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---
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
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