| | --- |
| | 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 |
| |
|
