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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-r2a2d0.1-1
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-r2a2d0.1-1
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: 0.3681
- Accuracy: 0.8396
- Precision: 0.8141
- Recall: 0.7865
- F1: 0.7980
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.566 | 1.0 | 122 | 0.5211 | 0.7168 | 0.6521 | 0.6396 | 0.6444 |
| 0.5148 | 2.0 | 244 | 0.5169 | 0.7243 | 0.6791 | 0.6974 | 0.6850 |
| 0.4927 | 3.0 | 366 | 0.4861 | 0.7544 | 0.7017 | 0.6887 | 0.6942 |
| 0.4627 | 4.0 | 488 | 0.4656 | 0.7619 | 0.7120 | 0.7065 | 0.7091 |
| 0.4504 | 5.0 | 610 | 0.4611 | 0.7544 | 0.7120 | 0.7337 | 0.7193 |
| 0.4276 | 6.0 | 732 | 0.4303 | 0.7895 | 0.7461 | 0.7410 | 0.7434 |
| 0.4176 | 7.0 | 854 | 0.4163 | 0.7945 | 0.7521 | 0.7546 | 0.7533 |
| 0.397 | 8.0 | 976 | 0.3960 | 0.8170 | 0.7814 | 0.7680 | 0.7741 |
| 0.3904 | 9.0 | 1098 | 0.3940 | 0.8271 | 0.7969 | 0.7726 | 0.7829 |
| 0.3743 | 10.0 | 1220 | 0.3900 | 0.8271 | 0.7994 | 0.7676 | 0.7804 |
| 0.3632 | 11.0 | 1342 | 0.3848 | 0.8346 | 0.8062 | 0.7830 | 0.7929 |
| 0.3599 | 12.0 | 1464 | 0.3795 | 0.8271 | 0.7959 | 0.7751 | 0.7841 |
| 0.3597 | 13.0 | 1586 | 0.3765 | 0.8346 | 0.8136 | 0.7705 | 0.7867 |
| 0.3461 | 14.0 | 1708 | 0.3729 | 0.8321 | 0.8061 | 0.7737 | 0.7867 |
| 0.3432 | 15.0 | 1830 | 0.3714 | 0.8371 | 0.8101 | 0.7847 | 0.7955 |
| 0.333 | 16.0 | 1952 | 0.3706 | 0.8421 | 0.8181 | 0.7883 | 0.8006 |
| 0.3323 | 17.0 | 2074 | 0.3700 | 0.8396 | 0.8155 | 0.7840 | 0.7969 |
| 0.3337 | 18.0 | 2196 | 0.3687 | 0.8396 | 0.8141 | 0.7865 | 0.7980 |
| 0.3298 | 19.0 | 2318 | 0.3684 | 0.8396 | 0.8141 | 0.7865 | 0.7980 |
| 0.3309 | 20.0 | 2440 | 0.3681 | 0.8396 | 0.8141 | 0.7865 | 0.7980 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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