---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-lora-r8-4
  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-r8-4

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: 0.1784
- Precision: 0.6597
- Recall: 0.8120
- F1: 0.7280
- Accuracy: 0.9434

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2537        | 1.0   | 106  | 0.7370          | 0.0       | 0.0    | 0.0    | 0.8366   |
| 0.7093        | 2.0   | 212  | 0.6298          | 0.1667    | 0.0028 | 0.0056 | 0.8373   |
| 0.6232        | 3.0   | 318  | 0.5443          | 0.2727    | 0.0171 | 0.0322 | 0.8417   |
| 0.5363        | 4.0   | 424  | 0.4559          | 0.3301    | 0.0969 | 0.1498 | 0.8624   |
| 0.4591        | 5.0   | 530  | 0.3863          | 0.4744    | 0.2906 | 0.3604 | 0.8929   |
| 0.387         | 6.0   | 636  | 0.3272          | 0.5789    | 0.5641 | 0.5714 | 0.9190   |
| 0.3252        | 7.0   | 742  | 0.2811          | 0.6035    | 0.6895 | 0.6436 | 0.9291   |
| 0.2874        | 8.0   | 848  | 0.2455          | 0.6108    | 0.7066 | 0.6552 | 0.9313   |
| 0.2588        | 9.0   | 954  | 0.2285          | 0.6179    | 0.7464 | 0.6761 | 0.9333   |
| 0.2393        | 10.0  | 1060 | 0.2153          | 0.6362    | 0.7721 | 0.6976 | 0.9363   |
| 0.224         | 11.0  | 1166 | 0.2062          | 0.6339    | 0.7892 | 0.7030 | 0.9387   |
| 0.2137        | 12.0  | 1272 | 0.2002          | 0.6473    | 0.7949 | 0.7136 | 0.9387   |
| 0.2052        | 13.0  | 1378 | 0.1889          | 0.6611    | 0.7949 | 0.7219 | 0.9424   |
| 0.2039        | 14.0  | 1484 | 0.1862          | 0.6690    | 0.8063 | 0.7313 | 0.9431   |
| 0.1975        | 15.0  | 1590 | 0.1868          | 0.6682    | 0.8091 | 0.7320 | 0.9431   |
| 0.1936        | 16.0  | 1696 | 0.1837          | 0.6628    | 0.8177 | 0.7321 | 0.9427   |
| 0.1908        | 17.0  | 1802 | 0.1825          | 0.6620    | 0.8148 | 0.7305 | 0.9427   |
| 0.1885        | 18.0  | 1908 | 0.1806          | 0.6582    | 0.8120 | 0.7270 | 0.9431   |
| 0.1877        | 19.0  | 2014 | 0.1783          | 0.6581    | 0.8063 | 0.7247 | 0.9431   |
| 0.1858        | 20.0  | 2120 | 0.1784          | 0.6597    | 0.8120 | 0.7280 | 0.9434   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2