---
library_name: transformers
license: mit
base_model: indobenchmark/indobert-large-p1
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results_indobert-large-p1_with_preprocess_augmentasi
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# results_indobert-large-p1_with_preprocess_augmentasi

This model is a fine-tuned version of [indobenchmark/indobert-large-p1](https://huggingface.co/indobenchmark/indobert-large-p1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9592
- Accuracy: 0.7001
- Precision: 0.6990
- Recall: 0.7029
- F1: 0.6985

## 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: 32
- eval_batch_size: 32
- 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_steps: 500
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.588         | 1.0   | 220  | 1.2180          | 0.5450   | 0.5403    | 0.5561 | 0.5172 |
| 0.9771        | 2.0   | 440  | 0.8663          | 0.6659   | 0.6636    | 0.6781 | 0.6656 |
| 0.7545        | 3.0   | 660  | 0.8137          | 0.6921   | 0.6912    | 0.6966 | 0.6898 |
| 0.6008        | 4.0   | 880  | 0.8976          | 0.6819   | 0.6784    | 0.6900 | 0.6822 |
| 0.4587        | 5.0   | 1100 | 0.9592          | 0.7001   | 0.6990    | 0.7029 | 0.6985 |
| 0.3447        | 6.0   | 1320 | 1.1043          | 0.6705   | 0.6731    | 0.6778 | 0.6738 |
| 0.2566        | 7.0   | 1540 | 1.1884          | 0.6625   | 0.6609    | 0.6781 | 0.6658 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1