---
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_3
  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_3

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: 0.6566
- Accuracy: 0.8566
- Precision: 0.8385
- Recall: 0.8215
- F1: 0.8286

## 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  | 0.6056          | 0.8307   | 0.8493    | 0.7768 | 0.7998 |
| 0.4224        | 2.0   | 504  | 0.6566          | 0.8566   | 0.8385    | 0.8215 | 0.8286 |
| 0.4224        | 3.0   | 756  | 0.8546          | 0.8506   | 0.8410    | 0.8094 | 0.8211 |
| 0.1153        | 4.0   | 1008 | 0.9318          | 0.8446   | 0.8220    | 0.8093 | 0.8146 |
| 0.1153        | 5.0   | 1260 | 1.0414          | 0.8367   | 0.8297    | 0.7856 | 0.7984 |
| 0.0403        | 6.0   | 1512 | 1.1900          | 0.8327   | 0.8118    | 0.7848 | 0.7945 |
| 0.0403        | 7.0   | 1764 | 1.1295          | 0.8406   | 0.8143    | 0.8041 | 0.8086 |
| 0.0107        | 8.0   | 2016 | 1.2422          | 0.8327   | 0.8164    | 0.7927 | 0.8004 |
| 0.0107        | 9.0   | 2268 | 1.1965          | 0.8386   | 0.8170    | 0.8013 | 0.8079 |
| 0.0003        | 10.0  | 2520 | 1.2360          | 0.8426   | 0.8212    | 0.8025 | 0.8103 |


### Framework versions

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