---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-lora-r8a0d0.05
  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-r8a0d0.05

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: 0.1266
- Precision: 0.7622
- Recall: 0.8698
- F1: 0.8125
- Accuracy: 0.9591

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7039        | 1.0   | 528   | 0.3293          | 0.5553    | 0.4962 | 0.5241 | 0.9123   |
| 0.2536        | 2.0   | 1056  | 0.1835          | 0.6530    | 0.8210 | 0.7274 | 0.9424   |
| 0.1831        | 3.0   | 1584  | 0.1832          | 0.6678    | 0.8210 | 0.7365 | 0.9440   |
| 0.1623        | 4.0   | 2112  | 0.1463          | 0.7213    | 0.8466 | 0.7789 | 0.9535   |
| 0.1439        | 5.0   | 2640  | 0.1387          | 0.7173    | 0.8420 | 0.7747 | 0.9541   |
| 0.1348        | 6.0   | 3168  | 0.1383          | 0.7256    | 0.8652 | 0.7893 | 0.9553   |
| 0.1293        | 7.0   | 3696  | 0.1394          | 0.7242    | 0.8652 | 0.7885 | 0.9545   |
| 0.124         | 8.0   | 4224  | 0.1351          | 0.7353    | 0.8698 | 0.7969 | 0.9569   |
| 0.1176        | 9.0   | 4752  | 0.1304          | 0.7404    | 0.8536 | 0.7930 | 0.9561   |
| 0.1153        | 10.0  | 5280  | 0.1278          | 0.7582    | 0.8582 | 0.8051 | 0.9585   |
| 0.111         | 11.0  | 5808  | 0.1304          | 0.7386    | 0.8652 | 0.7969 | 0.9579   |
| 0.109         | 12.0  | 6336  | 0.1323          | 0.7415    | 0.8652 | 0.7986 | 0.9565   |
| 0.1077        | 13.0  | 6864  | 0.1253          | 0.7649    | 0.8675 | 0.8130 | 0.9597   |
| 0.1032        | 14.0  | 7392  | 0.1243          | 0.7639    | 0.8629 | 0.8104 | 0.9593   |
| 0.1035        | 15.0  | 7920  | 0.1261          | 0.7664    | 0.8675 | 0.8138 | 0.9597   |
| 0.1017        | 16.0  | 8448  | 0.1258          | 0.7470    | 0.8559 | 0.7977 | 0.9577   |
| 0.1004        | 17.0  | 8976  | 0.1278          | 0.7576    | 0.8698 | 0.8098 | 0.9589   |
| 0.099         | 18.0  | 9504  | 0.1284          | 0.7510    | 0.8675 | 0.8051 | 0.9585   |
| 0.0991        | 19.0  | 10032 | 0.1256          | 0.7572    | 0.8605 | 0.8055 | 0.9581   |
| 0.0984        | 20.0  | 10560 | 0.1266          | 0.7622    | 0.8698 | 0.8125 | 0.9591   |


### Framework versions

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