---
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