---
license: mit
base_model: indobenchmark/indobert-base-p2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: performa_model
  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. -->

# performa_model

This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4147
- Accuracy: 0.8230
- F1: 0.7904
- Precision: 0.8488
- Recall: 0.7395

## 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: 16
- eval_batch_size: 16
- 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 | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 370  | 0.4182          | 0.8297   | 0.8108 | 0.8133    | 0.8084 |
| 0.4155        | 2.0   | 740  | 0.4147          | 0.8230   | 0.7904 | 0.8488    | 0.7395 |
| 0.304         | 3.0   | 1110 | 0.4912          | 0.8162   | 0.7952 | 0.8       | 0.7904 |
| 0.304         | 4.0   | 1480 | 0.8223          | 0.8014   | 0.7879 | 0.7604    | 0.8174 |
| 0.1698        | 5.0   | 1850 | 0.8766          | 0.8108   | 0.7935 | 0.7820    | 0.8054 |
| 0.0868        | 6.0   | 2220 | 1.2547          | 0.7919   | 0.7775 | 0.7514    | 0.8054 |
| 0.0357        | 7.0   | 2590 | 1.2560          | 0.7946   | 0.7847 | 0.7446    | 0.8293 |
| 0.0357        | 8.0   | 2960 | 1.3313          | 0.8095   | 0.7886 | 0.7898    | 0.7874 |
| 0.0159        | 9.0   | 3330 | 1.3923          | 0.8122   | 0.7965 | 0.7794    | 0.8144 |
| 0.0066        | 10.0  | 3700 | 1.4022          | 0.8149   | 0.8006 | 0.7790    | 0.8234 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0