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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-1
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-1
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: 0.7908
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3889 | 1.0 | 122 | 0.4200 | 0.8045 | 0.8255 | 0.6867 | 0.7110 |
| 0.2335 | 2.0 | 244 | 0.3136 | 0.8922 | 0.8644 | 0.8863 | 0.8739 |
| 0.1411 | 3.0 | 366 | 0.3569 | 0.8972 | 0.8781 | 0.8723 | 0.8751 |
| 0.1078 | 4.0 | 488 | 0.3537 | 0.9148 | 0.8923 | 0.9072 | 0.8992 |
| 0.0822 | 5.0 | 610 | 0.5069 | 0.8797 | 0.8795 | 0.8224 | 0.8439 |
| 0.0529 | 6.0 | 732 | 0.4262 | 0.9073 | 0.8862 | 0.8919 | 0.8890 |
| 0.0365 | 7.0 | 854 | 0.5586 | 0.8972 | 0.8743 | 0.8798 | 0.8770 |
| 0.033 | 8.0 | 976 | 0.5012 | 0.8947 | 0.8870 | 0.8530 | 0.8675 |
| 0.0248 | 9.0 | 1098 | 0.5833 | 0.8922 | 0.8873 | 0.8462 | 0.8631 |
| 0.0123 | 10.0 | 1220 | 0.6611 | 0.9023 | 0.8858 | 0.8758 | 0.8806 |
| 0.0088 | 11.0 | 1342 | 0.6936 | 0.8947 | 0.8847 | 0.8555 | 0.8682 |
| 0.0074 | 12.0 | 1464 | 0.6790 | 0.9023 | 0.8858 | 0.8758 | 0.8806 |
| 0.0141 | 13.0 | 1586 | 0.6981 | 0.8972 | 0.8830 | 0.8648 | 0.8731 |
| 0.0034 | 14.0 | 1708 | 0.7145 | 0.8972 | 0.8781 | 0.8723 | 0.8751 |
| 0.0059 | 15.0 | 1830 | 0.7304 | 0.8997 | 0.8871 | 0.8666 | 0.8759 |
| 0.0056 | 16.0 | 1952 | 0.7518 | 0.8997 | 0.8778 | 0.8816 | 0.8797 |
| 0.0039 | 17.0 | 2074 | 0.7390 | 0.9023 | 0.8893 | 0.8708 | 0.8793 |
| 0.004 | 18.0 | 2196 | 0.7641 | 0.9023 | 0.8875 | 0.8733 | 0.8799 |
| 0.007 | 19.0 | 2318 | 0.7848 | 0.9023 | 0.8875 | 0.8733 | 0.8799 |
| 0.0042 | 20.0 | 2440 | 0.7908 | 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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