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---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: 20240327211222_nice_straka
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. -->
# 20240327211222_nice_straka
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0208
- Precision: 0.9848
- Recall: 0.9853
- F1: 0.9850
- Accuracy: 0.9923
## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 69
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 350
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0519 | 0.09 | 300 | 0.0367 | 0.9736 | 0.9691 | 0.9713 | 0.9856 |
| 0.0518 | 0.17 | 600 | 0.0379 | 0.9717 | 0.9709 | 0.9713 | 0.9855 |
| 0.048 | 0.26 | 900 | 0.0357 | 0.9742 | 0.9692 | 0.9717 | 0.9858 |
| 0.0478 | 0.34 | 1200 | 0.0350 | 0.9736 | 0.9724 | 0.9730 | 0.9863 |
| 0.0495 | 0.43 | 1500 | 0.0366 | 0.9734 | 0.9703 | 0.9718 | 0.9856 |
| 0.0457 | 0.51 | 1800 | 0.0344 | 0.9719 | 0.9749 | 0.9734 | 0.9863 |
| 0.0464 | 0.6 | 2100 | 0.0347 | 0.9731 | 0.9717 | 0.9724 | 0.9861 |
| 0.0447 | 0.68 | 2400 | 0.0329 | 0.9743 | 0.9739 | 0.9741 | 0.9868 |
| 0.0435 | 0.77 | 2700 | 0.0332 | 0.9738 | 0.9748 | 0.9743 | 0.9868 |
| 0.0414 | 0.85 | 3000 | 0.0324 | 0.9729 | 0.9771 | 0.9750 | 0.9871 |
| 0.0412 | 0.94 | 3300 | 0.0312 | 0.9759 | 0.9756 | 0.9758 | 0.9875 |
| 0.0352 | 1.02 | 3600 | 0.0312 | 0.9749 | 0.9760 | 0.9754 | 0.9875 |
| 0.0353 | 1.11 | 3900 | 0.0304 | 0.9767 | 0.9759 | 0.9763 | 0.9878 |
| 0.0348 | 1.19 | 4200 | 0.0305 | 0.9765 | 0.9748 | 0.9757 | 0.9877 |
| 0.0362 | 1.28 | 4500 | 0.0313 | 0.9768 | 0.9738 | 0.9753 | 0.9876 |
| 0.0352 | 1.36 | 4800 | 0.0304 | 0.9764 | 0.9771 | 0.9767 | 0.9880 |
| 0.0344 | 1.45 | 5100 | 0.0306 | 0.9778 | 0.9744 | 0.9761 | 0.9880 |
| 0.0337 | 1.54 | 5400 | 0.0288 | 0.9779 | 0.9769 | 0.9774 | 0.9886 |
| 0.0328 | 1.62 | 5700 | 0.0284 | 0.9776 | 0.9777 | 0.9776 | 0.9888 |
| 0.0335 | 1.71 | 6000 | 0.0277 | 0.9783 | 0.9779 | 0.9781 | 0.9887 |
| 0.0329 | 1.79 | 6300 | 0.0284 | 0.9791 | 0.9752 | 0.9772 | 0.9886 |
| 0.0328 | 1.88 | 6600 | 0.0292 | 0.9764 | 0.9773 | 0.9768 | 0.9882 |
| 0.0316 | 1.96 | 6900 | 0.0268 | 0.9785 | 0.9773 | 0.9779 | 0.9890 |
| 0.0264 | 2.05 | 7200 | 0.0272 | 0.9776 | 0.9803 | 0.9789 | 0.9892 |
| 0.0269 | 2.13 | 7500 | 0.0274 | 0.9792 | 0.9782 | 0.9787 | 0.9891 |
| 0.027 | 2.22 | 7800 | 0.0291 | 0.9774 | 0.9782 | 0.9778 | 0.9889 |
| 0.0262 | 2.3 | 8100 | 0.0249 | 0.9809 | 0.9807 | 0.9808 | 0.9902 |
| 0.0258 | 2.39 | 8400 | 0.0255 | 0.9808 | 0.9805 | 0.9806 | 0.9900 |
| 0.0261 | 2.47 | 8700 | 0.0251 | 0.9808 | 0.9800 | 0.9804 | 0.9900 |
| 0.0251 | 2.56 | 9000 | 0.0250 | 0.9814 | 0.9788 | 0.9801 | 0.9901 |
| 0.0248 | 2.64 | 9300 | 0.0248 | 0.9813 | 0.9791 | 0.9802 | 0.9901 |
| 0.0246 | 2.73 | 9600 | 0.0248 | 0.9800 | 0.9817 | 0.9809 | 0.9902 |
| 0.0243 | 2.82 | 9900 | 0.0239 | 0.9793 | 0.9819 | 0.9806 | 0.9900 |
| 0.0241 | 2.9 | 10200 | 0.0236 | 0.9805 | 0.9823 | 0.9814 | 0.9904 |
| 0.0238 | 2.99 | 10500 | 0.0231 | 0.9822 | 0.9799 | 0.9811 | 0.9907 |
| 0.0187 | 3.07 | 10800 | 0.0259 | 0.9782 | 0.9823 | 0.9802 | 0.9901 |
| 0.0188 | 3.16 | 11100 | 0.0231 | 0.9821 | 0.9827 | 0.9824 | 0.9909 |
| 0.0189 | 3.24 | 11400 | 0.0229 | 0.9830 | 0.9802 | 0.9816 | 0.9910 |
| 0.0191 | 3.33 | 11700 | 0.0220 | 0.9815 | 0.9827 | 0.9821 | 0.9910 |
| 0.0187 | 3.41 | 12000 | 0.0223 | 0.9821 | 0.9834 | 0.9828 | 0.9912 |
| 0.018 | 3.5 | 12300 | 0.0224 | 0.9802 | 0.9829 | 0.9815 | 0.9909 |
| 0.0183 | 3.58 | 12600 | 0.0217 | 0.9823 | 0.9831 | 0.9827 | 0.9911 |
| 0.0176 | 3.67 | 12900 | 0.0214 | 0.9840 | 0.9824 | 0.9832 | 0.9916 |
| 0.0177 | 3.75 | 13200 | 0.0211 | 0.9837 | 0.9834 | 0.9835 | 0.9916 |
| 0.0173 | 3.84 | 13500 | 0.0210 | 0.9828 | 0.9840 | 0.9834 | 0.9916 |
| 0.017 | 3.92 | 13800 | 0.0207 | 0.9832 | 0.9839 | 0.9836 | 0.9916 |
| 0.0141 | 4.01 | 14100 | 0.0213 | 0.9844 | 0.9838 | 0.9841 | 0.9919 |
| 0.0129 | 4.09 | 14400 | 0.0213 | 0.9837 | 0.9849 | 0.9843 | 0.9919 |
| 0.013 | 4.18 | 14700 | 0.0228 | 0.9831 | 0.9834 | 0.9833 | 0.9915 |
| 0.0128 | 4.27 | 15000 | 0.0210 | 0.9844 | 0.9846 | 0.9845 | 0.9920 |
| 0.0126 | 4.35 | 15300 | 0.0212 | 0.9843 | 0.9842 | 0.9842 | 0.9920 |
| 0.0125 | 4.44 | 15600 | 0.0214 | 0.9845 | 0.9844 | 0.9844 | 0.9920 |
| 0.0121 | 4.52 | 15900 | 0.0217 | 0.9844 | 0.9846 | 0.9845 | 0.9921 |
| 0.012 | 4.61 | 16200 | 0.0211 | 0.9847 | 0.9848 | 0.9847 | 0.9922 |
| 0.0119 | 4.69 | 16500 | 0.0209 | 0.9845 | 0.9852 | 0.9848 | 0.9922 |
| 0.0116 | 4.78 | 16800 | 0.0211 | 0.9845 | 0.9847 | 0.9846 | 0.9922 |
| 0.0115 | 4.86 | 17100 | 0.0210 | 0.9850 | 0.9844 | 0.9847 | 0.9923 |
| 0.0115 | 4.95 | 17400 | 0.0208 | 0.9848 | 0.9853 | 0.9850 | 0.9923 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.0a0+6a974be
- Datasets 2.18.0
- Tokenizers 0.15.2
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