---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-lora-r8-1
  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-r8-1

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: 0.1742
- Precision: 0.6892
- Recall: 0.8266
- F1: 0.7516
- Accuracy: 0.9465

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2459        | 1.0   | 106  | 0.7376          | 0.0       | 0.0    | 0.0    | 0.8353   |
| 0.7125        | 2.0   | 212  | 0.6395          | 0.1667    | 0.0029 | 0.0057 | 0.8363   |
| 0.6362        | 3.0   | 318  | 0.5518          | 0.1739    | 0.0116 | 0.0217 | 0.8400   |
| 0.5564        | 4.0   | 424  | 0.4672          | 0.2688    | 0.0723 | 0.1139 | 0.8578   |
| 0.4714        | 5.0   | 530  | 0.3912          | 0.4363    | 0.2572 | 0.3236 | 0.8880   |
| 0.3978        | 6.0   | 636  | 0.3240          | 0.5348    | 0.4884 | 0.5106 | 0.9135   |
| 0.3365        | 7.0   | 742  | 0.2839          | 0.5784    | 0.6503 | 0.6122 | 0.9242   |
| 0.294         | 8.0   | 848  | 0.2507          | 0.6173    | 0.7225 | 0.6658 | 0.9319   |
| 0.2677        | 9.0   | 954  | 0.2320          | 0.6401    | 0.7659 | 0.6974 | 0.9356   |
| 0.2457        | 10.0  | 1060 | 0.2109          | 0.6618    | 0.7803 | 0.7162 | 0.9393   |
| 0.2339        | 11.0  | 1166 | 0.2022          | 0.6667    | 0.7919 | 0.7239 | 0.9405   |
| 0.2215        | 12.0  | 1272 | 0.1987          | 0.6802    | 0.8237 | 0.7451 | 0.9425   |
| 0.2125        | 13.0  | 1378 | 0.1899          | 0.6770    | 0.8179 | 0.7408 | 0.9433   |
| 0.2085        | 14.0  | 1484 | 0.1854          | 0.6843    | 0.8208 | 0.7464 | 0.9438   |
| 0.2002        | 15.0  | 1590 | 0.1797          | 0.6917    | 0.8237 | 0.7520 | 0.9460   |
| 0.2           | 16.0  | 1696 | 0.1779          | 0.6867    | 0.8237 | 0.7490 | 0.9453   |
| 0.1929        | 17.0  | 1802 | 0.1774          | 0.6842    | 0.8266 | 0.7487 | 0.9450   |
| 0.1932        | 18.0  | 1908 | 0.1761          | 0.6875    | 0.8266 | 0.7507 | 0.9458   |
| 0.1916        | 19.0  | 2014 | 0.1747          | 0.6892    | 0.8266 | 0.7516 | 0.9465   |
| 0.1887        | 20.0  | 2120 | 0.1742          | 0.6892    | 0.8266 | 0.7516 | 0.9465   |


### Framework versions

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