---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r4a1d0.05-1
  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-r4a1d0.05-1

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: 0.3356
- Accuracy: 0.8622
- Precision: 0.8399
- Recall: 0.8200
- F1: 0.8289

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5657        | 1.0   | 122  | 0.5182          | 0.7243   | 0.6604    | 0.6424 | 0.6488 |
| 0.5109        | 2.0   | 244  | 0.5051          | 0.7243   | 0.6748    | 0.6874 | 0.6796 |
| 0.48          | 3.0   | 366  | 0.4643          | 0.7569   | 0.7047    | 0.6880 | 0.6948 |
| 0.434         | 4.0   | 488  | 0.4281          | 0.7920   | 0.7497    | 0.7378 | 0.7431 |
| 0.4106        | 5.0   | 610  | 0.4194          | 0.7920   | 0.7528    | 0.7778 | 0.7618 |
| 0.3812        | 6.0   | 732  | 0.3936          | 0.8296   | 0.8008    | 0.7744 | 0.7854 |
| 0.3689        | 7.0   | 854  | 0.3700          | 0.8521   | 0.8220    | 0.8204 | 0.8212 |
| 0.3489        | 8.0   | 976  | 0.3656          | 0.8346   | 0.8088    | 0.7780 | 0.7905 |
| 0.3502        | 9.0   | 1098 | 0.3640          | 0.8371   | 0.8101    | 0.7847 | 0.7955 |
| 0.3349        | 10.0  | 1220 | 0.3608          | 0.8346   | 0.8074    | 0.7805 | 0.7917 |
| 0.3189        | 11.0  | 1342 | 0.3574          | 0.8396   | 0.8128    | 0.7890 | 0.7992 |
| 0.3121        | 12.0  | 1464 | 0.3547          | 0.8471   | 0.8175    | 0.8093 | 0.8132 |
| 0.3181        | 13.0  | 1586 | 0.3478          | 0.8521   | 0.8332    | 0.7979 | 0.8122 |
| 0.3092        | 14.0  | 1708 | 0.3435          | 0.8596   | 0.8374    | 0.8157 | 0.8253 |
| 0.3018        | 15.0  | 1830 | 0.3466          | 0.8546   | 0.8296    | 0.8121 | 0.8200 |
| 0.2955        | 16.0  | 1952 | 0.3365          | 0.8596   | 0.8347    | 0.8207 | 0.8272 |
| 0.2917        | 17.0  | 2074 | 0.3353          | 0.8596   | 0.8374    | 0.8157 | 0.8253 |
| 0.2956        | 18.0  | 2196 | 0.3379          | 0.8596   | 0.8360    | 0.8182 | 0.8262 |
| 0.2899        | 19.0  | 2318 | 0.3353          | 0.8647   | 0.8455    | 0.8192 | 0.8306 |
| 0.2885        | 20.0  | 2440 | 0.3356          | 0.8622   | 0.8399    | 0.8200 | 0.8289 |


### Framework versions

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