---
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: 0.3054
- Accuracy: 0.8872
- Precision: 0.8614
- Recall: 0.8702
- F1: 0.8656

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5456        | 1.0   | 122  | 0.4916          | 0.7469   | 0.6913    | 0.6534 | 0.6642 |
| 0.4369        | 2.0   | 244  | 0.4108          | 0.8120   | 0.7803    | 0.8220 | 0.7912 |
| 0.3316        | 3.0   | 366  | 0.3294          | 0.8571   | 0.8463    | 0.7964 | 0.8152 |
| 0.2909        | 4.0   | 488  | 0.3019          | 0.8772   | 0.8547    | 0.8456 | 0.8500 |
| 0.2584        | 5.0   | 610  | 0.3023          | 0.8697   | 0.8428    | 0.8428 | 0.8428 |
| 0.237         | 6.0   | 732  | 0.3020          | 0.8647   | 0.8359    | 0.8392 | 0.8376 |
| 0.2186        | 7.0   | 854  | 0.2989          | 0.8722   | 0.8425    | 0.8571 | 0.8491 |
| 0.2108        | 8.0   | 976  | 0.2961          | 0.8872   | 0.8687    | 0.8552 | 0.8615 |
| 0.1898        | 9.0   | 1098 | 0.3013          | 0.8747   | 0.8499    | 0.8463 | 0.8481 |
| 0.1894        | 10.0  | 1220 | 0.3231          | 0.8747   | 0.8537    | 0.8388 | 0.8457 |
| 0.1817        | 11.0  | 1342 | 0.3012          | 0.8772   | 0.8524    | 0.8506 | 0.8515 |
| 0.1723        | 12.0  | 1464 | 0.2979          | 0.8647   | 0.8377    | 0.8342 | 0.8359 |
| 0.1547        | 13.0  | 1586 | 0.2937          | 0.8697   | 0.8449    | 0.8378 | 0.8412 |
| 0.1569        | 14.0  | 1708 | 0.3065          | 0.8697   | 0.8383    | 0.8628 | 0.8486 |
| 0.1442        | 15.0  | 1830 | 0.2884          | 0.8772   | 0.8504    | 0.8556 | 0.8530 |
| 0.1435        | 16.0  | 1952 | 0.3016          | 0.8797   | 0.8530    | 0.8599 | 0.8563 |
| 0.1378        | 17.0  | 2074 | 0.3114          | 0.8772   | 0.8478    | 0.8656 | 0.8557 |
| 0.1377        | 18.0  | 2196 | 0.3096          | 0.8772   | 0.8483    | 0.8631 | 0.8550 |
| 0.1307        | 19.0  | 2318 | 0.3065          | 0.8797   | 0.8539    | 0.8574 | 0.8556 |
| 0.126         | 20.0  | 2440 | 0.3054          | 0.8872   | 0.8614    | 0.8702 | 0.8656 |


### Framework versions

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