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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-base-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-base-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: 0.8030
- Accuracy: 0.9023
- Precision: 0.8875
- Recall: 0.8733
- F1: 0.8799
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4092 | 1.0 | 122 | 0.3457 | 0.8521 | 0.8930 | 0.7554 | 0.7892 |
| 0.2282 | 2.0 | 244 | 0.2584 | 0.8922 | 0.8749 | 0.8612 | 0.8676 |
| 0.138 | 3.0 | 366 | 0.4417 | 0.8797 | 0.8825 | 0.8199 | 0.8430 |
| 0.0837 | 4.0 | 488 | 0.4037 | 0.9023 | 0.8893 | 0.8708 | 0.8793 |
| 0.0426 | 5.0 | 610 | 0.5462 | 0.9048 | 0.8806 | 0.8951 | 0.8873 |
| 0.0502 | 6.0 | 732 | 0.5626 | 0.8897 | 0.8618 | 0.8820 | 0.8707 |
| 0.0242 | 7.0 | 854 | 0.6241 | 0.9073 | 0.8977 | 0.8744 | 0.8849 |
| 0.0217 | 8.0 | 976 | 0.7096 | 0.8872 | 0.8579 | 0.8852 | 0.8692 |
| 0.0229 | 9.0 | 1098 | 0.6115 | 0.9123 | 0.8910 | 0.9004 | 0.8955 |
| 0.0109 | 10.0 | 1220 | 0.7575 | 0.8972 | 0.8796 | 0.8698 | 0.8745 |
| 0.0068 | 11.0 | 1342 | 0.7537 | 0.9073 | 0.8938 | 0.8794 | 0.8861 |
| 0.0131 | 12.0 | 1464 | 0.7247 | 0.8972 | 0.8732 | 0.8823 | 0.8776 |
| 0.0101 | 13.0 | 1586 | 0.7928 | 0.8972 | 0.8754 | 0.8773 | 0.8764 |
| 0.0061 | 14.0 | 1708 | 0.7849 | 0.9073 | 0.8875 | 0.8894 | 0.8884 |
| 0.0135 | 15.0 | 1830 | 0.7816 | 0.8972 | 0.8830 | 0.8648 | 0.8731 |
| 0.0081 | 16.0 | 1952 | 0.7727 | 0.8972 | 0.8767 | 0.8748 | 0.8757 |
| 0.0027 | 17.0 | 2074 | 0.8128 | 0.8972 | 0.8754 | 0.8773 | 0.8764 |
| 0.0041 | 18.0 | 2196 | 0.8081 | 0.9023 | 0.8828 | 0.8808 | 0.8818 |
| 0.0018 | 19.0 | 2318 | 0.8039 | 0.9023 | 0.8893 | 0.8708 | 0.8793 |
| 0.0025 | 20.0 | 2440 | 0.8030 | 0.9023 | 0.8875 | 0.8733 | 0.8799 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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