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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-lora-r4a0d0.15-0
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-lora-r4a0d0.15-0
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: 0.3513
- Accuracy: 0.8471
- Precision: 0.8147
- Recall: 0.8193
- F1: 0.8169
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5621 | 1.0 | 122 | 0.5100 | 0.7218 | 0.6593 | 0.6482 | 0.6527 |
| 0.5049 | 2.0 | 244 | 0.4890 | 0.7343 | 0.6945 | 0.7195 | 0.7011 |
| 0.4776 | 3.0 | 366 | 0.4480 | 0.7594 | 0.7150 | 0.7323 | 0.7216 |
| 0.4422 | 4.0 | 488 | 0.4104 | 0.7945 | 0.7524 | 0.7446 | 0.7482 |
| 0.4146 | 5.0 | 610 | 0.4257 | 0.7594 | 0.7202 | 0.7473 | 0.7283 |
| 0.3828 | 6.0 | 732 | 0.3869 | 0.8246 | 0.7880 | 0.7909 | 0.7894 |
| 0.3697 | 7.0 | 854 | 0.3959 | 0.8145 | 0.7766 | 0.7988 | 0.7854 |
| 0.3486 | 8.0 | 976 | 0.3808 | 0.8321 | 0.7961 | 0.8087 | 0.8018 |
| 0.3437 | 9.0 | 1098 | 0.3738 | 0.8271 | 0.7904 | 0.8001 | 0.7949 |
| 0.3317 | 10.0 | 1220 | 0.3643 | 0.8471 | 0.8159 | 0.8143 | 0.8151 |
| 0.3114 | 11.0 | 1342 | 0.3683 | 0.8271 | 0.7902 | 0.8051 | 0.7968 |
| 0.3035 | 12.0 | 1464 | 0.3660 | 0.8346 | 0.7988 | 0.8155 | 0.8061 |
| 0.3117 | 13.0 | 1586 | 0.3518 | 0.8471 | 0.8167 | 0.8118 | 0.8142 |
| 0.3048 | 14.0 | 1708 | 0.3533 | 0.8446 | 0.8115 | 0.8176 | 0.8144 |
| 0.2916 | 15.0 | 1830 | 0.3570 | 0.8421 | 0.8083 | 0.8158 | 0.8119 |
| 0.2832 | 16.0 | 1952 | 0.3579 | 0.8471 | 0.8138 | 0.8243 | 0.8187 |
| 0.284 | 17.0 | 2074 | 0.3496 | 0.8471 | 0.8153 | 0.8168 | 0.8160 |
| 0.2906 | 18.0 | 2196 | 0.3537 | 0.8446 | 0.8111 | 0.8201 | 0.8153 |
| 0.2805 | 19.0 | 2318 | 0.3505 | 0.8496 | 0.8186 | 0.8186 | 0.8186 |
| 0.2815 | 20.0 | 2440 | 0.3513 | 0.8471 | 0.8147 | 0.8193 | 0.8169 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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