---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-lora-r2a2d0.1
  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-r2a2d0.1

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: 0.1332
- Precision: 0.7287
- Recall: 0.8536
- F1: 0.7862
- Accuracy: 0.9555

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7886        | 1.0   | 528   | 0.4607          | 0.3243    | 0.0837 | 0.1330 | 0.8597   |
| 0.3911        | 2.0   | 1056  | 0.2542          | 0.6081    | 0.6915 | 0.6471 | 0.9293   |
| 0.2384        | 3.0   | 1584  | 0.1934          | 0.6527    | 0.7937 | 0.7163 | 0.9376   |
| 0.1934        | 4.0   | 2112  | 0.1678          | 0.6880    | 0.8187 | 0.7477 | 0.9446   |
| 0.172         | 5.0   | 2640  | 0.1589          | 0.6901    | 0.8373 | 0.7566 | 0.9468   |
| 0.1602        | 6.0   | 3168  | 0.1533          | 0.6931    | 0.8489 | 0.7631 | 0.9488   |
| 0.1532        | 7.0   | 3696  | 0.1505          | 0.6935    | 0.8559 | 0.7662 | 0.9498   |
| 0.1457        | 8.0   | 4224  | 0.1456          | 0.7103    | 0.8536 | 0.7754 | 0.9522   |
| 0.1401        | 9.0   | 4752  | 0.1418          | 0.7301    | 0.8536 | 0.7870 | 0.9543   |
| 0.1375        | 10.0  | 5280  | 0.1388          | 0.7308    | 0.8582 | 0.7894 | 0.9551   |
| 0.1331        | 11.0  | 5808  | 0.1360          | 0.7308    | 0.8582 | 0.7894 | 0.9555   |
| 0.1304        | 12.0  | 6336  | 0.1365          | 0.7258    | 0.8536 | 0.7845 | 0.9549   |
| 0.1285        | 13.0  | 6864  | 0.1343          | 0.7380    | 0.8512 | 0.7906 | 0.9559   |
| 0.1255        | 14.0  | 7392  | 0.1345          | 0.7401    | 0.8605 | 0.7958 | 0.9559   |
| 0.1249        | 15.0  | 7920  | 0.1346          | 0.7332    | 0.8605 | 0.7918 | 0.9549   |
| 0.1238        | 16.0  | 8448  | 0.1342          | 0.7307    | 0.8559 | 0.7883 | 0.9551   |
| 0.1232        | 17.0  | 8976  | 0.1342          | 0.7326    | 0.8582 | 0.7905 | 0.9557   |
| 0.1215        | 18.0  | 9504  | 0.1351          | 0.7317    | 0.8605 | 0.7909 | 0.9549   |
| 0.1209        | 19.0  | 10032 | 0.1337          | 0.7278    | 0.8559 | 0.7866 | 0.9547   |
| 0.1207        | 20.0  | 10560 | 0.1332          | 0.7287    | 0.8536 | 0.7862 | 0.9555   |


### Framework versions

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