---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-base-0
  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-base-0

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: 0.2749
- Precision: 0.8234
- Recall: 0.8964
- F1: 0.8584
- Accuracy: 0.9631

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3551        | 1.0   | 106  | 0.1873          | 0.6789    | 0.8757 | 0.7649 | 0.9414   |
| 0.1199        | 2.0   | 212  | 0.1308          | 0.7602    | 0.8817 | 0.8164 | 0.9611   |
| 0.0746        | 3.0   | 318  | 0.1383          | 0.7755    | 0.8787 | 0.8239 | 0.9618   |
| 0.0497        | 4.0   | 424  | 0.1717          | 0.7922    | 0.8462 | 0.8183 | 0.9554   |
| 0.0289        | 5.0   | 530  | 0.1706          | 0.8027    | 0.8787 | 0.8390 | 0.9621   |
| 0.023         | 6.0   | 636  | 0.1929          | 0.7688    | 0.8757 | 0.8188 | 0.9585   |
| 0.0161        | 7.0   | 742  | 0.2457          | 0.7769    | 0.8757 | 0.8234 | 0.9539   |
| 0.0106        | 8.0   | 848  | 0.2450          | 0.7926    | 0.8817 | 0.8347 | 0.9572   |
| 0.0065        | 9.0   | 954  | 0.2315          | 0.8150    | 0.8994 | 0.8551 | 0.9629   |
| 0.0053        | 10.0  | 1060 | 0.2373          | 0.8147    | 0.8846 | 0.8482 | 0.9626   |
| 0.004         | 11.0  | 1166 | 0.2421          | 0.8283    | 0.8846 | 0.8555 | 0.9639   |
| 0.003         | 12.0  | 1272 | 0.2572          | 0.808     | 0.8964 | 0.8499 | 0.9621   |
| 0.0027        | 13.0  | 1378 | 0.2516          | 0.8135    | 0.8905 | 0.8503 | 0.9616   |
| 0.0012        | 14.0  | 1484 | 0.2636          | 0.8123    | 0.8964 | 0.8523 | 0.9649   |
| 0.002         | 15.0  | 1590 | 0.2672          | 0.8091    | 0.8905 | 0.8479 | 0.9626   |
| 0.0012        | 16.0  | 1696 | 0.2610          | 0.8130    | 0.8876 | 0.8487 | 0.9634   |
| 0.001         | 17.0  | 1802 | 0.2694          | 0.8251    | 0.8935 | 0.8580 | 0.9631   |
| 0.0012        | 18.0  | 1908 | 0.2815          | 0.8177    | 0.9024 | 0.8579 | 0.9626   |
| 0.0012        | 19.0  | 2014 | 0.2723          | 0.8229    | 0.8935 | 0.8567 | 0.9629   |
| 0.0008        | 20.0  | 2120 | 0.2749          | 0.8234    | 0.8964 | 0.8584 | 0.9631   |


### Framework versions

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