---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: model_TrainTestSplit_berturk_v2_24Feb
  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_TrainTestSplit_berturk_v2_24Feb

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: 0.0003
- Precision: 0.9999
- Recall: 0.9999
- F1: 0.9999
- Accuracy: 0.9999

## 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   | 196  | 0.0058          | 0.9982    | 0.9980 | 0.9981 | 0.9986   |
| No log        | 2.0   | 392  | 0.0042          | 0.9987    | 0.9986 | 0.9986 | 0.9990   |
| 0.0132        | 3.0   | 588  | 0.0042          | 0.9985    | 0.9988 | 0.9986 | 0.9990   |
| 0.0132        | 4.0   | 784  | 0.0022          | 0.9993    | 0.9992 | 0.9992 | 0.9993   |
| 0.0132        | 5.0   | 980  | 0.0020          | 0.9993    | 0.9992 | 0.9993 | 0.9995   |
| 0.0069        | 6.0   | 1176 | 0.0013          | 0.9994    | 0.9994 | 0.9994 | 0.9995   |
| 0.0069        | 7.0   | 1372 | 0.0008          | 0.9997    | 0.9997 | 0.9997 | 0.9998   |
| 0.0035        | 8.0   | 1568 | 0.0008          | 0.9997    | 0.9997 | 0.9997 | 0.9998   |
| 0.0035        | 9.0   | 1764 | 0.0006          | 0.9996    | 0.9997 | 0.9996 | 0.9997   |
| 0.0035        | 10.0  | 1960 | 0.0004          | 0.9998    | 0.9999 | 0.9998 | 0.9999   |
| 0.0019        | 11.0  | 2156 | 0.0003          | 0.9999    | 0.9999 | 0.9999 | 0.9999   |
| 0.0019        | 12.0  | 2352 | 0.0003          | 0.9999    | 0.9999 | 0.9999 | 0.9999   |
| 0.0012        | 13.0  | 2548 | 0.0004          | 0.9999    | 0.9999 | 0.9999 | 0.9999   |
| 0.0012        | 14.0  | 2744 | 0.0003          | 0.9999    | 0.9999 | 0.9999 | 0.9999   |
| 0.0012        | 15.0  | 2940 | 0.0003          | 0.9999    | 0.9999 | 0.9999 | 0.9999   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2