---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl30-4
  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-pl30-4

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: 0.3168
- Accuracy: 0.8797
- Precision: 0.8530
- Recall: 0.8599
- F1: 0.8563

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5413        | 1.0   | 122  | 0.5007          | 0.7243   | 0.6593    | 0.6374 | 0.6446 |
| 0.4584        | 2.0   | 244  | 0.3856          | 0.8296   | 0.8123    | 0.7569 | 0.7761 |
| 0.3559        | 3.0   | 366  | 0.3407          | 0.8571   | 0.8638    | 0.7814 | 0.8079 |
| 0.2961        | 4.0   | 488  | 0.3089          | 0.8697   | 0.8438    | 0.8403 | 0.8420 |
| 0.276         | 5.0   | 610  | 0.2917          | 0.8622   | 0.8314    | 0.8425 | 0.8365 |
| 0.2555        | 6.0   | 732  | 0.2905          | 0.8697   | 0.8428    | 0.8428 | 0.8428 |
| 0.2427        | 7.0   | 854  | 0.3031          | 0.8772   | 0.8670    | 0.8281 | 0.8440 |
| 0.2219        | 8.0   | 976  | 0.2908          | 0.8772   | 0.8514    | 0.8531 | 0.8522 |
| 0.2158        | 9.0   | 1098 | 0.3084          | 0.8847   | 0.8760    | 0.8384 | 0.8540 |
| 0.2           | 10.0  | 1220 | 0.2938          | 0.8747   | 0.8457    | 0.8588 | 0.8517 |
| 0.1885        | 11.0  | 1342 | 0.2977          | 0.8772   | 0.8524    | 0.8506 | 0.8515 |
| 0.183         | 12.0  | 1464 | 0.3070          | 0.8847   | 0.8717    | 0.8434 | 0.8557 |
| 0.1752        | 13.0  | 1586 | 0.2959          | 0.8797   | 0.8522    | 0.8624 | 0.8570 |
| 0.1558        | 14.0  | 1708 | 0.3040          | 0.8747   | 0.8447    | 0.8638 | 0.8531 |
| 0.1538        | 15.0  | 1830 | 0.3082          | 0.8722   | 0.8431    | 0.8546 | 0.8484 |
| 0.152         | 16.0  | 1952 | 0.3100          | 0.8772   | 0.8576    | 0.8406 | 0.8484 |
| 0.1436        | 17.0  | 2074 | 0.3105          | 0.8747   | 0.8463    | 0.8563 | 0.8510 |
| 0.1426        | 18.0  | 2196 | 0.3119          | 0.8747   | 0.8471    | 0.8538 | 0.8503 |
| 0.1398        | 19.0  | 2318 | 0.3164          | 0.8797   | 0.8522    | 0.8624 | 0.8570 |
| 0.14          | 20.0  | 2440 | 0.3168          | 0.8797   | 0.8530    | 0.8599 | 0.8563 |


### Framework versions

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