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

# kategori_aspek_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: 0.5731
- Accuracy: 0.7532
- F1: 0.7342
- Precision: 0.6791
- Recall: 0.8234

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6662        | 1.0   | 1816  | 0.6854          | 0.7449   | 0.7139 | 0.6657    | 0.7857 |
| 0.4846        | 2.0   | 3632  | 0.5731          | 0.7532   | 0.7342 | 0.6791    | 0.8234 |
| 0.3135        | 3.0   | 5448  | 0.6906          | 0.7667   | 0.7431 | 0.7017    | 0.7994 |
| 0.2189        | 4.0   | 7264  | 0.8181          | 0.7755   | 0.7387 | 0.7065    | 0.7994 |
| 0.152         | 5.0   | 9080  | 0.9838          | 0.7893   | 0.7486 | 0.7290    | 0.7799 |
| 0.0938        | 6.0   | 10896 | 1.0601          | 0.7826   | 0.7598 | 0.7314    | 0.7957 |
| 0.0629        | 7.0   | 12712 | 1.3297          | 0.7868   | 0.7665 | 0.7673    | 0.7684 |
| 0.0423        | 8.0   | 14528 | 1.3356          | 0.7906   | 0.7639 | 0.7477    | 0.7875 |
| 0.0178        | 9.0   | 16344 | 1.5868          | 0.7887   | 0.7625 | 0.7656    | 0.7638 |
| 0.008         | 10.0  | 18160 | 1.5453          | 0.7928   | 0.7650 | 0.7621    | 0.7709 |


### Framework versions

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