---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r8a0d0.15-0
  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-r8a0d0.15-0

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: 0.3217
- Accuracy: 0.8622
- Precision: 0.8326
- Recall: 0.8375
- F1: 0.8349

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5593        | 1.0   | 122  | 0.5026          | 0.7268   | 0.6658    | 0.6542 | 0.6589 |
| 0.4995        | 2.0   | 244  | 0.4797          | 0.7544   | 0.7149    | 0.7412 | 0.7226 |
| 0.4612        | 3.0   | 366  | 0.4282          | 0.7644   | 0.7199    | 0.7358 | 0.7262 |
| 0.4019        | 4.0   | 488  | 0.3934          | 0.8296   | 0.7949    | 0.7919 | 0.7934 |
| 0.3665        | 5.0   | 610  | 0.4234          | 0.7970   | 0.7618    | 0.7964 | 0.7720 |
| 0.334         | 6.0   | 732  | 0.3723          | 0.8195   | 0.7817    | 0.7973 | 0.7884 |
| 0.3263        | 7.0   | 854  | 0.3704          | 0.8346   | 0.7990    | 0.8230 | 0.8086 |
| 0.3076        | 8.0   | 976  | 0.3521          | 0.8471   | 0.8153    | 0.8168 | 0.8160 |
| 0.298         | 9.0   | 1098 | 0.3522          | 0.8471   | 0.8138    | 0.8243 | 0.8187 |
| 0.2923        | 10.0  | 1220 | 0.3375          | 0.8571   | 0.8289    | 0.8239 | 0.8264 |
| 0.2689        | 11.0  | 1342 | 0.3392          | 0.8622   | 0.8319    | 0.8400 | 0.8357 |
| 0.2686        | 12.0  | 1464 | 0.3484          | 0.8622   | 0.8309    | 0.8450 | 0.8373 |
| 0.2726        | 13.0  | 1586 | 0.3258          | 0.8596   | 0.8316    | 0.8282 | 0.8298 |
| 0.2713        | 14.0  | 1708 | 0.3246          | 0.8622   | 0.8333    | 0.8350 | 0.8341 |
| 0.2577        | 15.0  | 1830 | 0.3307          | 0.8596   | 0.8293    | 0.8357 | 0.8324 |
| 0.2519        | 16.0  | 1952 | 0.3305          | 0.8622   | 0.8314    | 0.8425 | 0.8365 |
| 0.2488        | 17.0  | 2074 | 0.3234          | 0.8546   | 0.8246    | 0.8246 | 0.8246 |
| 0.2546        | 18.0  | 2196 | 0.3247          | 0.8647   | 0.8346    | 0.8442 | 0.8391 |
| 0.2463        | 19.0  | 2318 | 0.3204          | 0.8596   | 0.8307    | 0.8307 | 0.8307 |
| 0.2458        | 20.0  | 2440 | 0.3217          | 0.8622   | 0.8326    | 0.8375 | 0.8349 |


### Framework versions

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