---
license: mit
base_model: ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: best_bert_model_fold_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. -->

# best_bert_model_fold_4

This model is a fine-tuned version of [ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa](https://huggingface.co/ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6077
- Accuracy: 0.8187
- Precision: 0.7940
- Recall: 0.7673
- F1: 0.7780

## 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: 5e-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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 252  | 0.5413          | 0.8088   | 0.7842    | 0.7520 | 0.7641 |
| 0.5338        | 2.0   | 504  | 0.6077          | 0.8187   | 0.7940    | 0.7673 | 0.7780 |
| 0.5338        | 3.0   | 756  | 0.9325          | 0.7928   | 0.7597    | 0.7270 | 0.7357 |
| 0.1829        | 4.0   | 1008 | 1.1287          | 0.8068   | 0.7916    | 0.7662 | 0.7763 |
| 0.1829        | 5.0   | 1260 | 1.2985          | 0.7988   | 0.7700    | 0.7688 | 0.7676 |
| 0.0459        | 6.0   | 1512 | 1.5210          | 0.8108   | 0.7837    | 0.7721 | 0.7773 |
| 0.0459        | 7.0   | 1764 | 1.5855          | 0.8028   | 0.7799    | 0.7611 | 0.7680 |
| 0.0097        | 8.0   | 2016 | 1.5212          | 0.8008   | 0.7684    | 0.7731 | 0.7706 |
| 0.0097        | 9.0   | 2268 | 1.5775          | 0.8028   | 0.7730    | 0.7656 | 0.7682 |
| 0.0001        | 10.0  | 2520 | 1.5819          | 0.8048   | 0.7746    | 0.7669 | 0.7698 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1