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---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r4a0d0.05-1
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-lora-r4a0d0.05-1
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: 0.3356
- Accuracy: 0.8622
- Precision: 0.8399
- Recall: 0.8200
- F1: 0.8289
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5657 | 1.0 | 122 | 0.5182 | 0.7243 | 0.6604 | 0.6424 | 0.6488 |
| 0.5109 | 2.0 | 244 | 0.5051 | 0.7243 | 0.6748 | 0.6874 | 0.6796 |
| 0.48 | 3.0 | 366 | 0.4643 | 0.7569 | 0.7047 | 0.6880 | 0.6948 |
| 0.434 | 4.0 | 488 | 0.4281 | 0.7920 | 0.7497 | 0.7378 | 0.7431 |
| 0.4106 | 5.0 | 610 | 0.4194 | 0.7920 | 0.7528 | 0.7778 | 0.7618 |
| 0.3812 | 6.0 | 732 | 0.3936 | 0.8296 | 0.8008 | 0.7744 | 0.7854 |
| 0.3689 | 7.0 | 854 | 0.3700 | 0.8521 | 0.8220 | 0.8204 | 0.8212 |
| 0.3489 | 8.0 | 976 | 0.3656 | 0.8346 | 0.8088 | 0.7780 | 0.7905 |
| 0.3502 | 9.0 | 1098 | 0.3640 | 0.8371 | 0.8101 | 0.7847 | 0.7955 |
| 0.3349 | 10.0 | 1220 | 0.3608 | 0.8346 | 0.8074 | 0.7805 | 0.7917 |
| 0.3189 | 11.0 | 1342 | 0.3574 | 0.8396 | 0.8128 | 0.7890 | 0.7992 |
| 0.3121 | 12.0 | 1464 | 0.3547 | 0.8471 | 0.8175 | 0.8093 | 0.8132 |
| 0.3181 | 13.0 | 1586 | 0.3478 | 0.8521 | 0.8332 | 0.7979 | 0.8122 |
| 0.3092 | 14.0 | 1708 | 0.3435 | 0.8596 | 0.8374 | 0.8157 | 0.8253 |
| 0.3018 | 15.0 | 1830 | 0.3466 | 0.8546 | 0.8296 | 0.8121 | 0.8200 |
| 0.2955 | 16.0 | 1952 | 0.3365 | 0.8596 | 0.8347 | 0.8207 | 0.8272 |
| 0.2917 | 17.0 | 2074 | 0.3353 | 0.8596 | 0.8374 | 0.8157 | 0.8253 |
| 0.2956 | 18.0 | 2196 | 0.3379 | 0.8596 | 0.8360 | 0.8182 | 0.8262 |
| 0.2899 | 19.0 | 2318 | 0.3353 | 0.8647 | 0.8455 | 0.8192 | 0.8306 |
| 0.2885 | 20.0 | 2440 | 0.3356 | 0.8622 | 0.8399 | 0.8200 | 0.8289 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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