---
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_2
  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_2

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: 1.1931
- Accuracy: 0.8606
- Precision: 0.8353
- Recall: 0.8104
- F1: 0.8211

## 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  | 0.5154          | 0.8347   | 0.8228    | 0.7436 | 0.7630 |
| 0.5197        | 2.0   | 504  | 0.5658          | 0.8466   | 0.8122    | 0.7892 | 0.7981 |
| 0.5197        | 3.0   | 756  | 0.8319          | 0.8526   | 0.8328    | 0.7857 | 0.8007 |
| 0.1558        | 4.0   | 1008 | 0.8339          | 0.8526   | 0.8230    | 0.8138 | 0.8180 |
| 0.1558        | 5.0   | 1260 | 1.0511          | 0.8486   | 0.8241    | 0.7922 | 0.8052 |
| 0.0472        | 6.0   | 1512 | 1.1080          | 0.8546   | 0.8313    | 0.8008 | 0.8135 |
| 0.0472        | 7.0   | 1764 | 1.1492          | 0.8566   | 0.8315    | 0.8093 | 0.8189 |
| 0.005         | 8.0   | 2016 | 1.1661          | 0.8566   | 0.8289    | 0.8084 | 0.8175 |
| 0.005         | 9.0   | 2268 | 1.1931          | 0.8606   | 0.8353    | 0.8104 | 0.8211 |
| 0.0031        | 10.0  | 2520 | 1.1820          | 0.8586   | 0.8335    | 0.8020 | 0.8149 |


### Framework versions

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