---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-lora-r4a1d0.05
  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-r4a1d0.05

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: 0.1305
- Precision: 0.7407
- Recall: 0.8698
- F1: 0.8001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7682        | 1.0   | 528   | 0.4394          | 0.4048    | 0.1185 | 0.1834 | 0.8663   |
| 0.3466        | 2.0   | 1056  | 0.2217          | 0.6022    | 0.7379 | 0.6632 | 0.9327   |
| 0.2131        | 3.0   | 1584  | 0.1728          | 0.6765    | 0.8396 | 0.7493 | 0.9428   |
| 0.1759        | 4.0   | 2112  | 0.1509          | 0.7221    | 0.8559 | 0.7833 | 0.9516   |
| 0.1563        | 5.0   | 2640  | 0.1422          | 0.7303    | 0.8605 | 0.7901 | 0.9533   |
| 0.1464        | 6.0   | 3168  | 0.1429          | 0.7202    | 0.8722 | 0.7890 | 0.9541   |
| 0.1394        | 7.0   | 3696  | 0.1440          | 0.7153    | 0.8745 | 0.7869 | 0.9525   |
| 0.1325        | 8.0   | 4224  | 0.1398          | 0.7274    | 0.8791 | 0.7961 | 0.9553   |
| 0.1269        | 9.0   | 4752  | 0.1341          | 0.7420    | 0.8675 | 0.7999 | 0.9579   |
| 0.124         | 10.0  | 5280  | 0.1331          | 0.7379    | 0.8768 | 0.8014 | 0.9565   |
| 0.1194        | 11.0  | 5808  | 0.1329          | 0.7389    | 0.8815 | 0.8039 | 0.9569   |
| 0.1171        | 12.0  | 6336  | 0.1337          | 0.7384    | 0.8791 | 0.8027 | 0.9567   |
| 0.1153        | 13.0  | 6864  | 0.1294          | 0.7447    | 0.8745 | 0.8044 | 0.9587   |
| 0.1119        | 14.0  | 7392  | 0.1310          | 0.7472    | 0.8791 | 0.8078 | 0.9573   |
| 0.1109        | 15.0  | 7920  | 0.1312          | 0.7457    | 0.8722 | 0.8040 | 0.9579   |
| 0.1102        | 16.0  | 8448  | 0.1309          | 0.7442    | 0.8791 | 0.8061 | 0.9581   |
| 0.1095        | 17.0  | 8976  | 0.1314          | 0.7447    | 0.8815 | 0.8073 | 0.9587   |
| 0.1073        | 18.0  | 9504  | 0.1323          | 0.7403    | 0.8745 | 0.8018 | 0.9577   |
| 0.107         | 19.0  | 10032 | 0.1300          | 0.7407    | 0.8698 | 0.8001 | 0.9581   |
| 0.1073        | 20.0  | 10560 | 0.1305          | 0.7407    | 0.8698 | 0.8001 | 0.9579   |


### Framework versions

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