---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-lora-r4a1d0.15
  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-r4a1d0.15

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: 0.1301
- Precision: 0.7357
- Recall: 0.8652
- F1: 0.7952
- Accuracy: 0.9577

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7663        | 1.0   | 528   | 0.4380          | 0.3934    | 0.1116 | 0.1738 | 0.8659   |
| 0.3481        | 2.0   | 1056  | 0.2220          | 0.6018    | 0.7403 | 0.6639 | 0.9339   |
| 0.2139        | 3.0   | 1584  | 0.1790          | 0.6561    | 0.8327 | 0.7339 | 0.9400   |
| 0.1777        | 4.0   | 2112  | 0.1535          | 0.7164    | 0.8559 | 0.7800 | 0.9512   |
| 0.1578        | 5.0   | 2640  | 0.1445          | 0.7367    | 0.8698 | 0.7978 | 0.9535   |
| 0.1469        | 6.0   | 3168  | 0.1441          | 0.7139    | 0.8745 | 0.7861 | 0.9535   |
| 0.1399        | 7.0   | 3696  | 0.1453          | 0.7175    | 0.8838 | 0.7920 | 0.9524   |
| 0.1333        | 8.0   | 4224  | 0.1403          | 0.7298    | 0.8838 | 0.7995 | 0.9547   |
| 0.1273        | 9.0   | 4752  | 0.1368          | 0.7387    | 0.8722 | 0.7999 | 0.9563   |
| 0.1246        | 10.0  | 5280  | 0.1342          | 0.7426    | 0.8768 | 0.8042 | 0.9569   |
| 0.1195        | 11.0  | 5808  | 0.1351          | 0.7359    | 0.8791 | 0.8012 | 0.9571   |
| 0.1172        | 12.0  | 6336  | 0.1349          | 0.7373    | 0.8791 | 0.8020 | 0.9573   |
| 0.1155        | 13.0  | 6864  | 0.1296          | 0.7441    | 0.8768 | 0.8050 | 0.9581   |
| 0.1118        | 14.0  | 7392  | 0.1302          | 0.7367    | 0.8698 | 0.7978 | 0.9577   |
| 0.1111        | 15.0  | 7920  | 0.1322          | 0.7426    | 0.8768 | 0.8042 | 0.9577   |
| 0.1097        | 16.0  | 8448  | 0.1303          | 0.7353    | 0.8698 | 0.7969 | 0.9577   |
| 0.1094        | 17.0  | 8976  | 0.1306          | 0.7343    | 0.8722 | 0.7973 | 0.9573   |
| 0.1077        | 18.0  | 9504  | 0.1319          | 0.7372    | 0.8722 | 0.7990 | 0.9577   |
| 0.1065        | 19.0  | 10032 | 0.1296          | 0.7376    | 0.8675 | 0.7973 | 0.9577   |
| 0.1078        | 20.0  | 10560 | 0.1301          | 0.7357    | 0.8652 | 0.7952 | 0.9577   |


### Framework versions

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