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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-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-base-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: 0.8105
- Accuracy: 0.9023
- Precision: 0.8828
- Recall: 0.8808
- F1: 0.8818
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4267 | 1.0 | 122 | 0.3586 | 0.8797 | 0.8892 | 0.8149 | 0.8409 |
| 0.2234 | 2.0 | 244 | 0.3668 | 0.8697 | 0.8395 | 0.8853 | 0.8539 |
| 0.126 | 3.0 | 366 | 0.4554 | 0.8922 | 0.8632 | 0.8938 | 0.8756 |
| 0.0886 | 4.0 | 488 | 0.4441 | 0.9073 | 0.8957 | 0.8769 | 0.8855 |
| 0.0611 | 5.0 | 610 | 0.4923 | 0.9048 | 0.8881 | 0.8801 | 0.8839 |
| 0.0366 | 6.0 | 732 | 0.6796 | 0.8997 | 0.8748 | 0.8891 | 0.8814 |
| 0.0358 | 7.0 | 854 | 0.5746 | 0.9048 | 0.8935 | 0.8726 | 0.8821 |
| 0.0272 | 8.0 | 976 | 0.5953 | 0.8947 | 0.8718 | 0.8755 | 0.8737 |
| 0.0231 | 9.0 | 1098 | 0.6506 | 0.8997 | 0.8891 | 0.8641 | 0.8752 |
| 0.0141 | 10.0 | 1220 | 0.6854 | 0.9023 | 0.8814 | 0.8833 | 0.8824 |
| 0.023 | 11.0 | 1342 | 0.7218 | 0.9023 | 0.8814 | 0.8833 | 0.8824 |
| 0.0067 | 12.0 | 1464 | 0.7695 | 0.9023 | 0.8814 | 0.8833 | 0.8824 |
| 0.0064 | 13.0 | 1586 | 0.9004 | 0.8797 | 0.8496 | 0.8749 | 0.8602 |
| 0.0103 | 14.0 | 1708 | 0.7978 | 0.9023 | 0.8792 | 0.8883 | 0.8835 |
| 0.0072 | 15.0 | 1830 | 0.8251 | 0.8997 | 0.8791 | 0.8791 | 0.8791 |
| 0.0054 | 16.0 | 1952 | 0.7715 | 0.9023 | 0.8814 | 0.8833 | 0.8824 |
| 0.0038 | 17.0 | 2074 | 0.7821 | 0.9073 | 0.8920 | 0.8819 | 0.8867 |
| 0.0021 | 18.0 | 2196 | 0.8211 | 0.8972 | 0.8754 | 0.8773 | 0.8764 |
| 0.0022 | 19.0 | 2318 | 0.8162 | 0.8997 | 0.8791 | 0.8791 | 0.8791 |
| 0.0027 | 20.0 | 2440 | 0.8105 | 0.9023 | 0.8828 | 0.8808 | 0.8818 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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