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---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-lora-r16-2
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. -->
# nerugm-lora-r16-2
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: 0.1626
- Precision: 0.6848
- Recall: 0.8525
- F1: 0.7595
- Accuracy: 0.9472
## 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: 16
- eval_batch_size: 64
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.1483 | 1.0 | 106 | 0.6900 | 0.0 | 0.0 | 0.0 | 0.8449 |
| 0.6875 | 2.0 | 212 | 0.5737 | 0.0 | 0.0 | 0.0 | 0.8464 |
| 0.5874 | 3.0 | 318 | 0.4661 | 0.2692 | 0.0619 | 0.1007 | 0.8634 |
| 0.4729 | 4.0 | 424 | 0.3599 | 0.4753 | 0.3127 | 0.3772 | 0.8982 |
| 0.3692 | 5.0 | 530 | 0.2940 | 0.5714 | 0.6136 | 0.5917 | 0.9247 |
| 0.3058 | 6.0 | 636 | 0.2527 | 0.6110 | 0.7227 | 0.6622 | 0.9335 |
| 0.2636 | 7.0 | 742 | 0.2246 | 0.6402 | 0.7611 | 0.6954 | 0.9375 |
| 0.24 | 8.0 | 848 | 0.2091 | 0.6578 | 0.8053 | 0.7241 | 0.9417 |
| 0.2228 | 9.0 | 954 | 0.1986 | 0.6404 | 0.8142 | 0.7169 | 0.9402 |
| 0.2105 | 10.0 | 1060 | 0.1821 | 0.6611 | 0.8230 | 0.7332 | 0.9417 |
| 0.2007 | 11.0 | 1166 | 0.1794 | 0.6675 | 0.8289 | 0.7395 | 0.9432 |
| 0.195 | 12.0 | 1272 | 0.1808 | 0.6597 | 0.8407 | 0.7393 | 0.9430 |
| 0.19 | 13.0 | 1378 | 0.1690 | 0.6787 | 0.8289 | 0.7463 | 0.9460 |
| 0.1835 | 14.0 | 1484 | 0.1631 | 0.6870 | 0.8289 | 0.7513 | 0.9477 |
| 0.1821 | 15.0 | 1590 | 0.1671 | 0.6835 | 0.8407 | 0.7540 | 0.9472 |
| 0.1774 | 16.0 | 1696 | 0.1668 | 0.6896 | 0.8584 | 0.7648 | 0.9472 |
| 0.1764 | 17.0 | 1802 | 0.1635 | 0.6899 | 0.8466 | 0.7603 | 0.9477 |
| 0.1729 | 18.0 | 1908 | 0.1654 | 0.6856 | 0.8555 | 0.7612 | 0.9472 |
| 0.1726 | 19.0 | 2014 | 0.1628 | 0.6872 | 0.8555 | 0.7622 | 0.9477 |
| 0.1684 | 20.0 | 2120 | 0.1626 | 0.6848 | 0.8525 | 0.7595 | 0.9472 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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