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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-r8a2d0.05
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-r8a2d0.05
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: 0.1266
- Precision: 0.7622
- Recall: 0.8698
- F1: 0.8125
- Accuracy: 0.9591
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7039 | 1.0 | 528 | 0.3293 | 0.5553 | 0.4962 | 0.5241 | 0.9123 |
| 0.2536 | 2.0 | 1056 | 0.1835 | 0.6530 | 0.8210 | 0.7274 | 0.9424 |
| 0.1831 | 3.0 | 1584 | 0.1832 | 0.6678 | 0.8210 | 0.7365 | 0.9440 |
| 0.1623 | 4.0 | 2112 | 0.1463 | 0.7213 | 0.8466 | 0.7789 | 0.9535 |
| 0.1439 | 5.0 | 2640 | 0.1387 | 0.7173 | 0.8420 | 0.7747 | 0.9541 |
| 0.1348 | 6.0 | 3168 | 0.1383 | 0.7256 | 0.8652 | 0.7893 | 0.9553 |
| 0.1293 | 7.0 | 3696 | 0.1394 | 0.7242 | 0.8652 | 0.7885 | 0.9545 |
| 0.124 | 8.0 | 4224 | 0.1351 | 0.7353 | 0.8698 | 0.7969 | 0.9569 |
| 0.1176 | 9.0 | 4752 | 0.1304 | 0.7404 | 0.8536 | 0.7930 | 0.9561 |
| 0.1153 | 10.0 | 5280 | 0.1278 | 0.7582 | 0.8582 | 0.8051 | 0.9585 |
| 0.111 | 11.0 | 5808 | 0.1304 | 0.7386 | 0.8652 | 0.7969 | 0.9579 |
| 0.109 | 12.0 | 6336 | 0.1323 | 0.7415 | 0.8652 | 0.7986 | 0.9565 |
| 0.1077 | 13.0 | 6864 | 0.1253 | 0.7649 | 0.8675 | 0.8130 | 0.9597 |
| 0.1032 | 14.0 | 7392 | 0.1243 | 0.7639 | 0.8629 | 0.8104 | 0.9593 |
| 0.1035 | 15.0 | 7920 | 0.1261 | 0.7664 | 0.8675 | 0.8138 | 0.9597 |
| 0.1017 | 16.0 | 8448 | 0.1258 | 0.7470 | 0.8559 | 0.7977 | 0.9577 |
| 0.1004 | 17.0 | 8976 | 0.1278 | 0.7576 | 0.8698 | 0.8098 | 0.9589 |
| 0.099 | 18.0 | 9504 | 0.1284 | 0.7510 | 0.8675 | 0.8051 | 0.9585 |
| 0.0991 | 19.0 | 10032 | 0.1256 | 0.7572 | 0.8605 | 0.8055 | 0.9581 |
| 0.0984 | 20.0 | 10560 | 0.1266 | 0.7622 | 0.8698 | 0.8125 | 0.9591 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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