---
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_2
  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_2

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: 0.3694
- Accuracy: 0.8785
- Precision: 0.8509
- Recall: 0.8453
- F1: 0.8475

## 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  | 0.3694          | 0.8785   | 0.8509    | 0.8453 | 0.8475 |
| 0.4624        | 2.0   | 504  | 0.6764          | 0.8506   | 0.8218    | 0.8193 | 0.8150 |
| 0.4624        | 3.0   | 756  | 0.7159          | 0.8745   | 0.8644    | 0.8280 | 0.8431 |
| 0.1511        | 4.0   | 1008 | 0.6390          | 0.8785   | 0.8689    | 0.8156 | 0.8345 |
| 0.1511        | 5.0   | 1260 | 0.7848          | 0.8785   | 0.8594    | 0.8438 | 0.8506 |
| 0.0503        | 6.0   | 1512 | 0.8157          | 0.8785   | 0.8544    | 0.8493 | 0.8512 |
| 0.0503        | 7.0   | 1764 | 0.9260          | 0.8785   | 0.8600    | 0.8427 | 0.8488 |
| 0.0103        | 8.0   | 2016 | 0.8872          | 0.8765   | 0.8526    | 0.8325 | 0.8415 |
| 0.0103        | 9.0   | 2268 | 1.0181          | 0.8745   | 0.8552    | 0.8330 | 0.8422 |
| 0.0006        | 10.0  | 2520 | 1.0201          | 0.8745   | 0.8542    | 0.8330 | 0.8418 |


### Framework versions

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