---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl20-3
  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-pl20-3

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: 0.2974
- Accuracy: 0.8847
- Precision: 0.8609
- Recall: 0.8609
- F1: 0.8609

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.551         | 1.0   | 122  | 0.5009          | 0.7243   | 0.6557    | 0.6074 | 0.6144 |
| 0.4528        | 2.0   | 244  | 0.4118          | 0.7820   | 0.7487    | 0.7857 | 0.7578 |
| 0.3588        | 3.0   | 366  | 0.3428          | 0.8521   | 0.8440    | 0.7854 | 0.8064 |
| 0.3192        | 4.0   | 488  | 0.3117          | 0.8546   | 0.8246    | 0.8246 | 0.8246 |
| 0.2714        | 5.0   | 610  | 0.3037          | 0.8672   | 0.8359    | 0.8560 | 0.8446 |
| 0.257         | 6.0   | 732  | 0.2833          | 0.8772   | 0.8592    | 0.8381 | 0.8475 |
| 0.2405        | 7.0   | 854  | 0.2861          | 0.8847   | 0.8599    | 0.8634 | 0.8616 |
| 0.2163        | 8.0   | 976  | 0.2954          | 0.8797   | 0.8539    | 0.8574 | 0.8556 |
| 0.2135        | 9.0   | 1098 | 0.2942          | 0.8747   | 0.8510    | 0.8438 | 0.8473 |
| 0.2001        | 10.0  | 1220 | 0.3002          | 0.8822   | 0.8656    | 0.8442 | 0.8537 |
| 0.1825        | 11.0  | 1342 | 0.3011          | 0.8922   | 0.8749    | 0.8612 | 0.8676 |
| 0.1765        | 12.0  | 1464 | 0.2858          | 0.8897   | 0.8695    | 0.8620 | 0.8656 |
| 0.1674        | 13.0  | 1586 | 0.2932          | 0.8947   | 0.8698    | 0.8805 | 0.8749 |
| 0.1597        | 14.0  | 1708 | 0.2937          | 0.8872   | 0.8599    | 0.8752 | 0.8669 |
| 0.1564        | 15.0  | 1830 | 0.2963          | 0.8947   | 0.8757    | 0.8680 | 0.8717 |
| 0.142         | 16.0  | 1952 | 0.3025          | 0.8922   | 0.8734    | 0.8637 | 0.8683 |
| 0.143         | 17.0  | 2074 | 0.2951          | 0.8897   | 0.8649    | 0.8720 | 0.8683 |
| 0.1315        | 18.0  | 2196 | 0.3013          | 0.8822   | 0.8574    | 0.8592 | 0.8583 |
| 0.1378        | 19.0  | 2318 | 0.3038          | 0.8872   | 0.8658    | 0.8602 | 0.8629 |
| 0.1333        | 20.0  | 2440 | 0.2974          | 0.8847   | 0.8609    | 0.8609 | 0.8609 |


### Framework versions

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