---
license: mit
base_model: ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: 22best_berita_roberta_model_fold_1
  results: []
---

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# 22best_berita_roberta_model_fold_1

This model is a fine-tuned version of [ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa](https://huggingface.co/ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8575
- Accuracy: 0.8868
- Precision: 0.8904
- Recall: 0.8843
- F1: 0.8870

## 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  | 0.9177          | 0.5472   | 0.7029    | 0.6055 | 0.5274 |
| No log        | 2.0   | 212  | 0.8990          | 0.7264   | 0.7612    | 0.7252 | 0.7101 |
| No log        | 3.0   | 318  | 0.7968          | 0.8491   | 0.8478    | 0.8622 | 0.8496 |
| No log        | 4.0   | 424  | 0.8026          | 0.8396   | 0.8400    | 0.8413 | 0.8353 |
| 0.5042        | 5.0   | 530  | 1.0039          | 0.8443   | 0.8579    | 0.8603 | 0.8488 |
| 0.5042        | 6.0   | 636  | 0.8274          | 0.8774   | 0.8743    | 0.8850 | 0.8780 |
| 0.5042        | 7.0   | 742  | 0.8575          | 0.8868   | 0.8904    | 0.8843 | 0.8870 |
| 0.5042        | 8.0   | 848  | 0.9014          | 0.8821   | 0.8806    | 0.8906 | 0.8830 |
| 0.5042        | 9.0   | 954  | 0.9622          | 0.8726   | 0.8723    | 0.8859 | 0.8741 |
| 0.0373        | 10.0  | 1060 | 0.9673          | 0.8726   | 0.8723    | 0.8859 | 0.8741 |


### Framework versions

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