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# model_from_berturk_upos_22Jan_val_on_dev_v1

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: 0.4600
- Precision: 0.8894
- Recall: 0.8931
- F1: 0.8913
- Accuracy: 0.9217

## 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   | 170  | 0.3259          | 0.8864    | 0.8851 | 0.8857 | 0.9170   |
| No log        | 2.0   | 340  | 0.2976          | 0.8991    | 0.9005 | 0.8998 | 0.9271   |
| 0.3859        | 3.0   | 510  | 0.2988          | 0.8970    | 0.8981 | 0.8976 | 0.9257   |
| 0.3859        | 4.0   | 680  | 0.3131          | 0.8977    | 0.8984 | 0.8981 | 0.9267   |
| 0.3859        | 5.0   | 850  | 0.3291          | 0.8943    | 0.8974 | 0.8958 | 0.9246   |
| 0.1338        | 6.0   | 1020 | 0.3298          | 0.8974    | 0.8984 | 0.8979 | 0.9253   |
| 0.1338        | 7.0   | 1190 | 0.3538          | 0.8908    | 0.8950 | 0.8929 | 0.9222   |
| 0.1338        | 8.0   | 1360 | 0.3879          | 0.8903    | 0.8921 | 0.8912 | 0.9220   |
| 0.0798        | 9.0   | 1530 | 0.3852          | 0.8929    | 0.8933 | 0.8931 | 0.9228   |
| 0.0798        | 10.0  | 1700 | 0.4111          | 0.8873    | 0.8896 | 0.8884 | 0.9196   |
| 0.0798        | 11.0  | 1870 | 0.4254          | 0.8928    | 0.8940 | 0.8934 | 0.9231   |
| 0.0507        | 12.0  | 2040 | 0.4476          | 0.8911    | 0.8930 | 0.8921 | 0.9224   |
| 0.0507        | 13.0  | 2210 | 0.4478          | 0.8900    | 0.8926 | 0.8913 | 0.9220   |
| 0.0507        | 14.0  | 2380 | 0.4563          | 0.8896    | 0.8931 | 0.8914 | 0.9217   |
| 0.0357        | 15.0  | 2550 | 0.4600          | 0.8894    | 0.8931 | 0.8913 | 0.9217   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "model_from_berturk_upos_22Jan_val_on_dev_v1", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

model\_from\_berturk\_upos\_22Jan\_val\_on\_dev\_v1
===================================================


This model is a fine-tuned version of dbmdz/bert-base-turkish-cased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4600
* Precision: 0.8894
* Recall: 0.8931
* F1: 0.8913
* Accuracy: 0.9217


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



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2