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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
should probably proofread and complete it, then remove this comment. -->
# 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
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