readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: finetuned-bert-bounti
    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. -->
  
  # finetuned-bert-bounti
  
  This model is a fine-tuned version of [dbmdz/bert-base-turkish-128k-uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) on the BounTi Turkish Twitter sentiment dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1188
  - Accuracy: 0.7246
  - F1: 0.6845
  - Precision: 0.6892
  - Recall: 0.6806
  
  ## 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: 1e-05
  - train_batch_size: 24
  - eval_batch_size: 36
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 300
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 1.0974        | 0.02  | 5    | 1.0790          | 0.3756   | 0.3064 | 0.3255    | 0.3232 |
  | 1.1345        | 0.04  | 10   | 1.0784          | 0.3725   | 0.3037 | 0.3219    | 0.3197 |
  | 1.1441        | 0.06  | 15   | 1.0776          | 0.3772   | 0.3072 | 0.3250    | 0.3234 |
  | 1.122         | 0.08  | 20   | 1.0774          | 0.3787   | 0.3077 | 0.3244    | 0.3228 |
  | 1.1201        | 0.1   | 25   | 1.0776          | 0.3787   | 0.3047 | 0.3193    | 0.3216 |
  | 1.1489        | 0.13  | 30   | 1.0783          | 0.3787   | 0.3012 | 0.3120    | 0.3189 |
  | 1.0716        | 0.15  | 35   | 1.0783          | 0.3897   | 0.3093 | 0.3212    | 0.3282 |
  | 1.082         | 0.17  | 40   | 1.0767          | 0.3865   | 0.3060 | 0.3203    | 0.3238 |
  | 1.1113        | 0.19  | 45   | 1.0738          | 0.3897   | 0.3058 | 0.3219    | 0.3211 |
  | 1.0892        | 0.21  | 50   | 1.0715          | 0.4069   | 0.3290 | 0.3475    | 0.3374 |
  | 1.0913        | 0.23  | 55   | 1.0719          | 0.4178   | 0.3283 | 0.3398    | 0.3361 |
  | 1.1114        | 0.25  | 60   | 1.0694          | 0.4397   | 0.3479 | 0.3605    | 0.3538 |
  | 1.1129        | 0.27  | 65   | 1.0682          | 0.4491   | 0.3593 | 0.3731    | 0.3648 |
  | 1.1283        | 0.29  | 70   | 1.0671          | 0.4664   | 0.3719 | 0.3775    | 0.3780 |
  | 1.1267        | 0.31  | 75   | 1.0714          | 0.4507   | 0.3826 | 0.3834    | 0.3835 |
  | 1.1325        | 0.33  | 80   | 1.0762          | 0.4335   | 0.3909 | 0.3918    | 0.3954 |
  | 1.0919        | 0.36  | 85   | 1.0723          | 0.4335   | 0.3930 | 0.3937    | 0.3982 |
  | 1.0545        | 0.38  | 90   | 1.0694          | 0.4507   | 0.4161 | 0.4180    | 0.4279 |
  | 1.1121        | 0.4   | 95   | 1.0698          | 0.4491   | 0.4151 | 0.4280    | 0.4324 |
  | 1.0675        | 0.42  | 100  | 1.0711          | 0.4382   | 0.4005 | 0.4349    | 0.4494 |
  | 1.0954        | 0.44  | 105  | 1.0720          | 0.4085   | 0.3690 | 0.4233    | 0.4326 |
  | 1.1087        | 0.46  | 110  | 1.0562          | 0.4820   | 0.4463 | 0.4762    | 0.4841 |
  | 1.0669        | 0.48  | 115  | 1.0459          | 0.5086   | 0.4746 | 0.4844    | 0.4997 |
  | 1.0529        | 0.5   | 120  | 1.0364          | 0.5243   | 0.4935 | 0.4946    | 0.5119 |
  | 1.0348        | 0.52  | 125  | 1.0248          | 0.5321   | 0.4953 | 0.4977    | 0.5067 |
  | 1.0454        | 0.54  | 130  | 1.0169          | 0.5415   | 0.5089 | 0.5084    | 0.5232 |
  | 1.0366        | 0.56  | 135  | 1.0071          | 0.5493   | 0.5176 | 0.5156    | 0.5344 |
  | 1.0197        | 0.59  | 140  | 1.0010          | 0.5446   | 0.5132 | 0.5150    | 0.5350 |
  | 1.0459        | 0.61  | 145  | 0.9966          | 0.5399   | 0.5094 | 0.5184    | 0.5383 |
  | 1.0059        | 0.63  | 150  | 1.0011          | 0.5477   | 0.5222 | 0.5394    | 0.5617 |
  | 0.9455        | 0.65  | 155  | 0.9898          | 0.5399   | 0.5173 | 0.5390    | 0.5583 |
  | 0.9732        | 0.67  | 160  | 0.9750          | 0.5477   | 0.5207 | 0.5406    | 0.5601 |
  | 1.0215        | 0.69  | 165  | 0.9494          | 0.5790   | 0.5495 | 0.5511    | 0.5759 |
  | 0.99          | 0.71  | 170  | 0.9331          | 0.5696   | 0.5355 | 0.5372    | 0.5500 |
  | 1.0102        | 0.73  | 175  | 0.9284          | 0.5759   | 0.5425 | 0.5488    | 0.5567 |
  | 0.9633        | 0.75  | 180  | 0.9313          | 0.5837   | 0.5571 | 0.5726    | 0.5758 |
  | 0.9388        | 0.77  | 185  | 0.9262          | 0.5869   | 0.5625 | 0.5830    | 0.5817 |
  | 0.9606        | 0.79  | 190  | 0.9140          | 0.5915   | 0.5638 | 0.5728    | 0.5835 |
  | 0.969         | 0.82  | 195  | 0.9170          | 0.5978   | 0.5712 | 0.5769    | 0.5964 |
  | 0.8779        | 0