---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerugm-lora-r2a0d0.05
  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-r2a0d0.05

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: 0.1346
- Precision: 0.7366
- Recall: 0.8629
- F1: 0.7948
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7885        | 1.0   | 528   | 0.4616          | 0.3182    | 0.0813 | 0.1296 | 0.8599   |
| 0.3921        | 2.0   | 1056  | 0.2524          | 0.6053    | 0.6798 | 0.6404 | 0.9273   |
| 0.2392        | 3.0   | 1584  | 0.1932          | 0.6500    | 0.7844 | 0.7109 | 0.9382   |
| 0.1931        | 4.0   | 2112  | 0.1676          | 0.6905    | 0.8234 | 0.7511 | 0.9444   |
| 0.1719        | 5.0   | 2640  | 0.1583          | 0.7056    | 0.8396 | 0.7668 | 0.9478   |
| 0.1602        | 6.0   | 3168  | 0.1539          | 0.7115    | 0.8582 | 0.7780 | 0.9502   |
| 0.1533        | 7.0   | 3696  | 0.1520          | 0.7031    | 0.8629 | 0.7748 | 0.9506   |
| 0.1455        | 8.0   | 4224  | 0.1456          | 0.7263    | 0.8559 | 0.7858 | 0.9525   |
| 0.1398        | 9.0   | 4752  | 0.1425          | 0.7301    | 0.8536 | 0.7870 | 0.9537   |
| 0.1368        | 10.0  | 5280  | 0.1395          | 0.7229    | 0.8536 | 0.7828 | 0.9533   |
| 0.1331        | 11.0  | 5808  | 0.1365          | 0.7360    | 0.8536 | 0.7904 | 0.9551   |
| 0.1305        | 12.0  | 6336  | 0.1377          | 0.7332    | 0.8605 | 0.7918 | 0.9549   |
| 0.1279        | 13.0  | 6864  | 0.1357          | 0.7415    | 0.8582 | 0.7956 | 0.9565   |
| 0.1251        | 14.0  | 7392  | 0.1355          | 0.7371    | 0.8652 | 0.7960 | 0.9555   |
| 0.1239        | 15.0  | 7920  | 0.1359          | 0.7366    | 0.8629 | 0.7948 | 0.9549   |
| 0.1231        | 16.0  | 8448  | 0.1347          | 0.7351    | 0.8629 | 0.7939 | 0.9551   |
| 0.122         | 17.0  | 8976  | 0.1353          | 0.7351    | 0.8629 | 0.7939 | 0.9555   |
| 0.1205        | 18.0  | 9504  | 0.1356          | 0.7317    | 0.8605 | 0.7909 | 0.9549   |
| 0.1202        | 19.0  | 10032 | 0.1347          | 0.7351    | 0.8629 | 0.7939 | 0.9551   |
| 0.1204        | 20.0  | 10560 | 0.1346          | 0.7366    | 0.8629 | 0.7948 | 0.9555   |


### Framework versions

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