---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r4a0d0.1-1
  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. -->

# sentiment-lora-r4a0d0.1-1

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: 0.3239
- Accuracy: 0.8622
- Precision: 0.8373
- Recall: 0.8250
- F1: 0.8307

## 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: 30
- eval_batch_size: 8
- 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 | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5658        | 1.0   | 122  | 0.5195          | 0.7268   | 0.6646    | 0.6492 | 0.6550 |
| 0.5125        | 2.0   | 244  | 0.5060          | 0.7293   | 0.6805    | 0.6935 | 0.6855 |
| 0.4809        | 3.0   | 366  | 0.4686          | 0.7669   | 0.7184    | 0.7151 | 0.7167 |
| 0.4353        | 4.0   | 488  | 0.4295          | 0.7920   | 0.7500    | 0.7353 | 0.7417 |
| 0.4116        | 5.0   | 610  | 0.4171          | 0.8020   | 0.7628    | 0.7849 | 0.7714 |
| 0.3809        | 6.0   | 732  | 0.3865          | 0.8446   | 0.8148    | 0.8051 | 0.8096 |
| 0.3681        | 7.0   | 854  | 0.3697          | 0.8496   | 0.8193    | 0.8161 | 0.8177 |
| 0.3469        | 8.0   | 976  | 0.3554          | 0.8471   | 0.8206    | 0.8018 | 0.8102 |
| 0.3455        | 9.0   | 1098 | 0.3494          | 0.8496   | 0.8211    | 0.8111 | 0.8158 |
| 0.3284        | 10.0  | 1220 | 0.3437          | 0.8496   | 0.8289    | 0.7961 | 0.8096 |
| 0.3132        | 11.0  | 1342 | 0.3371          | 0.8596   | 0.8389    | 0.8132 | 0.8243 |
| 0.3042        | 12.0  | 1464 | 0.3371          | 0.8546   | 0.8254    | 0.8221 | 0.8238 |
| 0.3063        | 13.0  | 1586 | 0.3317          | 0.8596   | 0.8406    | 0.8107 | 0.8233 |
| 0.3013        | 14.0  | 1708 | 0.3304          | 0.8622   | 0.8373    | 0.8250 | 0.8307 |
| 0.2928        | 15.0  | 1830 | 0.3295          | 0.8596   | 0.8325    | 0.8257 | 0.8290 |
| 0.2864        | 16.0  | 1952 | 0.3284          | 0.8622   | 0.8351    | 0.8300 | 0.8325 |
| 0.2819        | 17.0  | 2074 | 0.3254          | 0.8596   | 0.8347    | 0.8207 | 0.8272 |
| 0.2877        | 18.0  | 2196 | 0.3249          | 0.8596   | 0.8336    | 0.8232 | 0.8281 |
| 0.2819        | 19.0  | 2318 | 0.3241          | 0.8647   | 0.8410    | 0.8267 | 0.8333 |
| 0.2803        | 20.0  | 2440 | 0.3239          | 0.8622   | 0.8373    | 0.8250 | 0.8307 |


### Framework versions

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