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---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fine_tune_bert-base-cased
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. -->
# fine_tune_bert-base-cased
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co./bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0842
- Precision: 0.9376
- Recall: 0.9541
- F1: 0.9458
- Accuracy: 0.9866
## 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: 64
- eval_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2667 | 1.0 | 220 | 0.0739 | 0.8619 | 0.9118 | 0.8862 | 0.9776 |
| 0.0602 | 2.0 | 440 | 0.0641 | 0.9109 | 0.9357 | 0.9231 | 0.9830 |
| 0.0361 | 3.0 | 660 | 0.0594 | 0.9187 | 0.9401 | 0.9293 | 0.9845 |
| 0.0234 | 4.0 | 880 | 0.0564 | 0.9233 | 0.9461 | 0.9346 | 0.9854 |
| 0.0164 | 5.0 | 1100 | 0.0585 | 0.9211 | 0.9465 | 0.9336 | 0.9856 |
| 0.0123 | 6.0 | 1320 | 0.0656 | 0.9212 | 0.9483 | 0.9346 | 0.9850 |
| 0.0084 | 7.0 | 1540 | 0.0639 | 0.9290 | 0.9514 | 0.9401 | 0.9864 |
| 0.0072 | 8.0 | 1760 | 0.0735 | 0.9325 | 0.9482 | 0.9403 | 0.9862 |
| 0.0051 | 9.0 | 1980 | 0.0745 | 0.9319 | 0.9488 | 0.9403 | 0.9856 |
| 0.0042 | 10.0 | 2200 | 0.0783 | 0.9308 | 0.9490 | 0.9398 | 0.9858 |
| 0.0034 | 11.0 | 2420 | 0.0782 | 0.9337 | 0.9509 | 0.9422 | 0.9862 |
| 0.0026 | 12.0 | 2640 | 0.0822 | 0.9328 | 0.9505 | 0.9416 | 0.9858 |
| 0.0019 | 13.0 | 2860 | 0.0785 | 0.9335 | 0.9525 | 0.9429 | 0.9862 |
| 0.0018 | 14.0 | 3080 | 0.0819 | 0.9382 | 0.9525 | 0.9453 | 0.9865 |
| 0.0015 | 15.0 | 3300 | 0.0846 | 0.9349 | 0.9524 | 0.9436 | 0.9863 |
| 0.0013 | 16.0 | 3520 | 0.0880 | 0.9353 | 0.9519 | 0.9435 | 0.9860 |
| 0.0012 | 17.0 | 3740 | 0.0846 | 0.9362 | 0.9527 | 0.9444 | 0.9864 |
| 0.001 | 18.0 | 3960 | 0.0868 | 0.9374 | 0.9532 | 0.9453 | 0.9864 |
| 0.0009 | 19.0 | 4180 | 0.0842 | 0.9381 | 0.9536 | 0.9458 | 0.9868 |
| 0.0009 | 20.0 | 4400 | 0.0842 | 0.9376 | 0.9541 | 0.9458 | 0.9866 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.2.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1 |