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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-r8a0d0.1-0
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-r8a0d0.1-0
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: 0.3309
- Accuracy: 0.8672
- Precision: 0.8378
- Recall: 0.8460
- F1: 0.8417
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5622 | 1.0 | 122 | 0.5076 | 0.7193 | 0.6565 | 0.6464 | 0.6505 |
| 0.5003 | 2.0 | 244 | 0.4839 | 0.7469 | 0.7107 | 0.7409 | 0.7178 |
| 0.4614 | 3.0 | 366 | 0.4256 | 0.7719 | 0.7281 | 0.7436 | 0.7344 |
| 0.4022 | 4.0 | 488 | 0.3880 | 0.8170 | 0.7798 | 0.7756 | 0.7776 |
| 0.3678 | 5.0 | 610 | 0.4131 | 0.8020 | 0.7657 | 0.7974 | 0.7760 |
| 0.3376 | 6.0 | 732 | 0.3645 | 0.8321 | 0.7965 | 0.8037 | 0.7999 |
| 0.3268 | 7.0 | 854 | 0.3640 | 0.8346 | 0.7988 | 0.8180 | 0.8069 |
| 0.3044 | 8.0 | 976 | 0.3551 | 0.8346 | 0.7996 | 0.8055 | 0.8024 |
| 0.2984 | 9.0 | 1098 | 0.3509 | 0.8496 | 0.8169 | 0.8261 | 0.8212 |
| 0.2922 | 10.0 | 1220 | 0.3413 | 0.8521 | 0.8213 | 0.8229 | 0.8221 |
| 0.2666 | 11.0 | 1342 | 0.3494 | 0.8521 | 0.8193 | 0.8329 | 0.8254 |
| 0.2641 | 12.0 | 1464 | 0.3520 | 0.8546 | 0.8220 | 0.8372 | 0.8288 |
| 0.2694 | 13.0 | 1586 | 0.3358 | 0.8496 | 0.8202 | 0.8136 | 0.8167 |
| 0.2678 | 14.0 | 1708 | 0.3355 | 0.8647 | 0.8352 | 0.8417 | 0.8383 |
| 0.255 | 15.0 | 1830 | 0.3406 | 0.8647 | 0.8346 | 0.8442 | 0.8391 |
| 0.2482 | 16.0 | 1952 | 0.3370 | 0.8622 | 0.8309 | 0.8450 | 0.8373 |
| 0.2444 | 17.0 | 2074 | 0.3272 | 0.8697 | 0.8411 | 0.8478 | 0.8443 |
| 0.2521 | 18.0 | 2196 | 0.3319 | 0.8622 | 0.8314 | 0.8425 | 0.8365 |
| 0.2456 | 19.0 | 2318 | 0.3293 | 0.8697 | 0.8411 | 0.8478 | 0.8443 |
| 0.2458 | 20.0 | 2440 | 0.3309 | 0.8672 | 0.8378 | 0.8460 | 0.8417 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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