---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r16-3
  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-r16-3

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: 0.2726
- Accuracy: 0.8947
- Precision: 0.8757
- Recall: 0.8680
- F1: 0.8717

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.564         | 1.0   | 122  | 0.5210          | 0.7143   | 0.6432    | 0.6178 | 0.6246 |
| 0.5007        | 2.0   | 244  | 0.4797          | 0.7519   | 0.7062    | 0.7219 | 0.7123 |
| 0.428         | 3.0   | 366  | 0.3909          | 0.8246   | 0.7874    | 0.8009 | 0.7934 |
| 0.3751        | 4.0   | 488  | 0.3478          | 0.8471   | 0.8159    | 0.8143 | 0.8151 |
| 0.339         | 5.0   | 610  | 0.3369          | 0.8546   | 0.8224    | 0.8347 | 0.8280 |
| 0.3096        | 6.0   | 732  | 0.3206          | 0.8697   | 0.8411    | 0.8478 | 0.8443 |
| 0.2931        | 7.0   | 854  | 0.3140          | 0.8622   | 0.8373    | 0.8250 | 0.8307 |
| 0.2765        | 8.0   | 976  | 0.3045          | 0.8722   | 0.8453    | 0.8471 | 0.8462 |
| 0.2637        | 9.0   | 1098 | 0.3003          | 0.8797   | 0.8539    | 0.8574 | 0.8556 |
| 0.2601        | 10.0  | 1220 | 0.2910          | 0.8797   | 0.8549    | 0.8549 | 0.8549 |
| 0.2547        | 11.0  | 1342 | 0.2850          | 0.8897   | 0.8726    | 0.8570 | 0.8642 |
| 0.2426        | 12.0  | 1464 | 0.2798          | 0.8922   | 0.8706    | 0.8687 | 0.8697 |
| 0.2319        | 13.0  | 1586 | 0.2811          | 0.8922   | 0.8785    | 0.8562 | 0.8662 |
| 0.2359        | 14.0  | 1708 | 0.2720          | 0.8847   | 0.8609    | 0.8609 | 0.8609 |
| 0.2229        | 15.0  | 1830 | 0.2722          | 0.8947   | 0.8718    | 0.8755 | 0.8737 |
| 0.2218        | 16.0  | 1952 | 0.2731          | 0.8872   | 0.8624    | 0.8677 | 0.8650 |
| 0.2174        | 17.0  | 2074 | 0.2738          | 0.8922   | 0.8706    | 0.8687 | 0.8697 |
| 0.2165        | 18.0  | 2196 | 0.2739          | 0.8922   | 0.8694    | 0.8712 | 0.8703 |
| 0.2153        | 19.0  | 2318 | 0.2727          | 0.8972   | 0.8781    | 0.8723 | 0.8751 |
| 0.2159        | 20.0  | 2440 | 0.2726          | 0.8947   | 0.8757    | 0.8680 | 0.8717 |


### Framework versions

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