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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-pt-pl10-4
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-pt-pl10-4
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: 0.3026
- Accuracy: 0.8822
- Precision: 0.8624
- Recall: 0.8492
- F1: 0.8553
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5514 | 1.0 | 122 | 0.5084 | 0.7218 | 0.6601 | 0.6507 | 0.6546 |
| 0.4753 | 2.0 | 244 | 0.4007 | 0.8170 | 0.7870 | 0.7530 | 0.7662 |
| 0.3834 | 3.0 | 366 | 0.3542 | 0.8396 | 0.8449 | 0.7540 | 0.7805 |
| 0.3188 | 4.0 | 488 | 0.3214 | 0.8622 | 0.8342 | 0.8325 | 0.8333 |
| 0.2981 | 5.0 | 610 | 0.2984 | 0.8822 | 0.8624 | 0.8492 | 0.8553 |
| 0.2835 | 6.0 | 732 | 0.2810 | 0.8697 | 0.8520 | 0.8253 | 0.8368 |
| 0.2517 | 7.0 | 854 | 0.2866 | 0.8872 | 0.8672 | 0.8577 | 0.8622 |
| 0.2374 | 8.0 | 976 | 0.2997 | 0.8797 | 0.8671 | 0.8349 | 0.8485 |
| 0.2293 | 9.0 | 1098 | 0.2909 | 0.8797 | 0.8600 | 0.8449 | 0.8518 |
| 0.2091 | 10.0 | 1220 | 0.2928 | 0.8822 | 0.8564 | 0.8617 | 0.8590 |
| 0.198 | 11.0 | 1342 | 0.2847 | 0.8797 | 0.8522 | 0.8624 | 0.8570 |
| 0.1906 | 12.0 | 1464 | 0.3120 | 0.8747 | 0.8586 | 0.8313 | 0.8431 |
| 0.1818 | 13.0 | 1586 | 0.2906 | 0.8772 | 0.8535 | 0.8481 | 0.8507 |
| 0.1756 | 14.0 | 1708 | 0.2810 | 0.8772 | 0.8524 | 0.8506 | 0.8515 |
| 0.174 | 15.0 | 1830 | 0.2829 | 0.8847 | 0.8634 | 0.8559 | 0.8595 |
| 0.1705 | 16.0 | 1952 | 0.2922 | 0.8822 | 0.8624 | 0.8492 | 0.8553 |
| 0.1509 | 17.0 | 2074 | 0.2991 | 0.8822 | 0.8596 | 0.8542 | 0.8568 |
| 0.1549 | 18.0 | 2196 | 0.3000 | 0.8822 | 0.8624 | 0.8492 | 0.8553 |
| 0.1469 | 19.0 | 2318 | 0.2943 | 0.8847 | 0.8609 | 0.8609 | 0.8609 |
| 0.1493 | 20.0 | 2440 | 0.3026 | 0.8822 | 0.8624 | 0.8492 | 0.8553 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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