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---
license: mit
base_model: dbmdz/bert-base-turkish-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pos_tagger_3112
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. -->
# pos_tagger_3112
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: 0.5781
- Precision: 0.8885
- Recall: 0.8926
- F1: 0.8906
- Accuracy: 0.9222
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 244 | 0.2966 | 0.8889 | 0.8935 | 0.8912 | 0.9215 |
| No log | 2.0 | 488 | 0.2884 | 0.8958 | 0.8969 | 0.8963 | 0.9258 |
| 0.3778 | 3.0 | 732 | 0.3138 | 0.8919 | 0.8913 | 0.8916 | 0.9224 |
| 0.3778 | 4.0 | 976 | 0.3086 | 0.8935 | 0.8960 | 0.8948 | 0.9252 |
| 0.1601 | 5.0 | 1220 | 0.3354 | 0.8897 | 0.8940 | 0.8919 | 0.9232 |
| 0.1601 | 6.0 | 1464 | 0.3486 | 0.8863 | 0.8911 | 0.8887 | 0.9212 |
| 0.1077 | 7.0 | 1708 | 0.3700 | 0.8899 | 0.8945 | 0.8922 | 0.9236 |
| 0.1077 | 8.0 | 1952 | 0.3920 | 0.8846 | 0.8905 | 0.8875 | 0.9207 |
| 0.0709 | 9.0 | 2196 | 0.4220 | 0.8860 | 0.8911 | 0.8885 | 0.9211 |
| 0.0709 | 10.0 | 2440 | 0.4467 | 0.8889 | 0.8934 | 0.8912 | 0.9226 |
| 0.0447 | 11.0 | 2684 | 0.4730 | 0.8834 | 0.8891 | 0.8862 | 0.9183 |
| 0.0447 | 12.0 | 2928 | 0.5008 | 0.8869 | 0.8921 | 0.8895 | 0.9217 |
| 0.0289 | 13.0 | 3172 | 0.5190 | 0.8866 | 0.8919 | 0.8892 | 0.9213 |
| 0.0289 | 14.0 | 3416 | 0.5384 | 0.8865 | 0.8904 | 0.8884 | 0.9204 |
| 0.0204 | 15.0 | 3660 | 0.5499 | 0.8852 | 0.8907 | 0.8879 | 0.9205 |
| 0.0204 | 16.0 | 3904 | 0.5564 | 0.8864 | 0.8923 | 0.8893 | 0.9213 |
| 0.015 | 17.0 | 4148 | 0.5688 | 0.8883 | 0.8920 | 0.8901 | 0.9220 |
| 0.015 | 18.0 | 4392 | 0.5740 | 0.8874 | 0.8927 | 0.8901 | 0.9213 |
| 0.0116 | 19.0 | 4636 | 0.5742 | 0.8879 | 0.8925 | 0.8902 | 0.9219 |
| 0.0116 | 20.0 | 4880 | 0.5781 | 0.8885 | 0.8926 | 0.8906 | 0.9222 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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