---
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_emotion
  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_emotion

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: 0.7407
- Accuracy: 0.7682
- Precision: 0.7698
- Recall: 0.7773
- F1: 0.7668

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.6564        | 1.0   | 111  | 1.5223          | 0.2955   | 0.3507    | 0.2474 | 0.1849 |
| 1.3092        | 2.0   | 222  | 1.0048          | 0.6205   | 0.6162    | 0.6264 | 0.6064 |
| 0.8835        | 3.0   | 333  | 0.7088          | 0.75     | 0.7506    | 0.7557 | 0.7491 |
| 0.6384        | 4.0   | 444  | 0.7077          | 0.7477   | 0.7458    | 0.7658 | 0.7499 |
| 0.4962        | 5.0   | 555  | 0.7407          | 0.7682   | 0.7698    | 0.7773 | 0.7668 |
| 0.3875        | 6.0   | 666  | 0.7610          | 0.7477   | 0.7457    | 0.7649 | 0.7517 |
| 0.2849        | 7.0   | 777  | 0.8377          | 0.7523   | 0.7565    | 0.7598 | 0.7545 |


### Framework versions

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