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-multilingual-cased_allnli_tr
  
  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6144
  - Accuracy: 0.7662
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.8623        | 0.03  | 1000  | 0.9076          | 0.5917   |
  | 0.7528        | 0.07  | 2000  | 0.8587          | 0.6119   |
  | 0.7074        | 0.1   | 3000  | 0.7867          | 0.6647   |
  | 0.6949        | 0.14  | 4000  | 0.7474          | 0.6772   |
  | 0.6681        | 0.17  | 5000  | 0.7661          | 0.6814   |
  | 0.6597        | 0.2   | 6000  | 0.7264          | 0.6943   |
  | 0.6495        | 0.24  | 7000  | 0.7841          | 0.6781   |
  | 0.6323        | 0.27  | 8000  | 0.7256          | 0.6952   |
  | 0.6308        | 0.31  | 9000  | 0.7319          | 0.6958   |
  | 0.6254        | 0.34  | 10000 | 0.7054          | 0.7004   |
  | 0.6233        | 0.37  | 11000 | 0.7069          | 0.7085   |
  | 0.6165        | 0.41  | 12000 | 0.6880          | 0.7181   |
  | 0.6033        | 0.44  | 13000 | 0.6844          | 0.7197   |
  | 0.6014        | 0.48  | 14000 | 0.6753          | 0.7129   |
  | 0.5947        | 0.51  | 15000 | 0.7000          | 0.7039   |
  | 0.5965        | 0.54  | 16000 | 0.6708          | 0.7263   |
  | 0.5979        | 0.58  | 17000 | 0.6562          | 0.7285   |
  | 0.5787        | 0.61  | 18000 | 0.6554          | 0.7297   |
  | 0.58          | 0.65  | 19000 | 0.6544          | 0.7315   |
  | 0.574         | 0.68  | 20000 | 0.6549          | 0.7339   |
  | 0.5751        | 0.71  | 21000 | 0.6545          | 0.7289   |
  | 0.5659        | 0.75  | 22000 | 0.6467          | 0.7371   |
  | 0.5732        | 0.78  | 23000 | 0.6448          | 0.7362   |
  | 0.5637        | 0.82  | 24000 | 0.6520          | 0.7355   |
  | 0.5648        | 0.85  | 25000 | 0.6412          | 0.7345   |
  | 0.5622        | 0.88  | 26000 | 0.6350          | 0.7358   |
  | 0.5579        | 0.92  | 27000 | 0.6347          | 0.7393   |
  | 0.5518        | 0.95  | 28000 | 0.6417          | 0.7392   |
  | 0.5547        | 0.99  | 29000 | 0.6321          | 0.7437   |
  | 0.524         | 1.02  | 30000 | 0.6430          | 0.7412   |
  | 0.4982        | 1.05  | 31000 | 0.6253          | 0.7458   |
  | 0.5002        | 1.09  | 32000 | 0.6316          | 0.7418   |
  | 0.4993        | 1.12  | 33000 | 0.6197          | 0.7487   |
  | 0.4963        | 1.15  | 34000 | 0.6307          | 0.7462   |
  | 0.504         | 1.19  | 35000 | 0.6272          | 0.7480   |
  | 0.4922        | 1.22  | 36000 | 0.6410          | 0.7433   |
  | 0.5016        | 1.26  | 37000 | 0.6295          | 0.7461   |
  | 0.4957        | 1.29  | 38000 | 0.6183          | 0.7506   |
  | 0.4883        | 1.32  | 39000 | 0.6261          | 0.7502   |
  | 0.4985        | 1.36  | 40000 | 0.6315          | 0.7496   |
  | 0.4885        | 1.39  | 41000 | 0.6189          | 0.7529   |
  | 0.4909        | 1.43  | 42000 | 0.6189          | 0.7473   |
  | 0.4894        | 1.46  | 43000 | 0.6314          | 0.7433   |
  | 0.4912        | 1.49  | 44000 | 0.6184          | 0.7446   |
  | 0.4851        | 1.53  | 45000 | 0.6258          | 0.7461   |
  | 0.4879        | 1.56  | 46000 | 0.6286          | 0.7480   |
  | 0.4907        | 1.6   | 47000 | 0.6196          | 0.7512   |
  | 0.4884        | 1.63  | 48000 | 0.6157          | 0.7526   |
  | 0.4755        | 1.66  | 49000 | 0.6056          | 0.7591   |
  | 0.4811        | 1.7   | 50000 | 0.5977          | 0.7582   |
  | 0.4787        | 1.73  | 51000 | 0.5915          | 0.7621   |
  | 0.4779        | 1.77  | 52000 | 0.6014          | 0.7583   |
  | 0.4767        | 1.8   | 53000 | 0.6041          | 0.7623   |
  | 0.4737        | 1.83  | 54000 | 0.6093          | 0.7563   |
  | 0.4836        | 1.87  | 55000 | 0.6001          | 0.7568   |
  | 0.4765        | 1.9   | 56000 | 0.6109          | 0.7601   |
  | 0.4776        | 1.94  | 57000 | 0.6046          | 0.7599