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---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-ia3
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. -->
# sentiment-ia3
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4451
- Accuracy: 0.7870
- Precision: 0.7443
- Recall: 0.7243
- F1: 0.7325
## 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: 5e-05
- train_batch_size: 30
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5653 | 1.0 | 122 | 0.5246 | 0.7218 | 0.6540 | 0.6257 | 0.6333 |
| 0.5167 | 2.0 | 244 | 0.5215 | 0.7293 | 0.6805 | 0.6935 | 0.6855 |
| 0.4984 | 3.0 | 366 | 0.4975 | 0.7444 | 0.6916 | 0.6916 | 0.6916 |
| 0.4765 | 4.0 | 488 | 0.4854 | 0.7419 | 0.6837 | 0.6523 | 0.6619 |
| 0.4797 | 5.0 | 610 | 0.4852 | 0.7719 | 0.7270 | 0.7386 | 0.7320 |
| 0.4668 | 6.0 | 732 | 0.4738 | 0.7669 | 0.7190 | 0.7201 | 0.7195 |
| 0.4622 | 7.0 | 854 | 0.4769 | 0.7719 | 0.7261 | 0.7336 | 0.7295 |
| 0.4621 | 8.0 | 976 | 0.4625 | 0.7494 | 0.6949 | 0.6577 | 0.6686 |
| 0.4561 | 9.0 | 1098 | 0.4609 | 0.7769 | 0.7311 | 0.7122 | 0.7199 |
| 0.4519 | 10.0 | 1220 | 0.4608 | 0.7669 | 0.7252 | 0.6676 | 0.6822 |
| 0.4413 | 11.0 | 1342 | 0.4544 | 0.7694 | 0.7215 | 0.6994 | 0.7080 |
| 0.4449 | 12.0 | 1464 | 0.4569 | 0.7845 | 0.7401 | 0.7425 | 0.7413 |
| 0.4506 | 13.0 | 1586 | 0.4527 | 0.7644 | 0.7197 | 0.6683 | 0.6821 |
| 0.4446 | 14.0 | 1708 | 0.4488 | 0.7794 | 0.7379 | 0.6989 | 0.7121 |
| 0.4426 | 15.0 | 1830 | 0.4491 | 0.7870 | 0.7436 | 0.7293 | 0.7355 |
| 0.4409 | 16.0 | 1952 | 0.4465 | 0.7719 | 0.7257 | 0.6961 | 0.7068 |
| 0.4348 | 17.0 | 2074 | 0.4474 | 0.7870 | 0.7436 | 0.7293 | 0.7355 |
| 0.4478 | 18.0 | 2196 | 0.4460 | 0.7845 | 0.7408 | 0.7225 | 0.7302 |
| 0.4382 | 19.0 | 2318 | 0.4448 | 0.7870 | 0.7447 | 0.7218 | 0.7310 |
| 0.4313 | 20.0 | 2440 | 0.4451 | 0.7870 | 0.7443 | 0.7243 | 0.7325 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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