---
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: 22best_berita_bert_model_fold_2
  results: []
---

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# 22best_berita_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: 1.2522
- Accuracy: 0.8349
- Precision: 0.8373
- Recall: 0.8357
- F1: 0.8333

## 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   | 106  | 0.6551          | 0.7264   | 0.7745    | 0.7082 | 0.7121 |
| No log        | 2.0   | 212  | 0.9467          | 0.6934   | 0.7488    | 0.7142 | 0.6839 |
| No log        | 3.0   | 318  | 0.8995          | 0.8113   | 0.8139    | 0.8146 | 0.8111 |
| No log        | 4.0   | 424  | 1.1543          | 0.7972   | 0.8169    | 0.7986 | 0.7936 |
| 0.3548        | 5.0   | 530  | 1.1540          | 0.8208   | 0.8223    | 0.8148 | 0.8169 |
| 0.3548        | 6.0   | 636  | 1.3006          | 0.8255   | 0.8431    | 0.8261 | 0.8225 |
| 0.3548        | 7.0   | 742  | 1.2421          | 0.8255   | 0.8328    | 0.8265 | 0.8240 |
| 0.3548        | 8.0   | 848  | 1.2522          | 0.8349   | 0.8373    | 0.8357 | 0.8333 |
| 0.3548        | 9.0   | 954  | 1.2637          | 0.8302   | 0.8330    | 0.8308 | 0.8285 |
| 0.0125        | 10.0  | 1060 | 1.2667          | 0.8302   | 0.8330    | 0.8308 | 0.8285 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1