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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-r8-1
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. -->
# nerugm-lora-r8-1
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: 0.1742
- Precision: 0.6892
- Recall: 0.8266
- F1: 0.7516
- Accuracy: 0.9465
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2459 | 1.0 | 106 | 0.7376 | 0.0 | 0.0 | 0.0 | 0.8353 |
| 0.7125 | 2.0 | 212 | 0.6395 | 0.1667 | 0.0029 | 0.0057 | 0.8363 |
| 0.6362 | 3.0 | 318 | 0.5518 | 0.1739 | 0.0116 | 0.0217 | 0.8400 |
| 0.5564 | 4.0 | 424 | 0.4672 | 0.2688 | 0.0723 | 0.1139 | 0.8578 |
| 0.4714 | 5.0 | 530 | 0.3912 | 0.4363 | 0.2572 | 0.3236 | 0.8880 |
| 0.3978 | 6.0 | 636 | 0.3240 | 0.5348 | 0.4884 | 0.5106 | 0.9135 |
| 0.3365 | 7.0 | 742 | 0.2839 | 0.5784 | 0.6503 | 0.6122 | 0.9242 |
| 0.294 | 8.0 | 848 | 0.2507 | 0.6173 | 0.7225 | 0.6658 | 0.9319 |
| 0.2677 | 9.0 | 954 | 0.2320 | 0.6401 | 0.7659 | 0.6974 | 0.9356 |
| 0.2457 | 10.0 | 1060 | 0.2109 | 0.6618 | 0.7803 | 0.7162 | 0.9393 |
| 0.2339 | 11.0 | 1166 | 0.2022 | 0.6667 | 0.7919 | 0.7239 | 0.9405 |
| 0.2215 | 12.0 | 1272 | 0.1987 | 0.6802 | 0.8237 | 0.7451 | 0.9425 |
| 0.2125 | 13.0 | 1378 | 0.1899 | 0.6770 | 0.8179 | 0.7408 | 0.9433 |
| 0.2085 | 14.0 | 1484 | 0.1854 | 0.6843 | 0.8208 | 0.7464 | 0.9438 |
| 0.2002 | 15.0 | 1590 | 0.1797 | 0.6917 | 0.8237 | 0.7520 | 0.9460 |
| 0.2 | 16.0 | 1696 | 0.1779 | 0.6867 | 0.8237 | 0.7490 | 0.9453 |
| 0.1929 | 17.0 | 1802 | 0.1774 | 0.6842 | 0.8266 | 0.7487 | 0.9450 |
| 0.1932 | 18.0 | 1908 | 0.1761 | 0.6875 | 0.8266 | 0.7507 | 0.9458 |
| 0.1916 | 19.0 | 2014 | 0.1747 | 0.6892 | 0.8266 | 0.7516 | 0.9465 |
| 0.1887 | 20.0 | 2120 | 0.1742 | 0.6892 | 0.8266 | 0.7516 | 0.9465 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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