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

# gacha_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: 0.5789
- Accuracy: 0.8089
- F1: 0.8065
- Precision: 0.8115
- Recall: 0.8052

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 0.23  | 50   | 0.5084          | 0.7739   | 0.7737 | 0.7826    | 0.7790 |
| No log        | 0.47  | 100  | 0.4663          | 0.7972   | 0.7967 | 0.7964    | 0.7971 |
| No log        | 0.7   | 150  | 0.4834          | 0.8112   | 0.8094 | 0.8125    | 0.8082 |
| No log        | 0.93  | 200  | 0.4445          | 0.8135   | 0.8104 | 0.8194    | 0.8087 |
| No log        | 1.16  | 250  | 0.6506          | 0.7879   | 0.7786 | 0.8149    | 0.7781 |
| No log        | 1.4   | 300  | 0.5314          | 0.7692   | 0.7687 | 0.7810    | 0.7752 |
| No log        | 1.63  | 350  | 0.5149          | 0.8065   | 0.8021 | 0.8167    | 0.8003 |
| No log        | 1.86  | 400  | 0.4735          | 0.8298   | 0.8289 | 0.8296    | 0.8284 |
| No log        | 2.09  | 450  | 0.5093          | 0.8275   | 0.8262 | 0.8280    | 0.8253 |
| 0.3338        | 2.33  | 500  | 0.5789          | 0.8089   | 0.8065 | 0.8115    | 0.8052 |
| 0.3338        | 2.56  | 550  | 0.6539          | 0.8065   | 0.8059 | 0.8057    | 0.8062 |
| 0.3338        | 2.79  | 600  | 0.6995          | 0.8042   | 0.8018 | 0.8068    | 0.8005 |
| 0.3338        | 3.02  | 650  | 0.8298          | 0.8182   | 0.8168 | 0.8186    | 0.8160 |
| 0.3338        | 3.26  | 700  | 0.7829          | 0.8089   | 0.8077 | 0.8085    | 0.8072 |
| 0.3338        | 3.49  | 750  | 0.7700          | 0.8205   | 0.8195 | 0.8202    | 0.8191 |
| 0.3338        | 3.72  | 800  | 0.9060          | 0.8089   | 0.8057 | 0.8145    | 0.8040 |
| 0.3338        | 3.95  | 850  | 0.9478          | 0.8112   | 0.8072 | 0.8205    | 0.8053 |
| 0.3338        | 4.19  | 900  | 0.9171          | 0.8089   | 0.8067 | 0.8109    | 0.8054 |
| 0.3338        | 4.42  | 950  | 0.9512          | 0.8065   | 0.8043 | 0.8088    | 0.8030 |
| 0.079         | 4.65  | 1000 | 0.9579          | 0.8065   | 0.8047 | 0.8078    | 0.8035 |
| 0.079         | 4.88  | 1050 | 0.9471          | 0.8089   | 0.8073 | 0.8095    | 0.8063 |


### Framework versions

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