---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-ia3
  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-ia3

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: 0.1662
- Precision: 0.6732
- Recall: 0.7937
- F1: 0.7285
- Accuracy: 0.9450

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8173        | 1.0   | 528   | 0.5014          | 0.2653    | 0.0302 | 0.0543 | 0.8480   |
| 0.4808        | 2.0   | 1056  | 0.3565          | 0.5217    | 0.3068 | 0.3864 | 0.8941   |
| 0.3767        | 3.0   | 1584  | 0.2893          | 0.5761    | 0.5520 | 0.5638 | 0.9194   |
| 0.3159        | 4.0   | 2112  | 0.2479          | 0.6181    | 0.6612 | 0.6390 | 0.9279   |
| 0.2785        | 5.0   | 2640  | 0.2236          | 0.6304    | 0.7008 | 0.6637 | 0.9331   |
| 0.2527        | 6.0   | 3168  | 0.2097          | 0.6449    | 0.7333 | 0.6862 | 0.9361   |
| 0.2365        | 7.0   | 3696  | 0.1997          | 0.6415    | 0.7519 | 0.6923 | 0.9376   |
| 0.2243        | 8.0   | 4224  | 0.1905          | 0.6534    | 0.7612 | 0.7032 | 0.9394   |
| 0.2134        | 9.0   | 4752  | 0.1857          | 0.6522    | 0.7705 | 0.7064 | 0.9398   |
| 0.2072        | 10.0  | 5280  | 0.1814          | 0.6562    | 0.7798 | 0.7127 | 0.9418   |
| 0.2009        | 11.0  | 5808  | 0.1756          | 0.6601    | 0.7798 | 0.7150 | 0.9426   |
| 0.1962        | 12.0  | 6336  | 0.1738          | 0.6589    | 0.7844 | 0.7162 | 0.9436   |
| 0.1921        | 13.0  | 6864  | 0.1720          | 0.6621    | 0.7914 | 0.7210 | 0.9442   |
| 0.1887        | 14.0  | 7392  | 0.1705          | 0.6621    | 0.7914 | 0.7210 | 0.9442   |
| 0.1857        | 15.0  | 7920  | 0.1688          | 0.6680    | 0.7937 | 0.7254 | 0.9448   |
| 0.1846        | 16.0  | 8448  | 0.1684          | 0.6712    | 0.7960 | 0.7283 | 0.9450   |
| 0.1833        | 17.0  | 8976  | 0.1676          | 0.6706    | 0.7937 | 0.7270 | 0.9446   |
| 0.1804        | 18.0  | 9504  | 0.1667          | 0.6719    | 0.7937 | 0.7278 | 0.9446   |
| 0.1816        | 19.0  | 10032 | 0.1664          | 0.6719    | 0.7937 | 0.7278 | 0.9448   |
| 0.1801        | 20.0  | 10560 | 0.1662          | 0.6732    | 0.7937 | 0.7285 | 0.9450   |


### Framework versions

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