---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl30-3
  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-pl30-3

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: 0.2943
- Accuracy: 0.8922
- Precision: 0.8706
- Recall: 0.8687
- F1: 0.8697

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5452        | 1.0   | 122  | 0.4919          | 0.7469   | 0.6922    | 0.6459 | 0.6573 |
| 0.4299        | 2.0   | 244  | 0.4071          | 0.8070   | 0.7802    | 0.8285 | 0.7892 |
| 0.3291        | 3.0   | 366  | 0.3091          | 0.8672   | 0.8412    | 0.8360 | 0.8385 |
| 0.2887        | 4.0   | 488  | 0.3033          | 0.8521   | 0.8237    | 0.8154 | 0.8193 |
| 0.2579        | 5.0   | 610  | 0.2880          | 0.8647   | 0.8340    | 0.8467 | 0.8399 |
| 0.232         | 6.0   | 732  | 0.2919          | 0.8747   | 0.8443    | 0.8663 | 0.8537 |
| 0.2181        | 7.0   | 854  | 0.2797          | 0.8822   | 0.8574    | 0.8592 | 0.8583 |
| 0.2114        | 8.0   | 976  | 0.2828          | 0.8747   | 0.8488    | 0.8488 | 0.8488 |
| 0.199         | 9.0   | 1098 | 0.2835          | 0.8797   | 0.8522    | 0.8624 | 0.8570 |
| 0.189         | 10.0  | 1220 | 0.2816          | 0.8772   | 0.8547    | 0.8456 | 0.8500 |
| 0.1738        | 11.0  | 1342 | 0.2905          | 0.8822   | 0.8574    | 0.8592 | 0.8583 |
| 0.1688        | 12.0  | 1464 | 0.3152          | 0.8822   | 0.8674    | 0.8417 | 0.8529 |
| 0.1655        | 13.0  | 1586 | 0.2901          | 0.8697   | 0.8449    | 0.8378 | 0.8412 |
| 0.1467        | 14.0  | 1708 | 0.2955          | 0.8797   | 0.8515    | 0.8649 | 0.8577 |
| 0.1442        | 15.0  | 1830 | 0.2866          | 0.8822   | 0.8564    | 0.8617 | 0.8590 |
| 0.1419        | 16.0  | 1952 | 0.2902          | 0.8847   | 0.8599    | 0.8634 | 0.8616 |
| 0.1416        | 17.0  | 2074 | 0.2898          | 0.8897   | 0.8659    | 0.8695 | 0.8676 |
| 0.1389        | 18.0  | 2196 | 0.2956          | 0.8872   | 0.8658    | 0.8602 | 0.8629 |
| 0.1401        | 19.0  | 2318 | 0.2937          | 0.8922   | 0.8706    | 0.8687 | 0.8697 |
| 0.1348        | 20.0  | 2440 | 0.2943          | 0.8922   | 0.8706    | 0.8687 | 0.8697 |


### Framework versions

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