---
library_name: transformers
license: mit
base_model: indobenchmark/indobert-base-p1
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: results_fold_4
  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. -->

# results_fold_4

This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2303
- F1: 0.8166
- Roc Auc: 0.8836
- Accuracy: 0.7468

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.2001        | 1.0   | 1186 | 0.2002          | 0.7755 | 0.8456  | 0.6785   |
| 0.1439        | 2.0   | 2372 | 0.1855          | 0.8030 | 0.8729  | 0.7097   |
| 0.1403        | 3.0   | 3558 | 0.1960          | 0.8141 | 0.8784  | 0.7367   |
| 0.0556        | 4.0   | 4744 | 0.2235          | 0.8128 | 0.8770  | 0.7367   |
| 0.0173        | 5.0   | 5930 | 0.2303          | 0.8166 | 0.8836  | 0.7468   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1