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---
license: mit
base_model: ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: best_berita_bert_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_berita_bert_model_fold_3
This model is a fine-tuned version of [ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa](https://huggingface.co/ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1732
- Accuracy: 0.9808
- Precision: 0.9809
- Recall: 0.9811
- F1: 0.9809
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5469 | 1.0 | 601 | 0.2814 | 0.9409 | 0.9416 | 0.9414 | 0.9410 |
| 0.21 | 2.0 | 1202 | 0.1697 | 0.9600 | 0.9602 | 0.9605 | 0.9600 |
| 0.1002 | 3.0 | 1803 | 0.2227 | 0.9667 | 0.9674 | 0.9673 | 0.9666 |
| 0.0847 | 4.0 | 2404 | 0.2771 | 0.9584 | 0.9599 | 0.9592 | 0.9581 |
| 0.029 | 5.0 | 3005 | 0.1732 | 0.9808 | 0.9809 | 0.9811 | 0.9809 |
| 0.0095 | 6.0 | 3606 | 0.2415 | 0.9734 | 0.9737 | 0.9738 | 0.9733 |
| 0.0134 | 7.0 | 4207 | 0.2048 | 0.9767 | 0.9769 | 0.9771 | 0.9766 |
| 0.0001 | 8.0 | 4808 | 0.2916 | 0.9692 | 0.9697 | 0.9698 | 0.9691 |
| 0.0039 | 9.0 | 5409 | 0.2201 | 0.9784 | 0.9786 | 0.9787 | 0.9784 |
| 0.0 | 10.0 | 6010 | 0.2293 | 0.9742 | 0.9745 | 0.9746 | 0.9742 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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