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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-r2a2d0.1
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-r2a2d0.1
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: 0.1332
- Precision: 0.7287
- Recall: 0.8536
- F1: 0.7862
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7886 | 1.0 | 528 | 0.4607 | 0.3243 | 0.0837 | 0.1330 | 0.8597 |
| 0.3911 | 2.0 | 1056 | 0.2542 | 0.6081 | 0.6915 | 0.6471 | 0.9293 |
| 0.2384 | 3.0 | 1584 | 0.1934 | 0.6527 | 0.7937 | 0.7163 | 0.9376 |
| 0.1934 | 4.0 | 2112 | 0.1678 | 0.6880 | 0.8187 | 0.7477 | 0.9446 |
| 0.172 | 5.0 | 2640 | 0.1589 | 0.6901 | 0.8373 | 0.7566 | 0.9468 |
| 0.1602 | 6.0 | 3168 | 0.1533 | 0.6931 | 0.8489 | 0.7631 | 0.9488 |
| 0.1532 | 7.0 | 3696 | 0.1505 | 0.6935 | 0.8559 | 0.7662 | 0.9498 |
| 0.1457 | 8.0 | 4224 | 0.1456 | 0.7103 | 0.8536 | 0.7754 | 0.9522 |
| 0.1401 | 9.0 | 4752 | 0.1418 | 0.7301 | 0.8536 | 0.7870 | 0.9543 |
| 0.1375 | 10.0 | 5280 | 0.1388 | 0.7308 | 0.8582 | 0.7894 | 0.9551 |
| 0.1331 | 11.0 | 5808 | 0.1360 | 0.7308 | 0.8582 | 0.7894 | 0.9555 |
| 0.1304 | 12.0 | 6336 | 0.1365 | 0.7258 | 0.8536 | 0.7845 | 0.9549 |
| 0.1285 | 13.0 | 6864 | 0.1343 | 0.7380 | 0.8512 | 0.7906 | 0.9559 |
| 0.1255 | 14.0 | 7392 | 0.1345 | 0.7401 | 0.8605 | 0.7958 | 0.9559 |
| 0.1249 | 15.0 | 7920 | 0.1346 | 0.7332 | 0.8605 | 0.7918 | 0.9549 |
| 0.1238 | 16.0 | 8448 | 0.1342 | 0.7307 | 0.8559 | 0.7883 | 0.9551 |
| 0.1232 | 17.0 | 8976 | 0.1342 | 0.7326 | 0.8582 | 0.7905 | 0.9557 |
| 0.1215 | 18.0 | 9504 | 0.1351 | 0.7317 | 0.8605 | 0.7909 | 0.9549 |
| 0.1209 | 19.0 | 10032 | 0.1337 | 0.7278 | 0.8559 | 0.7866 | 0.9547 |
| 0.1207 | 20.0 | 10560 | 0.1332 | 0.7287 | 0.8536 | 0.7862 | 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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