---
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_bert_model_fold_5
  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_bert_model_fold_5

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: 1.0532
- Accuracy: 0.8586
- Precision: 0.8342
- Recall: 0.8005
- F1: 0.8134

## 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  | 0.5880          | 0.8048   | 0.8281    | 0.6620 | 0.6694 |
| 0.5304        | 2.0   | 504  | 0.8974          | 0.7928   | 0.7497    | 0.7582 | 0.7525 |
| 0.5304        | 3.0   | 756  | 1.0145          | 0.7928   | 0.7483    | 0.7455 | 0.7418 |
| 0.1872        | 4.0   | 1008 | 1.0229          | 0.8227   | 0.7856    | 0.7561 | 0.7678 |
| 0.1872        | 5.0   | 1260 | 1.0532          | 0.8586   | 0.8342    | 0.8005 | 0.8134 |
| 0.037         | 6.0   | 1512 | 1.2624          | 0.8347   | 0.7927    | 0.7997 | 0.7957 |
| 0.037         | 7.0   | 1764 | 1.3287          | 0.8227   | 0.7806    | 0.7951 | 0.7870 |
| 0.0076        | 8.0   | 2016 | 1.3369          | 0.8347   | 0.8064    | 0.7747 | 0.7863 |
| 0.0076        | 9.0   | 2268 | 1.3292          | 0.8406   | 0.8093    | 0.7883 | 0.7974 |
| 0.0002        | 10.0  | 2520 | 1.3507          | 0.8347   | 0.7970    | 0.7844 | 0.7901 |


### Framework versions

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