---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl20-3
  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-pl20-3

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: 0.3253
- Accuracy: 0.8822
- Precision: 0.8564
- Recall: 0.8617
- F1: 0.8590

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5445        | 1.0   | 122  | 0.4894          | 0.7519   | 0.6988    | 0.6594 | 0.6708 |
| 0.432         | 2.0   | 244  | 0.3691          | 0.8195   | 0.7843    | 0.8173 | 0.7954 |
| 0.3342        | 3.0   | 366  | 0.3301          | 0.8496   | 0.8574    | 0.7686 | 0.7957 |
| 0.2846        | 4.0   | 488  | 0.2886          | 0.8797   | 0.8633    | 0.8399 | 0.8502 |
| 0.2621        | 5.0   | 610  | 0.2728          | 0.8747   | 0.8488    | 0.8488 | 0.8488 |
| 0.2419        | 6.0   | 732  | 0.2753          | 0.8747   | 0.8471    | 0.8538 | 0.8503 |
| 0.2132        | 7.0   | 854  | 0.2753          | 0.8847   | 0.8589    | 0.8659 | 0.8623 |
| 0.2055        | 8.0   | 976  | 0.2791          | 0.8797   | 0.8560    | 0.8524 | 0.8541 |
| 0.1903        | 9.0   | 1098 | 0.3009          | 0.8622   | 0.8373    | 0.8250 | 0.8307 |
| 0.1852        | 10.0  | 1220 | 0.3085          | 0.8672   | 0.8479    | 0.8235 | 0.8342 |
| 0.1758        | 11.0  | 1342 | 0.2852          | 0.8797   | 0.8539    | 0.8574 | 0.8556 |
| 0.1617        | 12.0  | 1464 | 0.3011          | 0.8872   | 0.8634    | 0.8652 | 0.8643 |
| 0.1581        | 13.0  | 1586 | 0.3050          | 0.8922   | 0.8694    | 0.8712 | 0.8703 |
| 0.149         | 14.0  | 1708 | 0.3143          | 0.8922   | 0.8639    | 0.8888 | 0.8745 |
| 0.1386        | 15.0  | 1830 | 0.3028          | 0.8997   | 0.8791    | 0.8791 | 0.8791 |
| 0.1465        | 16.0  | 1952 | 0.3112          | 0.8922   | 0.8706    | 0.8687 | 0.8697 |
| 0.1307        | 17.0  | 2074 | 0.3198          | 0.8847   | 0.8581    | 0.8684 | 0.8629 |
| 0.1231        | 18.0  | 2196 | 0.3253          | 0.8847   | 0.8609    | 0.8609 | 0.8609 |
| 0.1344        | 19.0  | 2318 | 0.3290          | 0.8797   | 0.8560    | 0.8524 | 0.8541 |
| 0.1229        | 20.0  | 2440 | 0.3253          | 0.8822   | 0.8564    | 0.8617 | 0.8590 |


### Framework versions

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