---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-lora-r2a2d0.15
  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-r2a2d0.15

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: 0.1346
- Precision: 0.7342
- Recall: 0.8652
- F1: 0.7943
- Accuracy: 0.9555

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.79          | 1.0   | 528   | 0.4638          | 0.3302    | 0.0813 | 0.1305 | 0.8595   |
| 0.3919        | 2.0   | 1056  | 0.2519          | 0.5954    | 0.6729 | 0.6318 | 0.9275   |
| 0.2386        | 3.0   | 1584  | 0.1927          | 0.6540    | 0.7908 | 0.7159 | 0.9382   |
| 0.193         | 4.0   | 2112  | 0.1677          | 0.6826    | 0.8234 | 0.7464 | 0.9448   |
| 0.1712        | 5.0   | 2640  | 0.1594          | 0.6959    | 0.8443 | 0.7629 | 0.9476   |
| 0.1596        | 6.0   | 3168  | 0.1544          | 0.7082    | 0.8559 | 0.7751 | 0.9498   |
| 0.1524        | 7.0   | 3696  | 0.1519          | 0.7012    | 0.8605 | 0.7728 | 0.9506   |
| 0.1452        | 8.0   | 4224  | 0.1461          | 0.7203    | 0.8605 | 0.7842 | 0.9522   |
| 0.1397        | 9.0   | 4752  | 0.1432          | 0.7263    | 0.8559 | 0.7858 | 0.9535   |
| 0.1369        | 10.0  | 5280  | 0.1394          | 0.7258    | 0.8536 | 0.7845 | 0.9539   |
| 0.1336        | 11.0  | 5808  | 0.1375          | 0.7321    | 0.8512 | 0.7872 | 0.9543   |
| 0.1305        | 12.0  | 6336  | 0.1375          | 0.7345    | 0.8536 | 0.7896 | 0.9547   |
| 0.1281        | 13.0  | 6864  | 0.1351          | 0.7330    | 0.8536 | 0.7887 | 0.9547   |
| 0.1252        | 14.0  | 7392  | 0.1360          | 0.7342    | 0.8652 | 0.7943 | 0.9553   |
| 0.124         | 15.0  | 7920  | 0.1364          | 0.7292    | 0.8559 | 0.7875 | 0.9541   |
| 0.1234        | 16.0  | 8448  | 0.1351          | 0.7260    | 0.8605 | 0.7876 | 0.9549   |
| 0.1224        | 17.0  | 8976  | 0.1357          | 0.7299    | 0.8652 | 0.7918 | 0.9549   |
| 0.1208        | 18.0  | 9504  | 0.1360          | 0.7333    | 0.8675 | 0.7948 | 0.9553   |
| 0.1201        | 19.0  | 10032 | 0.1350          | 0.7347    | 0.8675 | 0.7956 | 0.9555   |
| 0.1205        | 20.0  | 10560 | 0.1346          | 0.7342    | 0.8652 | 0.7943 | 0.9555   |


### Framework versions

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