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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-pl30-0
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-pl30-0
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: 0.2913
- Accuracy: 0.9048
- Precision: 0.8851
- Recall: 0.8851
- F1: 0.8851
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5457 | 1.0 | 122 | 0.4753 | 0.7193 | 0.6465 | 0.5964 | 0.6014 |
| 0.4518 | 2.0 | 244 | 0.4070 | 0.7970 | 0.7589 | 0.7864 | 0.7685 |
| 0.3461 | 3.0 | 366 | 0.3412 | 0.8421 | 0.8231 | 0.7808 | 0.7970 |
| 0.2958 | 4.0 | 488 | 0.3253 | 0.8546 | 0.8263 | 0.8196 | 0.8229 |
| 0.2659 | 5.0 | 610 | 0.2941 | 0.8822 | 0.8610 | 0.8517 | 0.8561 |
| 0.2482 | 6.0 | 732 | 0.2965 | 0.8772 | 0.8473 | 0.8681 | 0.8563 |
| 0.2264 | 7.0 | 854 | 0.2869 | 0.8747 | 0.8447 | 0.8638 | 0.8531 |
| 0.2218 | 8.0 | 976 | 0.2795 | 0.8997 | 0.8961 | 0.8566 | 0.8730 |
| 0.2106 | 9.0 | 1098 | 0.2705 | 0.8922 | 0.8673 | 0.8763 | 0.8716 |
| 0.1981 | 10.0 | 1220 | 0.2751 | 0.9073 | 0.8920 | 0.8819 | 0.8867 |
| 0.1802 | 11.0 | 1342 | 0.2745 | 0.9048 | 0.8826 | 0.8901 | 0.8862 |
| 0.1828 | 12.0 | 1464 | 0.2799 | 0.9073 | 0.8957 | 0.8769 | 0.8855 |
| 0.1707 | 13.0 | 1586 | 0.2739 | 0.9098 | 0.8960 | 0.8837 | 0.8895 |
| 0.1606 | 14.0 | 1708 | 0.2868 | 0.9073 | 0.8862 | 0.8919 | 0.8890 |
| 0.1499 | 15.0 | 1830 | 0.2930 | 0.9023 | 0.8828 | 0.8808 | 0.8818 |
| 0.1555 | 16.0 | 1952 | 0.3041 | 0.8947 | 0.8682 | 0.8855 | 0.8760 |
| 0.1396 | 17.0 | 2074 | 0.2876 | 0.9023 | 0.8814 | 0.8833 | 0.8824 |
| 0.1477 | 18.0 | 2196 | 0.2900 | 0.9048 | 0.8865 | 0.8826 | 0.8845 |
| 0.1434 | 19.0 | 2318 | 0.2917 | 0.9048 | 0.8851 | 0.8851 | 0.8851 |
| 0.1386 | 20.0 | 2440 | 0.2913 | 0.9048 | 0.8851 | 0.8851 | 0.8851 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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