---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-lora-r8a0d0.1
  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-r8a0d0.1

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: 0.1278
- Precision: 0.7600
- Recall: 0.8815
- F1: 0.8162
- Accuracy: 0.9593

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.713         | 1.0   | 528   | 0.3558          | 0.4950    | 0.3736 | 0.4258 | 0.8990   |
| 0.2793        | 2.0   | 1056  | 0.1931          | 0.6472    | 0.8048 | 0.7174 | 0.9392   |
| 0.1876        | 3.0   | 1584  | 0.1619          | 0.6758    | 0.8466 | 0.7516 | 0.9462   |
| 0.1593        | 4.0   | 2112  | 0.1416          | 0.7366    | 0.8629 | 0.7948 | 0.9555   |
| 0.1412        | 5.0   | 2640  | 0.1350          | 0.7386    | 0.8652 | 0.7969 | 0.9559   |
| 0.1325        | 6.0   | 3168  | 0.1361          | 0.7324    | 0.8698 | 0.7952 | 0.9555   |
| 0.126         | 7.0   | 3696  | 0.1383          | 0.7310    | 0.8698 | 0.7944 | 0.9553   |
| 0.1194        | 8.0   | 4224  | 0.1349          | 0.7456    | 0.8838 | 0.8088 | 0.9583   |
| 0.1137        | 9.0   | 4752  | 0.1299          | 0.7495    | 0.8745 | 0.8072 | 0.9583   |
| 0.1112        | 10.0  | 5280  | 0.1285          | 0.7455    | 0.8698 | 0.8029 | 0.9579   |
| 0.1065        | 11.0  | 5808  | 0.1304          | 0.7525    | 0.8815 | 0.8119 | 0.9587   |
| 0.1044        | 12.0  | 6336  | 0.1329          | 0.7520    | 0.8791 | 0.8106 | 0.9577   |
| 0.1026        | 13.0  | 6864  | 0.1257          | 0.7520    | 0.8722 | 0.8076 | 0.9585   |
| 0.0989        | 14.0  | 7392  | 0.1265          | 0.7626    | 0.8791 | 0.8167 | 0.9599   |
| 0.0982        | 15.0  | 7920  | 0.1281          | 0.7631    | 0.8815 | 0.8180 | 0.9597   |
| 0.0974        | 16.0  | 8448  | 0.1264          | 0.7515    | 0.8768 | 0.8093 | 0.9597   |
| 0.0966        | 17.0  | 8976  | 0.1282          | 0.7545    | 0.8838 | 0.8140 | 0.9589   |
| 0.095         | 18.0  | 9504  | 0.1292          | 0.7570    | 0.8815 | 0.8145 | 0.9589   |
| 0.0941        | 19.0  | 10032 | 0.1268          | 0.7585    | 0.8815 | 0.8154 | 0.9595   |
| 0.0948        | 20.0  | 10560 | 0.1278          | 0.7600    | 0.8815 | 0.8162 | 0.9593   |


### Framework versions

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