---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl30-2
  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-pl30-2

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: 0.2988
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.541         | 1.0   | 122  | 0.4985          | 0.7293   | 0.6648    | 0.6310 | 0.6397 |
| 0.4477        | 2.0   | 244  | 0.4465          | 0.7644   | 0.7427    | 0.7883 | 0.7462 |
| 0.347         | 3.0   | 366  | 0.3237          | 0.8647   | 0.8556    | 0.8067 | 0.8255 |
| 0.3005        | 4.0   | 488  | 0.2992          | 0.8922   | 0.8734    | 0.8637 | 0.8683 |
| 0.281         | 5.0   | 610  | 0.2869          | 0.8647   | 0.8398    | 0.8292 | 0.8342 |
| 0.2419        | 6.0   | 732  | 0.2945          | 0.8747   | 0.8443    | 0.8663 | 0.8537 |
| 0.2394        | 7.0   | 854  | 0.2835          | 0.8772   | 0.8504    | 0.8556 | 0.8530 |
| 0.2192        | 8.0   | 976  | 0.2803          | 0.8772   | 0.8535    | 0.8481 | 0.8507 |
| 0.2144        | 9.0   | 1098 | 0.2861          | 0.8747   | 0.8499    | 0.8463 | 0.8481 |
| 0.2056        | 10.0  | 1220 | 0.2724          | 0.8922   | 0.8706    | 0.8687 | 0.8697 |
| 0.1822        | 11.0  | 1342 | 0.2813          | 0.8872   | 0.8606    | 0.8727 | 0.8662 |
| 0.1817        | 12.0  | 1464 | 0.2900          | 0.8872   | 0.8760    | 0.8452 | 0.8584 |
| 0.1621        | 13.0  | 1586 | 0.2926          | 0.8947   | 0.8773    | 0.8655 | 0.8711 |
| 0.1577        | 14.0  | 1708 | 0.2904          | 0.8922   | 0.8683    | 0.8737 | 0.8710 |
| 0.1612        | 15.0  | 1830 | 0.2996          | 0.8847   | 0.8648    | 0.8534 | 0.8588 |
| 0.1496        | 16.0  | 1952 | 0.2970          | 0.8872   | 0.8624    | 0.8677 | 0.8650 |
| 0.149         | 17.0  | 2074 | 0.2948          | 0.8822   | 0.8574    | 0.8592 | 0.8583 |
| 0.1424        | 18.0  | 2196 | 0.2977          | 0.8847   | 0.8609    | 0.8609 | 0.8609 |
| 0.1383        | 19.0  | 2318 | 0.2990          | 0.8847   | 0.8621    | 0.8584 | 0.8602 |
| 0.1407        | 20.0  | 2440 | 0.2988          | 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