---
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: 0.2793
- Accuracy: 0.8922
- Precision: 0.8665
- Recall: 0.8788
- F1: 0.8722

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5424        | 1.0   | 122  | 0.4762          | 0.7419   | 0.6837    | 0.6473 | 0.6575 |
| 0.4345        | 2.0   | 244  | 0.4157          | 0.7895   | 0.7581    | 0.7986 | 0.7674 |
| 0.3391        | 3.0   | 366  | 0.3388          | 0.8546   | 0.8324    | 0.8071 | 0.8180 |
| 0.2837        | 4.0   | 488  | 0.3279          | 0.8622   | 0.8342    | 0.8325 | 0.8333 |
| 0.2761        | 5.0   | 610  | 0.3132          | 0.8647   | 0.8346    | 0.8442 | 0.8391 |
| 0.2459        | 6.0   | 732  | 0.3033          | 0.8747   | 0.8440    | 0.8688 | 0.8544 |
| 0.2321        | 7.0   | 854  | 0.2871          | 0.8822   | 0.8530    | 0.8742 | 0.8622 |
| 0.2206        | 8.0   | 976  | 0.2634          | 0.8822   | 0.8610    | 0.8517 | 0.8561 |
| 0.2067        | 9.0   | 1098 | 0.2634          | 0.8922   | 0.8694    | 0.8712 | 0.8703 |
| 0.192         | 10.0  | 1220 | 0.2696          | 0.8922   | 0.8873    | 0.8462 | 0.8631 |
| 0.1866        | 11.0  | 1342 | 0.2752          | 0.8972   | 0.8691    | 0.8973 | 0.8808 |
| 0.1786        | 12.0  | 1464 | 0.2652          | 0.8972   | 0.8708    | 0.8898 | 0.8793 |
| 0.1695        | 13.0  | 1586 | 0.2536          | 0.9073   | 0.8920    | 0.8819 | 0.8867 |
| 0.1664        | 14.0  | 1708 | 0.2737          | 0.8872   | 0.8587    | 0.8802 | 0.8681 |
| 0.1521        | 15.0  | 1830 | 0.2620          | 0.9023   | 0.8802    | 0.8858 | 0.8830 |
| 0.1494        | 16.0  | 1952 | 0.3030          | 0.8922   | 0.8630    | 0.8963 | 0.8761 |
| 0.1487        | 17.0  | 2074 | 0.2702          | 0.8922   | 0.8650    | 0.8838 | 0.8734 |
| 0.1494        | 18.0  | 2196 | 0.2763          | 0.8947   | 0.8676    | 0.8880 | 0.8766 |
| 0.1334        | 19.0  | 2318 | 0.2826          | 0.8922   | 0.8650    | 0.8838 | 0.8734 |
| 0.1325        | 20.0  | 2440 | 0.2793          | 0.8922   | 0.8665    | 0.8788 | 0.8722 |


### Framework versions

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