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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: model_from_berturk_Feb_5_TrainTestSplit
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. -->
# model_from_berturk_Feb_5_TrainTestSplit
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3125
- Precision: 0.9120
- Recall: 0.9126
- F1: 0.9123
- Accuracy: 0.9376
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 185 | 0.2333 | 0.9065 | 0.9066 | 0.9066 | 0.9343 |
| No log | 2.0 | 370 | 0.2115 | 0.9122 | 0.9143 | 0.9133 | 0.9389 |
| 0.3861 | 3.0 | 555 | 0.2049 | 0.9185 | 0.9175 | 0.9180 | 0.9423 |
| 0.3861 | 4.0 | 740 | 0.2073 | 0.9183 | 0.9185 | 0.9184 | 0.9420 |
| 0.3861 | 5.0 | 925 | 0.2174 | 0.9150 | 0.9155 | 0.9153 | 0.9397 |
| 0.1487 | 6.0 | 1110 | 0.2227 | 0.9177 | 0.9185 | 0.9181 | 0.9415 |
| 0.1487 | 7.0 | 1295 | 0.2399 | 0.9149 | 0.9160 | 0.9155 | 0.9396 |
| 0.1487 | 8.0 | 1480 | 0.2504 | 0.9158 | 0.9163 | 0.9160 | 0.9400 |
| 0.0942 | 9.0 | 1665 | 0.2692 | 0.9141 | 0.9152 | 0.9146 | 0.9392 |
| 0.0942 | 10.0 | 1850 | 0.2782 | 0.9130 | 0.9153 | 0.9141 | 0.9388 |
| 0.0589 | 11.0 | 2035 | 0.2908 | 0.9131 | 0.9144 | 0.9138 | 0.9388 |
| 0.0589 | 12.0 | 2220 | 0.2940 | 0.9121 | 0.9136 | 0.9128 | 0.9377 |
| 0.0589 | 13.0 | 2405 | 0.3068 | 0.9117 | 0.9130 | 0.9123 | 0.9376 |
| 0.0407 | 14.0 | 2590 | 0.3107 | 0.9132 | 0.9148 | 0.9140 | 0.9387 |
| 0.0407 | 15.0 | 2775 | 0.3125 | 0.9120 | 0.9126 | 0.9123 | 0.9376 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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