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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-0
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-0
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: 0.2808
- Accuracy: 0.8797
- Precision: 0.8504
- Recall: 0.8699
- F1: 0.8590
## 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: 1
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5437 | 1.0 | 122 | 0.4910 | 0.7368 | 0.7052 | 0.5813 | 0.5755 |
| 0.4527 | 2.0 | 244 | 0.3825 | 0.8321 | 0.7972 | 0.7987 | 0.7979 |
| 0.3443 | 3.0 | 366 | 0.3294 | 0.8622 | 0.8373 | 0.8250 | 0.8307 |
| 0.3026 | 4.0 | 488 | 0.3161 | 0.8722 | 0.8474 | 0.8421 | 0.8446 |
| 0.2848 | 5.0 | 610 | 0.3151 | 0.8647 | 0.8336 | 0.8492 | 0.8406 |
| 0.2605 | 6.0 | 732 | 0.2879 | 0.8772 | 0.8524 | 0.8506 | 0.8515 |
| 0.2292 | 7.0 | 854 | 0.2645 | 0.8797 | 0.8560 | 0.8524 | 0.8541 |
| 0.2196 | 8.0 | 976 | 0.2868 | 0.8772 | 0.8470 | 0.8706 | 0.8570 |
| 0.2076 | 9.0 | 1098 | 0.2795 | 0.8797 | 0.8496 | 0.8749 | 0.8602 |
| 0.201 | 10.0 | 1220 | 0.3010 | 0.8772 | 0.8465 | 0.8756 | 0.8582 |
| 0.1919 | 11.0 | 1342 | 0.2928 | 0.8747 | 0.8436 | 0.8738 | 0.8556 |
| 0.1866 | 12.0 | 1464 | 0.2660 | 0.8922 | 0.8644 | 0.8863 | 0.8739 |
| 0.1721 | 13.0 | 1586 | 0.2594 | 0.8922 | 0.8657 | 0.8813 | 0.8728 |
| 0.1734 | 14.0 | 1708 | 0.2487 | 0.8847 | 0.8581 | 0.8684 | 0.8629 |
| 0.1601 | 15.0 | 1830 | 0.2958 | 0.8847 | 0.8550 | 0.8834 | 0.8666 |
| 0.1586 | 16.0 | 1952 | 0.2719 | 0.8822 | 0.8548 | 0.8667 | 0.8603 |
| 0.1486 | 17.0 | 2074 | 0.2737 | 0.8772 | 0.8489 | 0.8606 | 0.8544 |
| 0.1474 | 18.0 | 2196 | 0.2678 | 0.8872 | 0.8634 | 0.8652 | 0.8643 |
| 0.1511 | 19.0 | 2318 | 0.2719 | 0.8797 | 0.8509 | 0.8674 | 0.8583 |
| 0.1296 | 20.0 | 2440 | 0.2808 | 0.8797 | 0.8504 | 0.8699 | 0.8590 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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