---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl10-1
  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-pl10-1

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: 0.3090
- Accuracy: 0.8847
- Precision: 0.8609
- Recall: 0.8609
- F1: 0.8609

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5535        | 1.0   | 122  | 0.5041          | 0.7243   | 0.6584    | 0.6324 | 0.6401 |
| 0.4636        | 2.0   | 244  | 0.4692          | 0.7669   | 0.7253    | 0.7476 | 0.7331 |
| 0.4023        | 3.0   | 366  | 0.3605          | 0.8371   | 0.8326    | 0.7572 | 0.7809 |
| 0.3202        | 4.0   | 488  | 0.3256          | 0.8546   | 0.8357    | 0.8021 | 0.8159 |
| 0.2919        | 5.0   | 610  | 0.3067          | 0.8772   | 0.8592    | 0.8381 | 0.8475 |
| 0.2657        | 6.0   | 732  | 0.3400          | 0.8521   | 0.8193    | 0.8554 | 0.8320 |
| 0.2559        | 7.0   | 854  | 0.2993          | 0.8747   | 0.8451    | 0.8613 | 0.8524 |
| 0.2369        | 8.0   | 976  | 0.3018          | 0.8872   | 0.8760    | 0.8452 | 0.8584 |
| 0.2178        | 9.0   | 1098 | 0.2926          | 0.8847   | 0.8634    | 0.8559 | 0.8595 |
| 0.2118        | 10.0  | 1220 | 0.2955          | 0.8872   | 0.8672    | 0.8577 | 0.8622 |
| 0.2034        | 11.0  | 1342 | 0.2934          | 0.8847   | 0.8679    | 0.8484 | 0.8573 |
| 0.1856        | 12.0  | 1464 | 0.2978          | 0.8797   | 0.8572    | 0.8499 | 0.8534 |
| 0.1775        | 13.0  | 1586 | 0.3039          | 0.8797   | 0.8651    | 0.8374 | 0.8494 |
| 0.1719        | 14.0  | 1708 | 0.3036          | 0.8872   | 0.8672    | 0.8577 | 0.8622 |
| 0.1621        | 15.0  | 1830 | 0.2990          | 0.8822   | 0.8555    | 0.8642 | 0.8596 |
| 0.1535        | 16.0  | 1952 | 0.3040          | 0.8847   | 0.8599    | 0.8634 | 0.8616 |
| 0.1504        | 17.0  | 2074 | 0.3190          | 0.8797   | 0.8616    | 0.8424 | 0.8510 |
| 0.1459        | 18.0  | 2196 | 0.3101          | 0.8772   | 0.8514    | 0.8531 | 0.8522 |
| 0.1444        | 19.0  | 2318 | 0.3119          | 0.8822   | 0.8624    | 0.8492 | 0.8553 |
| 0.1384        | 20.0  | 2440 | 0.3090          | 0.8847   | 0.8609    | 0.8609 | 0.8609 |


### Framework versions

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