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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-r2a2d0.15
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-r2a2d0.15
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: 0.1346
- Precision: 0.7342
- Recall: 0.8652
- F1: 0.7943
- Accuracy: 0.9555
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.79 | 1.0 | 528 | 0.4638 | 0.3302 | 0.0813 | 0.1305 | 0.8595 |
| 0.3919 | 2.0 | 1056 | 0.2519 | 0.5954 | 0.6729 | 0.6318 | 0.9275 |
| 0.2386 | 3.0 | 1584 | 0.1927 | 0.6540 | 0.7908 | 0.7159 | 0.9382 |
| 0.193 | 4.0 | 2112 | 0.1677 | 0.6826 | 0.8234 | 0.7464 | 0.9448 |
| 0.1712 | 5.0 | 2640 | 0.1594 | 0.6959 | 0.8443 | 0.7629 | 0.9476 |
| 0.1596 | 6.0 | 3168 | 0.1544 | 0.7082 | 0.8559 | 0.7751 | 0.9498 |
| 0.1524 | 7.0 | 3696 | 0.1519 | 0.7012 | 0.8605 | 0.7728 | 0.9506 |
| 0.1452 | 8.0 | 4224 | 0.1461 | 0.7203 | 0.8605 | 0.7842 | 0.9522 |
| 0.1397 | 9.0 | 4752 | 0.1432 | 0.7263 | 0.8559 | 0.7858 | 0.9535 |
| 0.1369 | 10.0 | 5280 | 0.1394 | 0.7258 | 0.8536 | 0.7845 | 0.9539 |
| 0.1336 | 11.0 | 5808 | 0.1375 | 0.7321 | 0.8512 | 0.7872 | 0.9543 |
| 0.1305 | 12.0 | 6336 | 0.1375 | 0.7345 | 0.8536 | 0.7896 | 0.9547 |
| 0.1281 | 13.0 | 6864 | 0.1351 | 0.7330 | 0.8536 | 0.7887 | 0.9547 |
| 0.1252 | 14.0 | 7392 | 0.1360 | 0.7342 | 0.8652 | 0.7943 | 0.9553 |
| 0.124 | 15.0 | 7920 | 0.1364 | 0.7292 | 0.8559 | 0.7875 | 0.9541 |
| 0.1234 | 16.0 | 8448 | 0.1351 | 0.7260 | 0.8605 | 0.7876 | 0.9549 |
| 0.1224 | 17.0 | 8976 | 0.1357 | 0.7299 | 0.8652 | 0.7918 | 0.9549 |
| 0.1208 | 18.0 | 9504 | 0.1360 | 0.7333 | 0.8675 | 0.7948 | 0.9553 |
| 0.1201 | 19.0 | 10032 | 0.1350 | 0.7347 | 0.8675 | 0.7956 | 0.9555 |
| 0.1205 | 20.0 | 10560 | 0.1346 | 0.7342 | 0.8652 | 0.7943 | 0.9555 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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