---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl10-3
  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-pl10-3

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: 0.3389
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5509        | 1.0   | 122  | 0.4983          | 0.7393   | 0.6801    | 0.6406 | 0.6507 |
| 0.4511        | 2.0   | 244  | 0.4377          | 0.7769   | 0.7547    | 0.8022 | 0.7593 |
| 0.368         | 3.0   | 366  | 0.3260          | 0.8571   | 0.8381    | 0.8064 | 0.8196 |
| 0.3019        | 4.0   | 488  | 0.3036          | 0.8647   | 0.8410    | 0.8267 | 0.8333 |
| 0.2668        | 5.0   | 610  | 0.3192          | 0.8672   | 0.8372    | 0.8485 | 0.8425 |
| 0.2471        | 6.0   | 732  | 0.3059          | 0.8622   | 0.8305    | 0.8475 | 0.8380 |
| 0.2422        | 7.0   | 854  | 0.2950          | 0.8747   | 0.8451    | 0.8613 | 0.8524 |
| 0.2258        | 8.0   | 976  | 0.2928          | 0.8722   | 0.8463    | 0.8446 | 0.8454 |
| 0.2054        | 9.0   | 1098 | 0.3049          | 0.8797   | 0.8572    | 0.8499 | 0.8534 |
| 0.2009        | 10.0  | 1220 | 0.3013          | 0.8747   | 0.8488    | 0.8488 | 0.8488 |
| 0.1755        | 11.0  | 1342 | 0.3070          | 0.8822   | 0.8574    | 0.8592 | 0.8583 |
| 0.1821        | 12.0  | 1464 | 0.2995          | 0.8822   | 0.8596    | 0.8542 | 0.8568 |
| 0.1652        | 13.0  | 1586 | 0.3272          | 0.8847   | 0.8553    | 0.8809 | 0.8660 |
| 0.1566        | 14.0  | 1708 | 0.3336          | 0.8897   | 0.8609    | 0.8870 | 0.8719 |
| 0.1634        | 15.0  | 1830 | 0.3150          | 0.8847   | 0.8589    | 0.8659 | 0.8623 |
| 0.1496        | 16.0  | 1952 | 0.3321          | 0.8922   | 0.8706    | 0.8687 | 0.8697 |
| 0.1355        | 17.0  | 2074 | 0.3276          | 0.8847   | 0.8599    | 0.8634 | 0.8616 |
| 0.1477        | 18.0  | 2196 | 0.3365          | 0.8797   | 0.8530    | 0.8599 | 0.8563 |
| 0.1317        | 19.0  | 2318 | 0.3385          | 0.8822   | 0.8574    | 0.8592 | 0.8583 |
| 0.1267        | 20.0  | 2440 | 0.3389          | 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