---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl30-0
  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-pl30-0

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: 0.2913
- Accuracy: 0.9048
- Precision: 0.8851
- Recall: 0.8851
- F1: 0.8851

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5457        | 1.0   | 122  | 0.4753          | 0.7193   | 0.6465    | 0.5964 | 0.6014 |
| 0.4518        | 2.0   | 244  | 0.4070          | 0.7970   | 0.7589    | 0.7864 | 0.7685 |
| 0.3461        | 3.0   | 366  | 0.3412          | 0.8421   | 0.8231    | 0.7808 | 0.7970 |
| 0.2958        | 4.0   | 488  | 0.3253          | 0.8546   | 0.8263    | 0.8196 | 0.8229 |
| 0.2659        | 5.0   | 610  | 0.2941          | 0.8822   | 0.8610    | 0.8517 | 0.8561 |
| 0.2482        | 6.0   | 732  | 0.2965          | 0.8772   | 0.8473    | 0.8681 | 0.8563 |
| 0.2264        | 7.0   | 854  | 0.2869          | 0.8747   | 0.8447    | 0.8638 | 0.8531 |
| 0.2218        | 8.0   | 976  | 0.2795          | 0.8997   | 0.8961    | 0.8566 | 0.8730 |
| 0.2106        | 9.0   | 1098 | 0.2705          | 0.8922   | 0.8673    | 0.8763 | 0.8716 |
| 0.1981        | 10.0  | 1220 | 0.2751          | 0.9073   | 0.8920    | 0.8819 | 0.8867 |
| 0.1802        | 11.0  | 1342 | 0.2745          | 0.9048   | 0.8826    | 0.8901 | 0.8862 |
| 0.1828        | 12.0  | 1464 | 0.2799          | 0.9073   | 0.8957    | 0.8769 | 0.8855 |
| 0.1707        | 13.0  | 1586 | 0.2739          | 0.9098   | 0.8960    | 0.8837 | 0.8895 |
| 0.1606        | 14.0  | 1708 | 0.2868          | 0.9073   | 0.8862    | 0.8919 | 0.8890 |
| 0.1499        | 15.0  | 1830 | 0.2930          | 0.9023   | 0.8828    | 0.8808 | 0.8818 |
| 0.1555        | 16.0  | 1952 | 0.3041          | 0.8947   | 0.8682    | 0.8855 | 0.8760 |
| 0.1396        | 17.0  | 2074 | 0.2876          | 0.9023   | 0.8814    | 0.8833 | 0.8824 |
| 0.1477        | 18.0  | 2196 | 0.2900          | 0.9048   | 0.8865    | 0.8826 | 0.8845 |
| 0.1434        | 19.0  | 2318 | 0.2917          | 0.9048   | 0.8851    | 0.8851 | 0.8851 |
| 0.1386        | 20.0  | 2440 | 0.2913          | 0.9048   | 0.8851    | 0.8851 | 0.8851 |


### Framework versions

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