---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r8
  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-r8

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: 0.2786
- Accuracy: 0.8847
- Precision: 0.8648
- Recall: 0.8534
- F1: 0.8588

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5623        | 1.0   | 122  | 0.5217          | 0.7268   | 0.6604    | 0.6217 | 0.6301 |
| 0.5061        | 2.0   | 244  | 0.4898          | 0.7569   | 0.7074    | 0.7105 | 0.7089 |
| 0.4443        | 3.0   | 366  | 0.4085          | 0.8120   | 0.7751    | 0.7620 | 0.7679 |
| 0.3805        | 4.0   | 488  | 0.3672          | 0.8246   | 0.7980    | 0.7609 | 0.7752 |
| 0.3488        | 5.0   | 610  | 0.3535          | 0.8521   | 0.8207    | 0.8254 | 0.8229 |
| 0.3156        | 6.0   | 732  | 0.3337          | 0.8571   | 0.8299    | 0.8214 | 0.8255 |
| 0.3055        | 7.0   | 854  | 0.3217          | 0.8622   | 0.8385    | 0.8225 | 0.8298 |
| 0.2995        | 8.0   | 976  | 0.3145          | 0.8596   | 0.8347    | 0.8207 | 0.8272 |
| 0.2825        | 9.0   | 1098 | 0.3090          | 0.8672   | 0.8402    | 0.8385 | 0.8394 |
| 0.272         | 10.0  | 1220 | 0.2992          | 0.8722   | 0.8453    | 0.8471 | 0.8462 |
| 0.2626        | 11.0  | 1342 | 0.3008          | 0.8747   | 0.8568    | 0.8338 | 0.8440 |
| 0.2641        | 12.0  | 1464 | 0.2949          | 0.8747   | 0.8488    | 0.8488 | 0.8488 |
| 0.257         | 13.0  | 1586 | 0.2885          | 0.8772   | 0.8592    | 0.8381 | 0.8475 |
| 0.2473        | 14.0  | 1708 | 0.2826          | 0.8822   | 0.8596    | 0.8542 | 0.8568 |
| 0.2456        | 15.0  | 1830 | 0.2826          | 0.8847   | 0.8609    | 0.8609 | 0.8609 |
| 0.2477        | 16.0  | 1952 | 0.2795          | 0.8847   | 0.8621    | 0.8584 | 0.8602 |
| 0.2426        | 17.0  | 2074 | 0.2794          | 0.8797   | 0.8585    | 0.8474 | 0.8526 |
| 0.2359        | 18.0  | 2196 | 0.2796          | 0.8872   | 0.8658    | 0.8602 | 0.8629 |
| 0.2417        | 19.0  | 2318 | 0.2787          | 0.8847   | 0.8648    | 0.8534 | 0.8588 |
| 0.2319        | 20.0  | 2440 | 0.2786          | 0.8847   | 0.8648    | 0.8534 | 0.8588 |


### Framework versions

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