---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-lora-r8a0d0.15
  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-r8a0d0.15

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: 0.1281
- Precision: 0.7470
- Recall: 0.8629
- F1: 0.8008
- Accuracy: 0.9579

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7018        | 1.0   | 528   | 0.3353          | 0.5529    | 0.4800 | 0.5138 | 0.9115   |
| 0.2639        | 2.0   | 1056  | 0.1912          | 0.6494    | 0.8210 | 0.7252 | 0.9412   |
| 0.1862        | 3.0   | 1584  | 0.1672          | 0.6739    | 0.8536 | 0.7531 | 0.9466   |
| 0.1612        | 4.0   | 2112  | 0.1446          | 0.7238    | 0.8512 | 0.7824 | 0.9539   |
| 0.1439        | 5.0   | 2640  | 0.1390          | 0.7254    | 0.8582 | 0.7863 | 0.9545   |
| 0.1358        | 6.0   | 3168  | 0.1392          | 0.7256    | 0.8652 | 0.7893 | 0.9551   |
| 0.129         | 7.0   | 3696  | 0.1384          | 0.7267    | 0.8698 | 0.7919 | 0.9561   |
| 0.1228        | 8.0   | 4224  | 0.1339          | 0.7353    | 0.8698 | 0.7969 | 0.9575   |
| 0.1168        | 9.0   | 4752  | 0.1321          | 0.7439    | 0.8559 | 0.7960 | 0.9577   |
| 0.1146        | 10.0  | 5280  | 0.1300          | 0.7445    | 0.8582 | 0.7973 | 0.9581   |
| 0.1105        | 11.0  | 5808  | 0.1327          | 0.7333    | 0.8675 | 0.7948 | 0.9571   |
| 0.1083        | 12.0  | 6336  | 0.1333          | 0.7342    | 0.8652 | 0.7943 | 0.9569   |
| 0.106         | 13.0  | 6864  | 0.1265          | 0.7490    | 0.8582 | 0.7999 | 0.9591   |
| 0.1032        | 14.0  | 7392  | 0.1269          | 0.7445    | 0.8582 | 0.7973 | 0.9589   |
| 0.1023        | 15.0  | 7920  | 0.1291          | 0.7455    | 0.8629 | 0.7999 | 0.9585   |
| 0.1014        | 16.0  | 8448  | 0.1271          | 0.7400    | 0.8582 | 0.7947 | 0.9575   |
| 0.1002        | 17.0  | 8976  | 0.1281          | 0.7460    | 0.8722 | 0.8042 | 0.9589   |
| 0.0986        | 18.0  | 9504  | 0.1304          | 0.7416    | 0.8722 | 0.8016 | 0.9573   |
| 0.0978        | 19.0  | 10032 | 0.1271          | 0.7520    | 0.8652 | 0.8046 | 0.9589   |
| 0.0984        | 20.0  | 10560 | 0.1281          | 0.7470    | 0.8629 | 0.8008 | 0.9579   |


### Framework versions

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