---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-lora-r8-2
  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-r8-2

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: 0.1743
- Precision: 0.6820
- Recall: 0.8289
- F1: 0.7483
- Accuracy: 0.9445

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2665        | 1.0   | 106  | 0.7137          | 0.0       | 0.0    | 0.0    | 0.8449   |
| 0.713         | 2.0   | 212  | 0.6075          | 0.0       | 0.0    | 0.0    | 0.8451   |
| 0.6346        | 3.0   | 318  | 0.5231          | 0.1905    | 0.0118 | 0.0222 | 0.8494   |
| 0.5555        | 4.0   | 424  | 0.4458          | 0.275     | 0.0649 | 0.1050 | 0.8656   |
| 0.4696        | 5.0   | 530  | 0.3715          | 0.4802    | 0.2861 | 0.3586 | 0.8949   |
| 0.3932        | 6.0   | 636  | 0.3134          | 0.5563    | 0.5251 | 0.5402 | 0.9194   |
| 0.3299        | 7.0   | 742  | 0.2706          | 0.5968    | 0.6637 | 0.6285 | 0.9277   |
| 0.2896        | 8.0   | 848  | 0.2433          | 0.62      | 0.7316 | 0.6712 | 0.9340   |
| 0.2656        | 9.0   | 954  | 0.2277          | 0.6289    | 0.7699 | 0.6923 | 0.9355   |
| 0.2442        | 10.0  | 1060 | 0.2082          | 0.6526    | 0.7758 | 0.7089 | 0.9387   |
| 0.23          | 11.0  | 1166 | 0.2020          | 0.6390    | 0.7935 | 0.7079 | 0.9382   |
| 0.2229        | 12.0  | 1272 | 0.1977          | 0.6524    | 0.8083 | 0.7220 | 0.9385   |
| 0.2132        | 13.0  | 1378 | 0.1886          | 0.6602    | 0.8083 | 0.7268 | 0.9402   |
| 0.2055        | 14.0  | 1484 | 0.1810          | 0.6708    | 0.7994 | 0.7295 | 0.9415   |
| 0.2038        | 15.0  | 1590 | 0.1822          | 0.6595    | 0.8112 | 0.7275 | 0.9405   |
| 0.2004        | 16.0  | 1696 | 0.1788          | 0.6731    | 0.8201 | 0.7394 | 0.9430   |
| 0.1966        | 17.0  | 1802 | 0.1775          | 0.6731    | 0.8260 | 0.7417 | 0.9432   |
| 0.1931        | 18.0  | 1908 | 0.1765          | 0.6683    | 0.8260 | 0.7388 | 0.9435   |
| 0.1937        | 19.0  | 2014 | 0.1749          | 0.6747    | 0.8260 | 0.7427 | 0.9437   |
| 0.1888        | 20.0  | 2120 | 0.1743          | 0.6820    | 0.8289 | 0.7483 | 0.9445   |


### Framework versions

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