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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-pl10-3
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-pl10-3
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: 0.3389
- Accuracy: 0.8822
- Precision: 0.8574
- Recall: 0.8592
- F1: 0.8583
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5509 | 1.0 | 122 | 0.4983 | 0.7393 | 0.6801 | 0.6406 | 0.6507 |
| 0.4511 | 2.0 | 244 | 0.4377 | 0.7769 | 0.7547 | 0.8022 | 0.7593 |
| 0.368 | 3.0 | 366 | 0.3260 | 0.8571 | 0.8381 | 0.8064 | 0.8196 |
| 0.3019 | 4.0 | 488 | 0.3036 | 0.8647 | 0.8410 | 0.8267 | 0.8333 |
| 0.2668 | 5.0 | 610 | 0.3192 | 0.8672 | 0.8372 | 0.8485 | 0.8425 |
| 0.2471 | 6.0 | 732 | 0.3059 | 0.8622 | 0.8305 | 0.8475 | 0.8380 |
| 0.2422 | 7.0 | 854 | 0.2950 | 0.8747 | 0.8451 | 0.8613 | 0.8524 |
| 0.2258 | 8.0 | 976 | 0.2928 | 0.8722 | 0.8463 | 0.8446 | 0.8454 |
| 0.2054 | 9.0 | 1098 | 0.3049 | 0.8797 | 0.8572 | 0.8499 | 0.8534 |
| 0.2009 | 10.0 | 1220 | 0.3013 | 0.8747 | 0.8488 | 0.8488 | 0.8488 |
| 0.1755 | 11.0 | 1342 | 0.3070 | 0.8822 | 0.8574 | 0.8592 | 0.8583 |
| 0.1821 | 12.0 | 1464 | 0.2995 | 0.8822 | 0.8596 | 0.8542 | 0.8568 |
| 0.1652 | 13.0 | 1586 | 0.3272 | 0.8847 | 0.8553 | 0.8809 | 0.8660 |
| 0.1566 | 14.0 | 1708 | 0.3336 | 0.8897 | 0.8609 | 0.8870 | 0.8719 |
| 0.1634 | 15.0 | 1830 | 0.3150 | 0.8847 | 0.8589 | 0.8659 | 0.8623 |
| 0.1496 | 16.0 | 1952 | 0.3321 | 0.8922 | 0.8706 | 0.8687 | 0.8697 |
| 0.1355 | 17.0 | 2074 | 0.3276 | 0.8847 | 0.8599 | 0.8634 | 0.8616 |
| 0.1477 | 18.0 | 2196 | 0.3365 | 0.8797 | 0.8530 | 0.8599 | 0.8563 |
| 0.1317 | 19.0 | 2318 | 0.3385 | 0.8822 | 0.8574 | 0.8592 | 0.8583 |
| 0.1267 | 20.0 | 2440 | 0.3389 | 0.8822 | 0.8574 | 0.8592 | 0.8583 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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