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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-pl10-0
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-pl10-0
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: 0.2766
- Accuracy: 0.9023
- Precision: 0.8802
- Recall: 0.8858
- F1: 0.8830
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5459 | 1.0 | 122 | 0.4763 | 0.7393 | 0.6804 | 0.6356 | 0.6459 |
| 0.4528 | 2.0 | 244 | 0.4306 | 0.7845 | 0.7630 | 0.8125 | 0.7678 |
| 0.3653 | 3.0 | 366 | 0.3335 | 0.8622 | 0.8533 | 0.8025 | 0.8217 |
| 0.2987 | 4.0 | 488 | 0.3357 | 0.8546 | 0.8246 | 0.8246 | 0.8246 |
| 0.2746 | 5.0 | 610 | 0.3401 | 0.8546 | 0.8217 | 0.8547 | 0.8339 |
| 0.2477 | 6.0 | 732 | 0.3323 | 0.8496 | 0.8176 | 0.8586 | 0.8308 |
| 0.24 | 7.0 | 854 | 0.3171 | 0.8647 | 0.8325 | 0.8642 | 0.8447 |
| 0.2069 | 8.0 | 976 | 0.2770 | 0.8922 | 0.8734 | 0.8637 | 0.8683 |
| 0.2197 | 9.0 | 1098 | 0.3091 | 0.8672 | 0.8356 | 0.8735 | 0.8491 |
| 0.2005 | 10.0 | 1220 | 0.2552 | 0.9023 | 0.8842 | 0.8783 | 0.8812 |
| 0.1867 | 11.0 | 1342 | 0.2727 | 0.9048 | 0.8816 | 0.8926 | 0.8868 |
| 0.1722 | 12.0 | 1464 | 0.2739 | 0.8922 | 0.8657 | 0.8813 | 0.8728 |
| 0.161 | 13.0 | 1586 | 0.2714 | 0.8997 | 0.8852 | 0.8691 | 0.8765 |
| 0.1684 | 14.0 | 1708 | 0.2774 | 0.8972 | 0.8723 | 0.8848 | 0.8781 |
| 0.1548 | 15.0 | 1830 | 0.2742 | 0.8997 | 0.8767 | 0.8841 | 0.8803 |
| 0.1526 | 16.0 | 1952 | 0.2970 | 0.8872 | 0.8574 | 0.8902 | 0.8703 |
| 0.1467 | 17.0 | 2074 | 0.2729 | 0.8897 | 0.8618 | 0.8820 | 0.8707 |
| 0.1484 | 18.0 | 2196 | 0.2739 | 0.8972 | 0.8723 | 0.8848 | 0.8781 |
| 0.1434 | 19.0 | 2318 | 0.2729 | 0.8997 | 0.8778 | 0.8816 | 0.8797 |
| 0.1354 | 20.0 | 2440 | 0.2766 | 0.9023 | 0.8802 | 0.8858 | 0.8830 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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