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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-r4a1d0.05
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-r4a1d0.05
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: 0.1305
- Precision: 0.7407
- Recall: 0.8698
- F1: 0.8001
- Accuracy: 0.9579
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7682 | 1.0 | 528 | 0.4394 | 0.4048 | 0.1185 | 0.1834 | 0.8663 |
| 0.3466 | 2.0 | 1056 | 0.2217 | 0.6022 | 0.7379 | 0.6632 | 0.9327 |
| 0.2131 | 3.0 | 1584 | 0.1728 | 0.6765 | 0.8396 | 0.7493 | 0.9428 |
| 0.1759 | 4.0 | 2112 | 0.1509 | 0.7221 | 0.8559 | 0.7833 | 0.9516 |
| 0.1563 | 5.0 | 2640 | 0.1422 | 0.7303 | 0.8605 | 0.7901 | 0.9533 |
| 0.1464 | 6.0 | 3168 | 0.1429 | 0.7202 | 0.8722 | 0.7890 | 0.9541 |
| 0.1394 | 7.0 | 3696 | 0.1440 | 0.7153 | 0.8745 | 0.7869 | 0.9525 |
| 0.1325 | 8.0 | 4224 | 0.1398 | 0.7274 | 0.8791 | 0.7961 | 0.9553 |
| 0.1269 | 9.0 | 4752 | 0.1341 | 0.7420 | 0.8675 | 0.7999 | 0.9579 |
| 0.124 | 10.0 | 5280 | 0.1331 | 0.7379 | 0.8768 | 0.8014 | 0.9565 |
| 0.1194 | 11.0 | 5808 | 0.1329 | 0.7389 | 0.8815 | 0.8039 | 0.9569 |
| 0.1171 | 12.0 | 6336 | 0.1337 | 0.7384 | 0.8791 | 0.8027 | 0.9567 |
| 0.1153 | 13.0 | 6864 | 0.1294 | 0.7447 | 0.8745 | 0.8044 | 0.9587 |
| 0.1119 | 14.0 | 7392 | 0.1310 | 0.7472 | 0.8791 | 0.8078 | 0.9573 |
| 0.1109 | 15.0 | 7920 | 0.1312 | 0.7457 | 0.8722 | 0.8040 | 0.9579 |
| 0.1102 | 16.0 | 8448 | 0.1309 | 0.7442 | 0.8791 | 0.8061 | 0.9581 |
| 0.1095 | 17.0 | 8976 | 0.1314 | 0.7447 | 0.8815 | 0.8073 | 0.9587 |
| 0.1073 | 18.0 | 9504 | 0.1323 | 0.7403 | 0.8745 | 0.8018 | 0.9577 |
| 0.107 | 19.0 | 10032 | 0.1300 | 0.7407 | 0.8698 | 0.8001 | 0.9581 |
| 0.1073 | 20.0 | 10560 | 0.1305 | 0.7407 | 0.8698 | 0.8001 | 0.9579 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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