---
license: mit
base_model: ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: best_roberta_model_fold_5
  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_roberta_model_fold_5

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: 1.1930
- Accuracy: 0.8625
- Precision: 0.8332
- Recall: 0.8376
- F1: 0.8353

## 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  | 0.5996          | 0.8526   | 0.8428    | 0.7584 | 0.7817 |
| 0.4616        | 2.0   | 504  | 0.7400          | 0.8207   | 0.7814    | 0.7663 | 0.7684 |
| 0.4616        | 3.0   | 756  | 0.7731          | 0.8586   | 0.8327    | 0.8058 | 0.8173 |
| 0.1366        | 4.0   | 1008 | 0.9903          | 0.8446   | 0.8110    | 0.8439 | 0.8182 |
| 0.1366        | 5.0   | 1260 | 1.0818          | 0.8506   | 0.8198    | 0.8084 | 0.8133 |
| 0.0362        | 6.0   | 1512 | 1.1802          | 0.8486   | 0.8164    | 0.8199 | 0.8178 |
| 0.0362        | 7.0   | 1764 | 1.1920          | 0.8586   | 0.8333    | 0.8135 | 0.8224 |
| 0.0119        | 8.0   | 2016 | 1.2077          | 0.8546   | 0.8206    | 0.8323 | 0.8259 |
| 0.0119        | 9.0   | 2268 | 1.2426          | 0.8526   | 0.8178    | 0.8323 | 0.8244 |
| 0.0034        | 10.0  | 2520 | 1.1930          | 0.8625   | 0.8332    | 0.8376 | 0.8353 |


### Framework versions

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