---
license: mit
base_model: ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: best_berita_roberta_model_fold_4
  results: []
---

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# best_berita_roberta_model_fold_4

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: 0.1309
- Accuracy: 0.9808
- Precision: 0.9814
- Recall: 0.9809
- F1: 0.9811

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6294        | 1.0   | 601  | 0.2849          | 0.9217   | 0.9233    | 0.9253 | 0.9217 |
| 0.301         | 2.0   | 1202 | 0.3187          | 0.9409   | 0.9409    | 0.9428 | 0.9413 |
| 0.1565        | 3.0   | 1803 | 0.2576          | 0.9609   | 0.9609    | 0.9626 | 0.9611 |
| 0.0962        | 4.0   | 2404 | 0.3011          | 0.9567   | 0.9577    | 0.9593 | 0.9569 |
| 0.0322        | 5.0   | 3005 | 0.1309          | 0.9808   | 0.9814    | 0.9809 | 0.9811 |
| 0.0218        | 6.0   | 3606 | 0.2434          | 0.9667   | 0.9672    | 0.9684 | 0.9670 |
| 0.0127        | 7.0   | 4207 | 0.3759          | 0.9492   | 0.9509    | 0.9524 | 0.9492 |
| 0.0136        | 8.0   | 4808 | 0.3133          | 0.9642   | 0.9650    | 0.9664 | 0.9645 |
| 0.0024        | 9.0   | 5409 | 0.2968          | 0.9667   | 0.9674    | 0.9687 | 0.9671 |
| 0.0026        | 10.0  | 6010 | 0.2784          | 0.9684   | 0.9690    | 0.9703 | 0.9687 |


### Framework versions

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