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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-r16-1
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-r16-1
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: 0.2834
- Accuracy: 0.8822
- Precision: 0.8574
- Recall: 0.8592
- F1: 0.8583
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5612 | 1.0 | 122 | 0.5258 | 0.7268 | 0.6614 | 0.6317 | 0.6399 |
| 0.4935 | 2.0 | 244 | 0.4827 | 0.7494 | 0.7127 | 0.7427 | 0.7201 |
| 0.428 | 3.0 | 366 | 0.3863 | 0.8246 | 0.7874 | 0.7984 | 0.7924 |
| 0.363 | 4.0 | 488 | 0.3415 | 0.8446 | 0.8207 | 0.7926 | 0.8043 |
| 0.3321 | 5.0 | 610 | 0.3417 | 0.8521 | 0.8186 | 0.8429 | 0.8285 |
| 0.3086 | 6.0 | 732 | 0.3376 | 0.8496 | 0.8158 | 0.8386 | 0.8253 |
| 0.2899 | 7.0 | 854 | 0.3156 | 0.8722 | 0.8453 | 0.8471 | 0.8462 |
| 0.2828 | 8.0 | 976 | 0.3073 | 0.8722 | 0.8463 | 0.8446 | 0.8454 |
| 0.2638 | 9.0 | 1098 | 0.3156 | 0.8622 | 0.8300 | 0.8525 | 0.8395 |
| 0.2628 | 10.0 | 1220 | 0.3002 | 0.8797 | 0.8522 | 0.8624 | 0.8570 |
| 0.249 | 11.0 | 1342 | 0.2935 | 0.8797 | 0.8572 | 0.8499 | 0.8534 |
| 0.2429 | 12.0 | 1464 | 0.2938 | 0.8772 | 0.8514 | 0.8531 | 0.8522 |
| 0.2406 | 13.0 | 1586 | 0.2902 | 0.8797 | 0.8585 | 0.8474 | 0.8526 |
| 0.2377 | 14.0 | 1708 | 0.2889 | 0.8722 | 0.8437 | 0.8521 | 0.8477 |
| 0.2257 | 15.0 | 1830 | 0.2848 | 0.8797 | 0.8530 | 0.8599 | 0.8563 |
| 0.2215 | 16.0 | 1952 | 0.2862 | 0.8747 | 0.8451 | 0.8613 | 0.8524 |
| 0.2297 | 17.0 | 2074 | 0.2833 | 0.8822 | 0.8610 | 0.8517 | 0.8561 |
| 0.2263 | 18.0 | 2196 | 0.2854 | 0.8772 | 0.8483 | 0.8631 | 0.8550 |
| 0.2194 | 19.0 | 2318 | 0.2833 | 0.8797 | 0.8539 | 0.8574 | 0.8556 |
| 0.214 | 20.0 | 2440 | 0.2834 | 0.8822 | 0.8574 | 0.8592 | 0.8583 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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