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---
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_1
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_1
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: 1.0321
- Accuracy: 0.8708
- Precision: 0.8700
- Recall: 0.8338
- F1: 0.8483
## 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 | 251 | 0.4388 | 0.8648 | 0.8750 | 0.8070 | 0.8273 |
| 0.4884 | 2.0 | 502 | 0.5608 | 0.8469 | 0.8258 | 0.8076 | 0.8143 |
| 0.4884 | 3.0 | 753 | 0.8603 | 0.8529 | 0.8604 | 0.7834 | 0.8026 |
| 0.1197 | 4.0 | 1004 | 0.7872 | 0.8648 | 0.8456 | 0.8444 | 0.8433 |
| 0.1197 | 5.0 | 1255 | 1.0494 | 0.8429 | 0.8142 | 0.8253 | 0.8155 |
| 0.0154 | 6.0 | 1506 | 1.0147 | 0.8688 | 0.8585 | 0.8296 | 0.8404 |
| 0.0154 | 7.0 | 1757 | 1.0059 | 0.8688 | 0.8618 | 0.8297 | 0.8426 |
| 0.0078 | 8.0 | 2008 | 1.0833 | 0.8549 | 0.8359 | 0.8251 | 0.8265 |
| 0.0078 | 9.0 | 2259 | 1.1080 | 0.8668 | 0.8612 | 0.8235 | 0.8369 |
| 0.0036 | 10.0 | 2510 | 1.0321 | 0.8708 | 0.8700 | 0.8338 | 0.8483 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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