---
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_1
  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_1

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: 1.2379
- Accuracy: 0.8469
- Precision: 0.8335
- Recall: 0.8102
- F1: 0.8194

## 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   | 251  | 0.4965          | 0.8270   | 0.8015    | 0.7727 | 0.7833 |
| 0.5228        | 2.0   | 502  | 0.5798          | 0.7932   | 0.7579    | 0.7603 | 0.7583 |
| 0.5228        | 3.0   | 753  | 0.9632          | 0.8290   | 0.8267    | 0.7803 | 0.7969 |
| 0.1728        | 4.0   | 1004 | 1.3744          | 0.7714   | 0.7453    | 0.7690 | 0.7476 |
| 0.1728        | 5.0   | 1255 | 1.1893          | 0.8072   | 0.7791    | 0.7798 | 0.7772 |
| 0.0483        | 6.0   | 1506 | 1.2379          | 0.8469   | 0.8335    | 0.8102 | 0.8194 |
| 0.0483        | 7.0   | 1757 | 1.3635          | 0.8370   | 0.8185    | 0.8044 | 0.8081 |
| 0.0075        | 8.0   | 2008 | 1.3300          | 0.8370   | 0.8168    | 0.8008 | 0.8074 |
| 0.0075        | 9.0   | 2259 | 1.3591          | 0.8410   | 0.8249    | 0.8074 | 0.8142 |
| 0.0026        | 10.0  | 2510 | 1.3548          | 0.8350   | 0.8169    | 0.8008 | 0.8072 |


### Framework versions

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