---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: training-4
  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. -->

# training-4

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0335
- Accuracy: 0.9931
- Precision: 0.9982
- Recall: 0.9875
- F1: 0.9928

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.25  | 85   | 0.0511          | 0.9819   | 0.9821    | 0.9804 | 0.9813 |
| No log        | 0.5   | 170  | 0.0752          | 0.9836   | 0.9982    | 0.9679 | 0.9828 |
| No log        | 0.75  | 255  | 0.0550          | 0.9888   | 0.9841    | 0.9929 | 0.9885 |
| 0.1064        | 1.0   | 340  | 0.0383          | 0.9923   | 0.9964    | 0.9875 | 0.9919 |
| 0.1064        | 1.25  | 425  | 0.0485          | 0.9923   | 0.9982    | 0.9857 | 0.9919 |
| 0.1064        | 1.5   | 510  | 0.0468          | 0.9914   | 0.9964    | 0.9857 | 0.9910 |
| 0.1064        | 1.76  | 595  | 0.0477          | 0.9914   | 1.0       | 0.9822 | 0.9910 |
| 0.0322        | 2.01  | 680  | 0.0506          | 0.9931   | 1.0       | 0.9857 | 0.9928 |
| 0.0322        | 2.26  | 765  | 0.0455          | 0.9914   | 0.9928    | 0.9893 | 0.9911 |
| 0.0322        | 2.51  | 850  | 0.0466          | 0.9914   | 0.9946    | 0.9875 | 0.9911 |
| 0.0322        | 2.76  | 935  | 0.0491          | 0.9931   | 1.0       | 0.9857 | 0.9928 |
| 0.0217        | 3.01  | 1020 | 0.0517          | 0.9923   | 0.9964    | 0.9875 | 0.9919 |
| 0.0217        | 3.26  | 1105 | 0.0455          | 0.9931   | 1.0       | 0.9857 | 0.9928 |
| 0.0217        | 3.51  | 1190 | 0.0338          | 0.9931   | 0.9982    | 0.9875 | 0.9928 |
| 0.0217        | 3.76  | 1275 | 0.0385          | 0.9940   | 1.0       | 0.9875 | 0.9937 |
| 0.024         | 4.01  | 1360 | 0.0376          | 0.9931   | 1.0       | 0.9857 | 0.9928 |
| 0.024         | 4.26  | 1445 | 0.0332          | 0.9931   | 0.9982    | 0.9875 | 0.9928 |
| 0.024         | 4.51  | 1530 | 0.0343          | 0.9923   | 0.9946    | 0.9893 | 0.9920 |
| 0.024         | 4.76  | 1615 | 0.0335          | 0.9931   | 0.9982    | 0.9875 | 0.9928 |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.2.0.dev20230913+cu121
- Tokenizers 0.13.3