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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-rad
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-rad
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: 0.1778
- Precision: 0.7975
- Recall: 0.8698
- F1: 0.8321
- Accuracy: 0.9608
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4434 | 1.0 | 528 | 0.1630 | 0.6799 | 0.8629 | 0.7606 | 0.9456 |
| 0.1462 | 2.0 | 1056 | 0.1294 | 0.7481 | 0.8768 | 0.8074 | 0.9567 |
| 0.1183 | 3.0 | 1584 | 0.1378 | 0.7521 | 0.8815 | 0.8117 | 0.9569 |
| 0.1012 | 4.0 | 2112 | 0.1359 | 0.7720 | 0.8815 | 0.8231 | 0.9597 |
| 0.0884 | 5.0 | 2640 | 0.1266 | 0.7930 | 0.8815 | 0.8349 | 0.9622 |
| 0.0793 | 6.0 | 3168 | 0.1409 | 0.8031 | 0.8815 | 0.8404 | 0.9610 |
| 0.072 | 7.0 | 3696 | 0.1546 | 0.7704 | 0.8815 | 0.8222 | 0.9589 |
| 0.067 | 8.0 | 4224 | 0.1433 | 0.7980 | 0.8722 | 0.8334 | 0.9608 |
| 0.0607 | 9.0 | 4752 | 0.1468 | 0.7864 | 0.8815 | 0.8312 | 0.9599 |
| 0.0562 | 10.0 | 5280 | 0.1497 | 0.7783 | 0.8815 | 0.8267 | 0.9612 |
| 0.0506 | 11.0 | 5808 | 0.1600 | 0.7938 | 0.8768 | 0.8332 | 0.9595 |
| 0.0483 | 12.0 | 6336 | 0.1596 | 0.7950 | 0.8745 | 0.8329 | 0.9608 |
| 0.0443 | 13.0 | 6864 | 0.1596 | 0.7786 | 0.8745 | 0.8238 | 0.9606 |
| 0.0421 | 14.0 | 7392 | 0.1650 | 0.7971 | 0.8768 | 0.8351 | 0.9612 |
| 0.0395 | 15.0 | 7920 | 0.1693 | 0.7908 | 0.8698 | 0.8284 | 0.9603 |
| 0.0375 | 16.0 | 8448 | 0.1725 | 0.7926 | 0.8791 | 0.8336 | 0.9595 |
| 0.0358 | 17.0 | 8976 | 0.1789 | 0.7975 | 0.8698 | 0.8321 | 0.9612 |
| 0.0339 | 18.0 | 9504 | 0.1782 | 0.7821 | 0.8675 | 0.8226 | 0.9601 |
| 0.0327 | 19.0 | 10032 | 0.1743 | 0.8010 | 0.8698 | 0.8340 | 0.9620 |
| 0.0327 | 20.0 | 10560 | 0.1778 | 0.7975 | 0.8698 | 0.8321 | 0.9608 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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