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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-r4a0d0.15
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-r4a0d0.15
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: 0.1301
- Precision: 0.7357
- Recall: 0.8652
- F1: 0.7952
- Accuracy: 0.9577
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7663 | 1.0 | 528 | 0.4380 | 0.3934 | 0.1116 | 0.1738 | 0.8659 |
| 0.3481 | 2.0 | 1056 | 0.2220 | 0.6018 | 0.7403 | 0.6639 | 0.9339 |
| 0.2139 | 3.0 | 1584 | 0.1790 | 0.6561 | 0.8327 | 0.7339 | 0.9400 |
| 0.1777 | 4.0 | 2112 | 0.1535 | 0.7164 | 0.8559 | 0.7800 | 0.9512 |
| 0.1578 | 5.0 | 2640 | 0.1445 | 0.7367 | 0.8698 | 0.7978 | 0.9535 |
| 0.1469 | 6.0 | 3168 | 0.1441 | 0.7139 | 0.8745 | 0.7861 | 0.9535 |
| 0.1399 | 7.0 | 3696 | 0.1453 | 0.7175 | 0.8838 | 0.7920 | 0.9524 |
| 0.1333 | 8.0 | 4224 | 0.1403 | 0.7298 | 0.8838 | 0.7995 | 0.9547 |
| 0.1273 | 9.0 | 4752 | 0.1368 | 0.7387 | 0.8722 | 0.7999 | 0.9563 |
| 0.1246 | 10.0 | 5280 | 0.1342 | 0.7426 | 0.8768 | 0.8042 | 0.9569 |
| 0.1195 | 11.0 | 5808 | 0.1351 | 0.7359 | 0.8791 | 0.8012 | 0.9571 |
| 0.1172 | 12.0 | 6336 | 0.1349 | 0.7373 | 0.8791 | 0.8020 | 0.9573 |
| 0.1155 | 13.0 | 6864 | 0.1296 | 0.7441 | 0.8768 | 0.8050 | 0.9581 |
| 0.1118 | 14.0 | 7392 | 0.1302 | 0.7367 | 0.8698 | 0.7978 | 0.9577 |
| 0.1111 | 15.0 | 7920 | 0.1322 | 0.7426 | 0.8768 | 0.8042 | 0.9577 |
| 0.1097 | 16.0 | 8448 | 0.1303 | 0.7353 | 0.8698 | 0.7969 | 0.9577 |
| 0.1094 | 17.0 | 8976 | 0.1306 | 0.7343 | 0.8722 | 0.7973 | 0.9573 |
| 0.1077 | 18.0 | 9504 | 0.1319 | 0.7372 | 0.8722 | 0.7990 | 0.9577 |
| 0.1065 | 19.0 | 10032 | 0.1296 | 0.7376 | 0.8675 | 0.7973 | 0.9577 |
| 0.1078 | 20.0 | 10560 | 0.1301 | 0.7357 | 0.8652 | 0.7952 | 0.9577 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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