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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-r2a1d0.05
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-r2a1d0.05
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: 0.1346
- Precision: 0.7366
- Recall: 0.8629
- F1: 0.7948
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7885 | 1.0 | 528 | 0.4616 | 0.3182 | 0.0813 | 0.1296 | 0.8599 |
| 0.3921 | 2.0 | 1056 | 0.2524 | 0.6053 | 0.6798 | 0.6404 | 0.9273 |
| 0.2392 | 3.0 | 1584 | 0.1932 | 0.6500 | 0.7844 | 0.7109 | 0.9382 |
| 0.1931 | 4.0 | 2112 | 0.1676 | 0.6905 | 0.8234 | 0.7511 | 0.9444 |
| 0.1719 | 5.0 | 2640 | 0.1583 | 0.7056 | 0.8396 | 0.7668 | 0.9478 |
| 0.1602 | 6.0 | 3168 | 0.1539 | 0.7115 | 0.8582 | 0.7780 | 0.9502 |
| 0.1533 | 7.0 | 3696 | 0.1520 | 0.7031 | 0.8629 | 0.7748 | 0.9506 |
| 0.1455 | 8.0 | 4224 | 0.1456 | 0.7263 | 0.8559 | 0.7858 | 0.9525 |
| 0.1398 | 9.0 | 4752 | 0.1425 | 0.7301 | 0.8536 | 0.7870 | 0.9537 |
| 0.1368 | 10.0 | 5280 | 0.1395 | 0.7229 | 0.8536 | 0.7828 | 0.9533 |
| 0.1331 | 11.0 | 5808 | 0.1365 | 0.7360 | 0.8536 | 0.7904 | 0.9551 |
| 0.1305 | 12.0 | 6336 | 0.1377 | 0.7332 | 0.8605 | 0.7918 | 0.9549 |
| 0.1279 | 13.0 | 6864 | 0.1357 | 0.7415 | 0.8582 | 0.7956 | 0.9565 |
| 0.1251 | 14.0 | 7392 | 0.1355 | 0.7371 | 0.8652 | 0.7960 | 0.9555 |
| 0.1239 | 15.0 | 7920 | 0.1359 | 0.7366 | 0.8629 | 0.7948 | 0.9549 |
| 0.1231 | 16.0 | 8448 | 0.1347 | 0.7351 | 0.8629 | 0.7939 | 0.9551 |
| 0.122 | 17.0 | 8976 | 0.1353 | 0.7351 | 0.8629 | 0.7939 | 0.9555 |
| 0.1205 | 18.0 | 9504 | 0.1356 | 0.7317 | 0.8605 | 0.7909 | 0.9549 |
| 0.1202 | 19.0 | 10032 | 0.1347 | 0.7351 | 0.8629 | 0.7939 | 0.9551 |
| 0.1204 | 20.0 | 10560 | 0.1346 | 0.7366 | 0.8629 | 0.7948 | 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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