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berturk-base-for-ordered-classification
This model is a fine-tuned version of dbmdz/bert-base-turkish-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4201
- Accuracy: {'accuracy': 0.8592592592592593}
- F1: {'f1': 0.87248322147651}
- Precision: {'precision': 0.8333333333333334}
- Recall: {'recall': 0.9154929577464789}
It achieves the following results on the test set:
- Accuracy: {'accuracy': 0.8667}
- F1: {'f1': 0.8767}
- Precision: {'precision': 0.8534}
- Recall: {'recall': 0.9014}
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.5255 | 1.0 | 40 | 0.4744 | {'accuracy': 0.7851851851851852} | {'f1': 0.8176100628930818} | {'precision': 0.7386363636363636} | {'recall': 0.9154929577464789} |
| 0.3723 | 2.0 | 80 | 0.3938 | {'accuracy': 0.8666666666666667} | {'f1': 0.8767123287671234} | {'precision': 0.8533333333333334} | {'recall': 0.9014084507042254} |
| 0.3527 | 3.0 | 120 | 0.4380 | {'accuracy': 0.8296296296296296} | {'f1': 0.8496732026143791} | {'precision': 0.7926829268292683} | {'recall': 0.9154929577464789} |
| 0.303 | 4.0 | 160 | 0.4216 | {'accuracy': 0.8592592592592593} | {'f1': 0.87248322147651} | {'precision': 0.8333333333333334} | {'recall': 0.9154929577464789} |
| 0.2825 | 5.0 | 200 | 0.4201 | {'accuracy': 0.8592592592592593} | {'f1': 0.87248322147651} | {'precision': 0.8333333333333334} | {'recall': 0.9154929577464789} |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
