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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-pl30-3
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-pl30-3
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: 0.2943
- Accuracy: 0.8922
- Precision: 0.8706
- Recall: 0.8687
- F1: 0.8697
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5452 | 1.0 | 122 | 0.4919 | 0.7469 | 0.6922 | 0.6459 | 0.6573 |
| 0.4299 | 2.0 | 244 | 0.4071 | 0.8070 | 0.7802 | 0.8285 | 0.7892 |
| 0.3291 | 3.0 | 366 | 0.3091 | 0.8672 | 0.8412 | 0.8360 | 0.8385 |
| 0.2887 | 4.0 | 488 | 0.3033 | 0.8521 | 0.8237 | 0.8154 | 0.8193 |
| 0.2579 | 5.0 | 610 | 0.2880 | 0.8647 | 0.8340 | 0.8467 | 0.8399 |
| 0.232 | 6.0 | 732 | 0.2919 | 0.8747 | 0.8443 | 0.8663 | 0.8537 |
| 0.2181 | 7.0 | 854 | 0.2797 | 0.8822 | 0.8574 | 0.8592 | 0.8583 |
| 0.2114 | 8.0 | 976 | 0.2828 | 0.8747 | 0.8488 | 0.8488 | 0.8488 |
| 0.199 | 9.0 | 1098 | 0.2835 | 0.8797 | 0.8522 | 0.8624 | 0.8570 |
| 0.189 | 10.0 | 1220 | 0.2816 | 0.8772 | 0.8547 | 0.8456 | 0.8500 |
| 0.1738 | 11.0 | 1342 | 0.2905 | 0.8822 | 0.8574 | 0.8592 | 0.8583 |
| 0.1688 | 12.0 | 1464 | 0.3152 | 0.8822 | 0.8674 | 0.8417 | 0.8529 |
| 0.1655 | 13.0 | 1586 | 0.2901 | 0.8697 | 0.8449 | 0.8378 | 0.8412 |
| 0.1467 | 14.0 | 1708 | 0.2955 | 0.8797 | 0.8515 | 0.8649 | 0.8577 |
| 0.1442 | 15.0 | 1830 | 0.2866 | 0.8822 | 0.8564 | 0.8617 | 0.8590 |
| 0.1419 | 16.0 | 1952 | 0.2902 | 0.8847 | 0.8599 | 0.8634 | 0.8616 |
| 0.1416 | 17.0 | 2074 | 0.2898 | 0.8897 | 0.8659 | 0.8695 | 0.8676 |
| 0.1389 | 18.0 | 2196 | 0.2956 | 0.8872 | 0.8658 | 0.8602 | 0.8629 |
| 0.1401 | 19.0 | 2318 | 0.2937 | 0.8922 | 0.8706 | 0.8687 | 0.8697 |
| 0.1348 | 20.0 | 2440 | 0.2943 | 0.8922 | 0.8706 | 0.8687 | 0.8697 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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