---
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_4
  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_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: 1.2676
- Accuracy: 0.8546
- Precision: 0.8334
- Recall: 0.8336
- F1: 0.8334

## 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  | 0.4839          | 0.8446   | 0.8238    | 0.8057 | 0.8132 |
| 0.4291        | 2.0   | 504  | 0.8905          | 0.8287   | 0.8060    | 0.8194 | 0.8089 |
| 0.4291        | 3.0   | 756  | 0.9640          | 0.8426   | 0.8142    | 0.8170 | 0.8156 |
| 0.1372        | 4.0   | 1008 | 1.0182          | 0.8406   | 0.8198    | 0.8103 | 0.8139 |
| 0.1372        | 5.0   | 1260 | 1.1472          | 0.8386   | 0.8238    | 0.8051 | 0.8134 |
| 0.0264        | 6.0   | 1512 | 1.2455          | 0.8367   | 0.8145    | 0.8022 | 0.8078 |
| 0.0264        | 7.0   | 1764 | 1.3009          | 0.8406   | 0.8221    | 0.8074 | 0.8139 |
| 0.0072        | 8.0   | 2016 | 1.3084          | 0.8486   | 0.8314    | 0.8187 | 0.8238 |
| 0.0072        | 9.0   | 2268 | 1.2815          | 0.8486   | 0.8272    | 0.8258 | 0.8265 |
| 0.0063        | 10.0  | 2520 | 1.2676          | 0.8546   | 0.8334    | 0.8336 | 0.8334 |


### Framework versions

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