---
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: 0.2998
- Accuracy: 0.8997
- Precision: 0.8804
- Recall: 0.8766
- F1: 0.8785

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5411        | 1.0   | 122  | 0.4939          | 0.7368   | 0.6762    | 0.6413 | 0.6509 |
| 0.4231        | 2.0   | 244  | 0.3852          | 0.8246   | 0.7888    | 0.8184 | 0.7995 |
| 0.3331        | 3.0   | 366  | 0.3313          | 0.8471   | 0.8233    | 0.7968 | 0.8081 |
| 0.2924        | 4.0   | 488  | 0.3057          | 0.8822   | 0.8610    | 0.8517 | 0.8561 |
| 0.2705        | 5.0   | 610  | 0.3069          | 0.8747   | 0.8605    | 0.8288 | 0.8422 |
| 0.2461        | 6.0   | 732  | 0.3119          | 0.8747   | 0.8436    | 0.8763 | 0.8562 |
| 0.2313        | 7.0   | 854  | 0.2880          | 0.8872   | 0.8606    | 0.8727 | 0.8662 |
| 0.2183        | 8.0   | 976  | 0.2773          | 0.8922   | 0.8749    | 0.8612 | 0.8676 |
| 0.2093        | 9.0   | 1098 | 0.2804          | 0.8847   | 0.8648    | 0.8534 | 0.8588 |
| 0.1986        | 10.0  | 1220 | 0.2890          | 0.8922   | 0.8804    | 0.8537 | 0.8655 |
| 0.1881        | 11.0  | 1342 | 0.2911          | 0.8872   | 0.8658    | 0.8602 | 0.8629 |
| 0.1802        | 12.0  | 1464 | 0.2866          | 0.8822   | 0.8596    | 0.8542 | 0.8568 |
| 0.169         | 13.0  | 1586 | 0.2964          | 0.8847   | 0.8697    | 0.8459 | 0.8565 |
| 0.1709        | 14.0  | 1708 | 0.2944          | 0.8872   | 0.8658    | 0.8602 | 0.8629 |
| 0.1492        | 15.0  | 1830 | 0.2866          | 0.8872   | 0.8645    | 0.8627 | 0.8636 |
| 0.1493        | 16.0  | 1952 | 0.2951          | 0.8947   | 0.8708    | 0.8780 | 0.8743 |
| 0.1425        | 17.0  | 2074 | 0.3048          | 0.8947   | 0.8773    | 0.8655 | 0.8711 |
| 0.1375        | 18.0  | 2196 | 0.2987          | 0.8997   | 0.8791    | 0.8791 | 0.8791 |
| 0.1326        | 19.0  | 2318 | 0.3073          | 0.8997   | 0.8819    | 0.8741 | 0.8778 |
| 0.1365        | 20.0  | 2440 | 0.2998          | 0.8997   | 0.8804    | 0.8766 | 0.8785 |


### Framework versions

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