---
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_3
  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_3

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: 1.2908
- Accuracy: 0.8386
- Precision: 0.8281
- Recall: 0.7986
- F1: 0.8101

## 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  | 0.6290          | 0.8008   | 0.7903    | 0.7322 | 0.7457 |
| 0.5166        | 2.0   | 504  | 0.6945          | 0.8068   | 0.8131    | 0.7396 | 0.7568 |
| 0.5166        | 3.0   | 756  | 0.9795          | 0.8108   | 0.7953    | 0.7652 | 0.7721 |
| 0.1546        | 4.0   | 1008 | 1.1504          | 0.8187   | 0.8024    | 0.7829 | 0.7902 |
| 0.1546        | 5.0   | 1260 | 1.2908          | 0.8386   | 0.8281    | 0.7986 | 0.8101 |
| 0.0243        | 6.0   | 1512 | 1.2868          | 0.8247   | 0.8043    | 0.7947 | 0.7988 |
| 0.0243        | 7.0   | 1764 | 1.4339          | 0.8307   | 0.8214    | 0.7823 | 0.7949 |
| 0.0077        | 8.0   | 2016 | 1.4287          | 0.8327   | 0.8222    | 0.7845 | 0.7978 |
| 0.0077        | 9.0   | 2268 | 1.4630          | 0.8287   | 0.8098    | 0.7842 | 0.7941 |
| 0.0001        | 10.0  | 2520 | 1.4618          | 0.8307   | 0.8129    | 0.7863 | 0.7966 |


### Framework versions

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