---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl5-4
  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-pl5-4

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: 0.3030
- Accuracy: 0.8847
- Precision: 0.8589
- Recall: 0.8659
- F1: 0.8623

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5491        | 1.0   | 122  | 0.5238          | 0.7118   | 0.6569    | 0.6636 | 0.6598 |
| 0.467         | 2.0   | 244  | 0.4078          | 0.7895   | 0.7486    | 0.7685 | 0.7564 |
| 0.3738        | 3.0   | 366  | 0.3612          | 0.8496   | 0.8538    | 0.7711 | 0.7971 |
| 0.321         | 4.0   | 488  | 0.3049          | 0.8596   | 0.8406    | 0.8107 | 0.8233 |
| 0.2799        | 5.0   | 610  | 0.2869          | 0.8772   | 0.8693    | 0.8256 | 0.8431 |
| 0.2619        | 6.0   | 732  | 0.2957          | 0.8747   | 0.8523    | 0.8413 | 0.8465 |
| 0.2475        | 7.0   | 854  | 0.2894          | 0.8722   | 0.8528    | 0.8321 | 0.8413 |
| 0.2336        | 8.0   | 976  | 0.3088          | 0.8596   | 0.8374    | 0.8157 | 0.8253 |
| 0.2233        | 9.0   | 1098 | 0.2827          | 0.8772   | 0.8561    | 0.8431 | 0.8492 |
| 0.2053        | 10.0  | 1220 | 0.2771          | 0.8897   | 0.8659    | 0.8695 | 0.8676 |
| 0.1926        | 11.0  | 1342 | 0.2792          | 0.8847   | 0.8573    | 0.8709 | 0.8636 |
| 0.1837        | 12.0  | 1464 | 0.2857          | 0.8872   | 0.8687    | 0.8552 | 0.8615 |
| 0.1748        | 13.0  | 1586 | 0.2900          | 0.8922   | 0.8706    | 0.8687 | 0.8697 |
| 0.1664        | 14.0  | 1708 | 0.3100          | 0.8872   | 0.8587    | 0.8802 | 0.8681 |
| 0.1575        | 15.0  | 1830 | 0.3073          | 0.8872   | 0.8593    | 0.8777 | 0.8675 |
| 0.1553        | 16.0  | 1952 | 0.3023          | 0.8972   | 0.8781    | 0.8723 | 0.8751 |
| 0.1439        | 17.0  | 2074 | 0.3054          | 0.8847   | 0.8581    | 0.8684 | 0.8629 |
| 0.1509        | 18.0  | 2196 | 0.3081          | 0.8847   | 0.8599    | 0.8634 | 0.8616 |
| 0.1489        | 19.0  | 2318 | 0.3018          | 0.8897   | 0.8670    | 0.8670 | 0.8670 |
| 0.146         | 20.0  | 2440 | 0.3030          | 0.8847   | 0.8589    | 0.8659 | 0.8623 |


### Framework versions

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