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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-r8
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-r8
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: 0.2908
- Accuracy: 0.8772
- Precision: 0.8535
- Recall: 0.8481
- F1: 0.8507
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.556 | 1.0 | 122 | 0.5325 | 0.7168 | 0.6617 | 0.6671 | 0.6641 |
| 0.5103 | 2.0 | 244 | 0.4822 | 0.7719 | 0.7715 | 0.6386 | 0.6524 |
| 0.4637 | 3.0 | 366 | 0.4245 | 0.8045 | 0.7715 | 0.7342 | 0.7480 |
| 0.4173 | 4.0 | 488 | 0.3898 | 0.8246 | 0.7888 | 0.7859 | 0.7873 |
| 0.3674 | 5.0 | 610 | 0.3571 | 0.8371 | 0.8059 | 0.7947 | 0.7999 |
| 0.3484 | 6.0 | 732 | 0.3432 | 0.8371 | 0.8038 | 0.8022 | 0.8030 |
| 0.3247 | 7.0 | 854 | 0.3299 | 0.8521 | 0.8271 | 0.8079 | 0.8164 |
| 0.3102 | 8.0 | 976 | 0.3260 | 0.8622 | 0.8510 | 0.8050 | 0.8228 |
| 0.2991 | 9.0 | 1098 | 0.3138 | 0.8571 | 0.8349 | 0.8114 | 0.8216 |
| 0.29 | 10.0 | 1220 | 0.3123 | 0.8546 | 0.8324 | 0.8071 | 0.8180 |
| 0.2778 | 11.0 | 1342 | 0.3065 | 0.8672 | 0.8423 | 0.8335 | 0.8377 |
| 0.2702 | 12.0 | 1464 | 0.3006 | 0.8571 | 0.8349 | 0.8114 | 0.8216 |
| 0.2664 | 13.0 | 1586 | 0.2996 | 0.8596 | 0.8316 | 0.8282 | 0.8298 |
| 0.264 | 14.0 | 1708 | 0.2987 | 0.8722 | 0.8437 | 0.8521 | 0.8477 |
| 0.254 | 15.0 | 1830 | 0.2951 | 0.8772 | 0.8514 | 0.8531 | 0.8522 |
| 0.2571 | 16.0 | 1952 | 0.2945 | 0.8672 | 0.8463 | 0.8260 | 0.8351 |
| 0.2511 | 17.0 | 2074 | 0.2918 | 0.8722 | 0.8463 | 0.8446 | 0.8454 |
| 0.2574 | 18.0 | 2196 | 0.2909 | 0.8747 | 0.8510 | 0.8438 | 0.8473 |
| 0.2508 | 19.0 | 2318 | 0.2907 | 0.8772 | 0.8535 | 0.8481 | 0.8507 |
| 0.2536 | 20.0 | 2440 | 0.2908 | 0.8772 | 0.8535 | 0.8481 | 0.8507 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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