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---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl10-2
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-pl10-2
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: 0.3132
- Accuracy: 0.8947
- Precision: 0.8743
- Recall: 0.8705
- F1: 0.8724
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5494 | 1.0 | 122 | 0.5033 | 0.7318 | 0.6683 | 0.6278 | 0.6369 |
| 0.4551 | 2.0 | 244 | 0.4276 | 0.7769 | 0.7434 | 0.7797 | 0.7522 |
| 0.3731 | 3.0 | 366 | 0.3508 | 0.8396 | 0.8294 | 0.7665 | 0.7879 |
| 0.3032 | 4.0 | 488 | 0.3237 | 0.8672 | 0.8353 | 0.8635 | 0.8466 |
| 0.2718 | 5.0 | 610 | 0.3102 | 0.8722 | 0.8445 | 0.8496 | 0.8470 |
| 0.2642 | 6.0 | 732 | 0.3006 | 0.8747 | 0.8451 | 0.8613 | 0.8524 |
| 0.2394 | 7.0 | 854 | 0.3013 | 0.8722 | 0.8544 | 0.8296 | 0.8404 |
| 0.2234 | 8.0 | 976 | 0.2904 | 0.8797 | 0.8572 | 0.8499 | 0.8534 |
| 0.2098 | 9.0 | 1098 | 0.2984 | 0.8897 | 0.8625 | 0.8795 | 0.8701 |
| 0.2029 | 10.0 | 1220 | 0.3189 | 0.8822 | 0.8762 | 0.8317 | 0.8495 |
| 0.1917 | 11.0 | 1342 | 0.2848 | 0.8847 | 0.8648 | 0.8534 | 0.8588 |
| 0.1797 | 12.0 | 1464 | 0.3003 | 0.8772 | 0.8535 | 0.8481 | 0.8507 |
| 0.1658 | 13.0 | 1586 | 0.3010 | 0.8847 | 0.8634 | 0.8559 | 0.8595 |
| 0.1551 | 14.0 | 1708 | 0.3077 | 0.8847 | 0.8589 | 0.8659 | 0.8623 |
| 0.1517 | 15.0 | 1830 | 0.3014 | 0.8947 | 0.8789 | 0.8630 | 0.8704 |
| 0.1532 | 16.0 | 1952 | 0.3067 | 0.8947 | 0.8718 | 0.8755 | 0.8737 |
| 0.136 | 17.0 | 2074 | 0.3174 | 0.8897 | 0.8670 | 0.8670 | 0.8670 |
| 0.1438 | 18.0 | 2196 | 0.3129 | 0.8897 | 0.8682 | 0.8645 | 0.8663 |
| 0.1507 | 19.0 | 2318 | 0.3165 | 0.8922 | 0.8734 | 0.8637 | 0.8683 |
| 0.1326 | 20.0 | 2440 | 0.3132 | 0.8947 | 0.8743 | 0.8705 | 0.8724 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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