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---
license: mit
base_model: indobenchmark/indobert-base-p2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: aspect_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. -->
# aspect_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: 1.3490
- Accuracy: 0.8084
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 72 | 0.6516 | 0.7735 |
| No log | 2.0 | 144 | 0.6119 | 0.7909 |
| No log | 3.0 | 216 | 0.6152 | 0.8049 |
| No log | 4.0 | 288 | 0.7480 | 0.8118 |
| No log | 5.0 | 360 | 1.0121 | 0.7770 |
| No log | 6.0 | 432 | 1.0780 | 0.7909 |
| 0.27 | 7.0 | 504 | 1.1602 | 0.7840 |
| 0.27 | 8.0 | 576 | 1.2136 | 0.8014 |
| 0.27 | 9.0 | 648 | 1.2490 | 0.8014 |
| 0.27 | 10.0 | 720 | 1.3102 | 0.7840 |
| 0.27 | 11.0 | 792 | 1.3184 | 0.8049 |
| 0.27 | 12.0 | 864 | 1.3255 | 0.8014 |
| 0.27 | 13.0 | 936 | 1.3192 | 0.8049 |
| 0.0022 | 14.0 | 1008 | 1.3229 | 0.7944 |
| 0.0022 | 15.0 | 1080 | 1.3415 | 0.8014 |
| 0.0022 | 16.0 | 1152 | 1.3515 | 0.7909 |
| 0.0022 | 17.0 | 1224 | 1.3544 | 0.7944 |
| 0.0022 | 18.0 | 1296 | 1.3529 | 0.7944 |
| 0.0022 | 19.0 | 1368 | 1.3484 | 0.8084 |
| 0.0022 | 20.0 | 1440 | 1.3490 | 0.8084 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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