        | 0.78  | 23000 | 0.6877          | 0.7075   |
  | 0.6183        | 0.82  | 24000 | 0.6818          | 0.7111   |
  | 0.6115        | 0.85  | 25000 | 0.6856          | 0.7122   |
  | 0.608         | 0.88  | 26000 | 0.6697          | 0.7179   |
  | 0.6071        | 0.92  | 27000 | 0.6727          | 0.7181   |
  | 0.601         | 0.95  | 28000 | 0.6798          | 0.7118   |
  | 0.6018        | 0.99  | 29000 | 0.6854          | 0.7071   |
  | 0.5762        | 1.02  | 30000 | 0.6697          | 0.7214   |
  | 0.5507        | 1.05  | 31000 | 0.6710          | 0.7185   |
  | 0.5575        | 1.09  | 32000 | 0.6709          | 0.7226   |
  | 0.5493        | 1.12  | 33000 | 0.6659          | 0.7191   |
  | 0.5464        | 1.15  | 34000 | 0.6709          | 0.7232   |
  | 0.5595        | 1.19  | 35000 | 0.6642          | 0.7220   |
  | 0.5446        | 1.22  | 36000 | 0.6709          | 0.7202   |
  | 0.5524        | 1.26  | 37000 | 0.6751          | 0.7148   |
  | 0.5473        | 1.29  | 38000 | 0.6642          | 0.7209   |
  | 0.5477        | 1.32  | 39000 | 0.6662          | 0.7223   |
  | 0.5522        | 1.36  | 40000 | 0.6586          | 0.7227   |
  | 0.5406        | 1.39  | 41000 | 0.6602          | 0.7258   |
  | 0.54          | 1.43  | 42000 | 0.6564          | 0.7273   |
  | 0.5458        | 1.46  | 43000 | 0.6780          | 0.7213   |
  | 0.5448        | 1.49  | 44000 | 0.6561          | 0.7235   |
  | 0.5418        | 1.53  | 45000 | 0.6600          | 0.7253   |
  | 0.5408        | 1.56  | 46000 | 0.6616          | 0.7274   |
  | 0.5451        | 1.6   | 47000 | 0.6557          | 0.7283   |
  | 0.5385        | 1.63  | 48000 | 0.6583          | 0.7295   |
  | 0.5261        | 1.66  | 49000 | 0.6468          | 0.7325   |
  | 0.5364        | 1.7   | 50000 | 0.6447          | 0.7329   |
  | 0.5294        | 1.73  | 51000 | 0.6429          | 0.7320   |
  | 0.5332        | 1.77  | 52000 | 0.6508          | 0.7272   |
  | 0.5274        | 1.8   | 53000 | 0.6492          | 0.7326   |
  | 0.5286        | 1.83  | 54000 | 0.6470          | 0.7318   |
  | 0.5359        | 1.87  | 55000 | 0.6393          | 0.7354   |
  | 0.5366        | 1.9   | 56000 | 0.6445          | 0.7367   |
  | 0.5296        | 1.94  | 57000 | 0.