---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r2a1d0.1-1
  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-r2a1d0.1-1

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: 0.3681
- Accuracy: 0.8396
- Precision: 0.8141
- Recall: 0.7865
- F1: 0.7980

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.566         | 1.0   | 122  | 0.5211          | 0.7168   | 0.6521    | 0.6396 | 0.6444 |
| 0.5148        | 2.0   | 244  | 0.5169          | 0.7243   | 0.6791    | 0.6974 | 0.6850 |
| 0.4927        | 3.0   | 366  | 0.4861          | 0.7544   | 0.7017    | 0.6887 | 0.6942 |
| 0.4627        | 4.0   | 488  | 0.4656          | 0.7619   | 0.7120    | 0.7065 | 0.7091 |
| 0.4504        | 5.0   | 610  | 0.4611          | 0.7544   | 0.7120    | 0.7337 | 0.7193 |
| 0.4276        | 6.0   | 732  | 0.4303          | 0.7895   | 0.7461    | 0.7410 | 0.7434 |
| 0.4176        | 7.0   | 854  | 0.4163          | 0.7945   | 0.7521    | 0.7546 | 0.7533 |
| 0.397         | 8.0   | 976  | 0.3960          | 0.8170   | 0.7814    | 0.7680 | 0.7741 |
| 0.3904        | 9.0   | 1098 | 0.3940          | 0.8271   | 0.7969    | 0.7726 | 0.7829 |
| 0.3743        | 10.0  | 1220 | 0.3900          | 0.8271   | 0.7994    | 0.7676 | 0.7804 |
| 0.3632        | 11.0  | 1342 | 0.3848          | 0.8346   | 0.8062    | 0.7830 | 0.7929 |
| 0.3599        | 12.0  | 1464 | 0.3795          | 0.8271   | 0.7959    | 0.7751 | 0.7841 |
| 0.3597        | 13.0  | 1586 | 0.3765          | 0.8346   | 0.8136    | 0.7705 | 0.7867 |
| 0.3461        | 14.0  | 1708 | 0.3729          | 0.8321   | 0.8061    | 0.7737 | 0.7867 |
| 0.3432        | 15.0  | 1830 | 0.3714          | 0.8371   | 0.8101    | 0.7847 | 0.7955 |
| 0.333         | 16.0  | 1952 | 0.3706          | 0.8421   | 0.8181    | 0.7883 | 0.8006 |
| 0.3323        | 17.0  | 2074 | 0.3700          | 0.8396   | 0.8155    | 0.7840 | 0.7969 |
| 0.3337        | 18.0  | 2196 | 0.3687          | 0.8396   | 0.8141    | 0.7865 | 0.7980 |
| 0.3298        | 19.0  | 2318 | 0.3684          | 0.8396   | 0.8141    | 0.7865 | 0.7980 |
| 0.3309        | 20.0  | 2440 | 0.3681          | 0.8396   | 0.8141    | 0.7865 | 0.7980 |


### Framework versions

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