---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl20-1
  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-pl20-1

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: 0.2937
- Accuracy: 0.8822
- Precision: 0.8574
- Recall: 0.8592
- F1: 0.8583

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5467        | 1.0   | 122  | 0.4994          | 0.7444   | 0.6878    | 0.6466 | 0.6574 |
| 0.4498        | 2.0   | 244  | 0.4013          | 0.7970   | 0.7568    | 0.7764 | 0.7646 |
| 0.3643        | 3.0   | 366  | 0.3603          | 0.8296   | 0.8252    | 0.7444 | 0.7685 |
| 0.3131        | 4.0   | 488  | 0.3134          | 0.8546   | 0.8357    | 0.8021 | 0.8159 |
| 0.2812        | 5.0   | 610  | 0.3087          | 0.8672   | 0.8463    | 0.8260 | 0.8351 |
| 0.2579        | 6.0   | 732  | 0.3037          | 0.8747   | 0.8443    | 0.8663 | 0.8537 |
| 0.242         | 7.0   | 854  | 0.2869          | 0.8772   | 0.8514    | 0.8531 | 0.8522 |
| 0.2238        | 8.0   | 976  | 0.3086          | 0.8622   | 0.8488    | 0.8075 | 0.8239 |
| 0.2134        | 9.0   | 1098 | 0.2916          | 0.8697   | 0.8520    | 0.8253 | 0.8368 |
| 0.2014        | 10.0  | 1220 | 0.3077          | 0.8697   | 0.8579    | 0.8178 | 0.8340 |
| 0.1918        | 11.0  | 1342 | 0.2910          | 0.8672   | 0.8385    | 0.8435 | 0.8409 |
| 0.1764        | 12.0  | 1464 | 0.2865          | 0.8797   | 0.8530    | 0.8599 | 0.8563 |
| 0.1771        | 13.0  | 1586 | 0.3068          | 0.8697   | 0.8520    | 0.8253 | 0.8368 |
| 0.1708        | 14.0  | 1708 | 0.2962          | 0.8872   | 0.8587    | 0.8802 | 0.8681 |
| 0.1585        | 15.0  | 1830 | 0.2889          | 0.8872   | 0.8645    | 0.8627 | 0.8636 |
| 0.1602        | 16.0  | 1952 | 0.2941          | 0.8822   | 0.8541    | 0.8692 | 0.8609 |
| 0.1481        | 17.0  | 2074 | 0.2971          | 0.8847   | 0.8634    | 0.8559 | 0.8595 |
| 0.1536        | 18.0  | 2196 | 0.2937          | 0.8847   | 0.8621    | 0.8584 | 0.8602 |
| 0.147         | 19.0  | 2318 | 0.2946          | 0.8822   | 0.8596    | 0.8542 | 0.8568 |
| 0.1379        | 20.0  | 2440 | 0.2937          | 0.8822   | 0.8574    | 0.8592 | 0.8583 |


### Framework versions

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