---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r16-1
  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-r16-1

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: 0.2834
- Accuracy: 0.8822
- Precision: 0.8574
- Recall: 0.8592
- F1: 0.8583

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5612        | 1.0   | 122  | 0.5258          | 0.7268   | 0.6614    | 0.6317 | 0.6399 |
| 0.4935        | 2.0   | 244  | 0.4827          | 0.7494   | 0.7127    | 0.7427 | 0.7201 |
| 0.428         | 3.0   | 366  | 0.3863          | 0.8246   | 0.7874    | 0.7984 | 0.7924 |
| 0.363         | 4.0   | 488  | 0.3415          | 0.8446   | 0.8207    | 0.7926 | 0.8043 |
| 0.3321        | 5.0   | 610  | 0.3417          | 0.8521   | 0.8186    | 0.8429 | 0.8285 |
| 0.3086        | 6.0   | 732  | 0.3376          | 0.8496   | 0.8158    | 0.8386 | 0.8253 |
| 0.2899        | 7.0   | 854  | 0.3156          | 0.8722   | 0.8453    | 0.8471 | 0.8462 |
| 0.2828        | 8.0   | 976  | 0.3073          | 0.8722   | 0.8463    | 0.8446 | 0.8454 |
| 0.2638        | 9.0   | 1098 | 0.3156          | 0.8622   | 0.8300    | 0.8525 | 0.8395 |
| 0.2628        | 10.0  | 1220 | 0.3002          | 0.8797   | 0.8522    | 0.8624 | 0.8570 |
| 0.249         | 11.0  | 1342 | 0.2935          | 0.8797   | 0.8572    | 0.8499 | 0.8534 |
| 0.2429        | 12.0  | 1464 | 0.2938          | 0.8772   | 0.8514    | 0.8531 | 0.8522 |
| 0.2406        | 13.0  | 1586 | 0.2902          | 0.8797   | 0.8585    | 0.8474 | 0.8526 |
| 0.2377        | 14.0  | 1708 | 0.2889          | 0.8722   | 0.8437    | 0.8521 | 0.8477 |
| 0.2257        | 15.0  | 1830 | 0.2848          | 0.8797   | 0.8530    | 0.8599 | 0.8563 |
| 0.2215        | 16.0  | 1952 | 0.2862          | 0.8747   | 0.8451    | 0.8613 | 0.8524 |
| 0.2297        | 17.0  | 2074 | 0.2833          | 0.8822   | 0.8610    | 0.8517 | 0.8561 |
| 0.2263        | 18.0  | 2196 | 0.2854          | 0.8772   | 0.8483    | 0.8631 | 0.8550 |
| 0.2194        | 19.0  | 2318 | 0.2833          | 0.8797   | 0.8539    | 0.8574 | 0.8556 |
| 0.214         | 20.0  | 2440 | 0.2834          | 0.8822   | 0.8574    | 0.8592 | 0.8583 |


### Framework versions

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