---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl5-3
  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-pl5-3

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: 0.2683
- Accuracy: 0.9023
- Precision: 0.8828
- Recall: 0.8808
- F1: 0.8818

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5455        | 1.0   | 122  | 0.4877          | 0.7544   | 0.7053    | 0.6512 | 0.6639 |
| 0.4356        | 2.0   | 244  | 0.3537          | 0.8446   | 0.8103    | 0.8376 | 0.8210 |
| 0.3468        | 3.0   | 366  | 0.3416          | 0.8496   | 0.8326    | 0.7911 | 0.8073 |
| 0.3049        | 4.0   | 488  | 0.3126          | 0.8546   | 0.8324    | 0.8071 | 0.8180 |
| 0.2673        | 5.0   | 610  | 0.2919          | 0.8596   | 0.8299    | 0.8332 | 0.8315 |
| 0.2516        | 6.0   | 732  | 0.2823          | 0.8647   | 0.8387    | 0.8317 | 0.8351 |
| 0.2243        | 7.0   | 854  | 0.2688          | 0.8822   | 0.8530    | 0.8742 | 0.8622 |
| 0.2157        | 8.0   | 976  | 0.2641          | 0.8947   | 0.8807    | 0.8605 | 0.8697 |
| 0.2052        | 9.0   | 1098 | 0.2627          | 0.8847   | 0.8679    | 0.8484 | 0.8573 |
| 0.1864        | 10.0  | 1220 | 0.2881          | 0.8847   | 0.8737    | 0.8409 | 0.8548 |
| 0.1928        | 11.0  | 1342 | 0.2785          | 0.8872   | 0.8593    | 0.8777 | 0.8675 |
| 0.1804        | 12.0  | 1464 | 0.2506          | 0.8997   | 0.8871    | 0.8666 | 0.8759 |
| 0.1654        | 13.0  | 1586 | 0.2664          | 0.8997   | 0.8791    | 0.8791 | 0.8791 |
| 0.1567        | 14.0  | 1708 | 0.2661          | 0.9048   | 0.8798    | 0.8976 | 0.8878 |
| 0.1438        | 15.0  | 1830 | 0.2615          | 0.9098   | 0.8898    | 0.8937 | 0.8917 |
| 0.1472        | 16.0  | 1952 | 0.2555          | 0.9048   | 0.8838    | 0.8876 | 0.8857 |
| 0.1394        | 17.0  | 2074 | 0.2648          | 0.8997   | 0.8791    | 0.8791 | 0.8791 |
| 0.1387        | 18.0  | 2196 | 0.2630          | 0.9048   | 0.8826    | 0.8901 | 0.8862 |
| 0.1378        | 19.0  | 2318 | 0.2689          | 0.9048   | 0.8865    | 0.8826 | 0.8845 |
| 0.1365        | 20.0  | 2440 | 0.2683          | 0.9023   | 0.8828    | 0.8808 | 0.8818 |


### Framework versions

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