---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl5-1
  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-pl5-1

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: 0.2868
- Accuracy: 0.8847
- Precision: 0.8599
- Recall: 0.8634
- F1: 0.8616

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5526        | 1.0   | 122  | 0.5135          | 0.7118   | 0.6438    | 0.6286 | 0.6339 |
| 0.4696        | 2.0   | 244  | 0.4522          | 0.7569   | 0.7328    | 0.7755 | 0.7369 |
| 0.3867        | 3.0   | 366  | 0.3466          | 0.8371   | 0.8297    | 0.7597 | 0.7824 |
| 0.3349        | 4.0   | 488  | 0.3128          | 0.8546   | 0.8395    | 0.7971 | 0.8137 |
| 0.2998        | 5.0   | 610  | 0.2932          | 0.8596   | 0.8293    | 0.8357 | 0.8324 |
| 0.2787        | 6.0   | 732  | 0.2855          | 0.8697   | 0.8419    | 0.8453 | 0.8436 |
| 0.2551        | 7.0   | 854  | 0.2898          | 0.8747   | 0.8438    | 0.8713 | 0.8550 |
| 0.2496        | 8.0   | 976  | 0.2936          | 0.8697   | 0.8653    | 0.8103 | 0.8309 |
| 0.2347        | 9.0   | 1098 | 0.2755          | 0.8847   | 0.8599    | 0.8634 | 0.8616 |
| 0.2199        | 10.0  | 1220 | 0.3038          | 0.8722   | 0.8675    | 0.8146 | 0.8347 |
| 0.2089        | 11.0  | 1342 | 0.2695          | 0.8822   | 0.8574    | 0.8592 | 0.8583 |
| 0.1992        | 12.0  | 1464 | 0.2710          | 0.8747   | 0.8488    | 0.8488 | 0.8488 |
| 0.1841        | 13.0  | 1586 | 0.2807          | 0.8722   | 0.8512    | 0.8346 | 0.8422 |
| 0.1808        | 14.0  | 1708 | 0.2822          | 0.8822   | 0.8548    | 0.8667 | 0.8603 |
| 0.1677        | 15.0  | 1830 | 0.2841          | 0.8747   | 0.8479    | 0.8513 | 0.8496 |
| 0.1683        | 16.0  | 1952 | 0.2821          | 0.8772   | 0.8496    | 0.8581 | 0.8537 |
| 0.1748        | 17.0  | 2074 | 0.2824          | 0.8797   | 0.8572    | 0.8499 | 0.8534 |
| 0.1566        | 18.0  | 2196 | 0.2847          | 0.8872   | 0.8606    | 0.8727 | 0.8662 |
| 0.1522        | 19.0  | 2318 | 0.2880          | 0.8822   | 0.8574    | 0.8592 | 0.8583 |
| 0.1566        | 20.0  | 2440 | 0.2868          | 0.8847   | 0.8599    | 0.8634 | 0.8616 |


### Framework versions

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