---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r16a0d0.1-0
  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-r16a0d0.1-0

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: 0.2912
- Accuracy: 0.8647
- Precision: 0.8346
- Recall: 0.8442
- F1: 0.8391

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.559         | 1.0   | 122  | 0.5023          | 0.7268   | 0.6671    | 0.6592 | 0.6627 |
| 0.4818        | 2.0   | 244  | 0.4518          | 0.7569   | 0.7248    | 0.7605 | 0.7318 |
| 0.4129        | 3.0   | 366  | 0.3967          | 0.8246   | 0.7876    | 0.7959 | 0.7914 |
| 0.3519        | 4.0   | 488  | 0.3626          | 0.8496   | 0.8193    | 0.8161 | 0.8177 |
| 0.3191        | 5.0   | 610  | 0.3720          | 0.8471   | 0.8130    | 0.8393 | 0.8235 |
| 0.2977        | 6.0   | 732  | 0.3482          | 0.8546   | 0.8216    | 0.8422 | 0.8303 |
| 0.2861        | 7.0   | 854  | 0.3343          | 0.8672   | 0.8363    | 0.8535 | 0.8439 |
| 0.2662        | 8.0   | 976  | 0.3213          | 0.8622   | 0.8314    | 0.8425 | 0.8365 |
| 0.2618        | 9.0   | 1098 | 0.3246          | 0.8697   | 0.8399    | 0.8528 | 0.8458 |
| 0.2556        | 10.0  | 1220 | 0.3065          | 0.8596   | 0.8307    | 0.8307 | 0.8307 |
| 0.2353        | 11.0  | 1342 | 0.3148          | 0.8722   | 0.8416    | 0.8621 | 0.8505 |
| 0.24          | 12.0  | 1464 | 0.3098          | 0.8747   | 0.8451    | 0.8613 | 0.8524 |
| 0.2346        | 13.0  | 1586 | 0.2989          | 0.8772   | 0.8524    | 0.8506 | 0.8515 |
| 0.2367        | 14.0  | 1708 | 0.3001          | 0.8697   | 0.8399    | 0.8528 | 0.8458 |
| 0.2248        | 15.0  | 1830 | 0.3040          | 0.8722   | 0.8420    | 0.8596 | 0.8498 |
| 0.2174        | 16.0  | 1952 | 0.3016          | 0.8697   | 0.8386    | 0.8603 | 0.8479 |
| 0.2112        | 17.0  | 2074 | 0.2887          | 0.8647   | 0.8346    | 0.8442 | 0.8391 |
| 0.2162        | 18.0  | 2196 | 0.2980          | 0.8722   | 0.8416    | 0.8621 | 0.8505 |
| 0.2124        | 19.0  | 2318 | 0.2892          | 0.8697   | 0.8411    | 0.8478 | 0.8443 |
| 0.2118        | 20.0  | 2440 | 0.2912          | 0.8647   | 0.8346    | 0.8442 | 0.8391 |


### Framework versions

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