---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl5-0
  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-pt-pl5-0

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: 0.3123
- Accuracy: 0.8822
- Precision: 0.8548
- Recall: 0.8667
- F1: 0.8603

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5463        | 1.0   | 122  | 0.4832          | 0.7419   | 0.6850    | 0.6698 | 0.6759 |
| 0.4591        | 2.0   | 244  | 0.4429          | 0.7519   | 0.7323    | 0.7770 | 0.7338 |
| 0.3761        | 3.0   | 366  | 0.3485          | 0.8421   | 0.8113    | 0.8033 | 0.8071 |
| 0.3183        | 4.0   | 488  | 0.3375          | 0.8521   | 0.8228    | 0.8179 | 0.8203 |
| 0.2811        | 5.0   | 610  | 0.3170          | 0.8571   | 0.8260    | 0.8339 | 0.8298 |
| 0.2593        | 6.0   | 732  | 0.3106          | 0.8722   | 0.8413    | 0.8646 | 0.8511 |
| 0.2372        | 7.0   | 854  | 0.3502          | 0.8471   | 0.8147    | 0.8543 | 0.8277 |
| 0.2287        | 8.0   | 976  | 0.2938          | 0.8922   | 0.8873    | 0.8462 | 0.8631 |
| 0.2109        | 9.0   | 1098 | 0.2922          | 0.8797   | 0.8496    | 0.8749 | 0.8602 |
| 0.2025        | 10.0  | 1220 | 0.2650          | 0.8822   | 0.8574    | 0.8592 | 0.8583 |
| 0.1918        | 11.0  | 1342 | 0.3182          | 0.8772   | 0.8463    | 0.8806 | 0.8594 |
| 0.1807        | 12.0  | 1464 | 0.2841          | 0.8947   | 0.8676    | 0.8880 | 0.8766 |
| 0.1751        | 13.0  | 1586 | 0.2675          | 0.8922   | 0.8683    | 0.8737 | 0.8710 |
| 0.1724        | 14.0  | 1708 | 0.2957          | 0.8847   | 0.8573    | 0.8709 | 0.8636 |
| 0.1502        | 15.0  | 1830 | 0.3010          | 0.8822   | 0.8541    | 0.8692 | 0.8609 |
| 0.1469        | 16.0  | 1952 | 0.3227          | 0.8872   | 0.8583    | 0.8827 | 0.8687 |
| 0.1457        | 17.0  | 2074 | 0.3037          | 0.8822   | 0.8541    | 0.8692 | 0.8609 |
| 0.1453        | 18.0  | 2196 | 0.3064          | 0.8822   | 0.8555    | 0.8642 | 0.8596 |
| 0.1402        | 19.0  | 2318 | 0.3092          | 0.8797   | 0.8530    | 0.8599 | 0.8563 |
| 0.1301        | 20.0  | 2440 | 0.3123          | 0.8822   | 0.8548    | 0.8667 | 0.8603 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1