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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-pl20-0
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-pl20-0
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: 0.2882
- Accuracy: 0.9073
- Precision: 0.8875
- Recall: 0.8894
- F1: 0.8884
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5417 | 1.0 | 122 | 0.4732 | 0.7544 | 0.7028 | 0.6612 | 0.6731 |
| 0.4395 | 2.0 | 244 | 0.4128 | 0.7920 | 0.7613 | 0.8028 | 0.7705 |
| 0.3319 | 3.0 | 366 | 0.3230 | 0.8647 | 0.8439 | 0.8217 | 0.8315 |
| 0.2873 | 4.0 | 488 | 0.3222 | 0.8521 | 0.8201 | 0.8279 | 0.8238 |
| 0.2571 | 5.0 | 610 | 0.2968 | 0.8722 | 0.8431 | 0.8546 | 0.8484 |
| 0.2443 | 6.0 | 732 | 0.2918 | 0.8672 | 0.8353 | 0.8635 | 0.8466 |
| 0.2256 | 7.0 | 854 | 0.2982 | 0.8647 | 0.8325 | 0.8642 | 0.8447 |
| 0.2172 | 8.0 | 976 | 0.2722 | 0.8922 | 0.8826 | 0.8512 | 0.8647 |
| 0.2049 | 9.0 | 1098 | 0.2648 | 0.8947 | 0.8698 | 0.8805 | 0.8749 |
| 0.1914 | 10.0 | 1220 | 0.2680 | 0.9073 | 0.8977 | 0.8744 | 0.8849 |
| 0.1724 | 11.0 | 1342 | 0.2645 | 0.8997 | 0.8757 | 0.8866 | 0.8808 |
| 0.1689 | 12.0 | 1464 | 0.2746 | 0.8997 | 0.8740 | 0.8916 | 0.8819 |
| 0.1473 | 13.0 | 1586 | 0.2837 | 0.9048 | 0.9002 | 0.8651 | 0.8801 |
| 0.1577 | 14.0 | 1708 | 0.2892 | 0.9023 | 0.8773 | 0.8933 | 0.8846 |
| 0.1468 | 15.0 | 1830 | 0.2789 | 0.9023 | 0.8802 | 0.8858 | 0.8830 |
| 0.1473 | 16.0 | 1952 | 0.2852 | 0.8972 | 0.8732 | 0.8823 | 0.8776 |
| 0.1274 | 17.0 | 2074 | 0.2858 | 0.9048 | 0.8838 | 0.8876 | 0.8857 |
| 0.1318 | 18.0 | 2196 | 0.2927 | 0.8997 | 0.8767 | 0.8841 | 0.8803 |
| 0.1355 | 19.0 | 2318 | 0.2884 | 0.9073 | 0.8875 | 0.8894 | 0.8884 |
| 0.1367 | 20.0 | 2440 | 0.2882 | 0.9073 | 0.8875 | 0.8894 | 0.8884 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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