---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl30-1
  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-pl30-1

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: 0.3434
- Accuracy: 0.8722
- Precision: 0.8485
- Recall: 0.8396
- F1: 0.8438

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5472        | 1.0   | 122  | 0.4993          | 0.7343   | 0.6726    | 0.6245 | 0.6339 |
| 0.4484        | 2.0   | 244  | 0.4157          | 0.7945   | 0.7655    | 0.8096 | 0.7744 |
| 0.3338        | 3.0   | 366  | 0.3279          | 0.8596   | 0.8510    | 0.7982 | 0.8179 |
| 0.2902        | 4.0   | 488  | 0.3037          | 0.8672   | 0.8449    | 0.8285 | 0.8360 |
| 0.2756        | 5.0   | 610  | 0.2922          | 0.8747   | 0.8499    | 0.8463 | 0.8481 |
| 0.2514        | 6.0   | 732  | 0.3059          | 0.8672   | 0.8359    | 0.8560 | 0.8446 |
| 0.2338        | 7.0   | 854  | 0.2970          | 0.8596   | 0.8278    | 0.8432 | 0.8347 |
| 0.2205        | 8.0   | 976  | 0.2967          | 0.8847   | 0.8784    | 0.8359 | 0.8531 |
| 0.2153        | 9.0   | 1098 | 0.2982          | 0.8672   | 0.8393    | 0.8410 | 0.8402 |
| 0.1969        | 10.0  | 1220 | 0.2943          | 0.8672   | 0.8423    | 0.8335 | 0.8377 |
| 0.185         | 11.0  | 1342 | 0.2973          | 0.8647   | 0.8359    | 0.8392 | 0.8376 |
| 0.1733        | 12.0  | 1464 | 0.3074          | 0.8672   | 0.8423    | 0.8335 | 0.8377 |
| 0.1616        | 13.0  | 1586 | 0.3186          | 0.8697   | 0.8460    | 0.8353 | 0.8404 |
| 0.16          | 14.0  | 1708 | 0.3222          | 0.8596   | 0.8278    | 0.8432 | 0.8347 |
| 0.1494        | 15.0  | 1830 | 0.3260          | 0.8747   | 0.8523    | 0.8413 | 0.8465 |
| 0.1501        | 16.0  | 1952 | 0.3233          | 0.8647   | 0.8359    | 0.8392 | 0.8376 |
| 0.1468        | 17.0  | 2074 | 0.3296          | 0.8672   | 0.8412    | 0.8360 | 0.8385 |
| 0.1423        | 18.0  | 2196 | 0.3367          | 0.8647   | 0.8398    | 0.8292 | 0.8342 |
| 0.1327        | 19.0  | 2318 | 0.3395          | 0.8697   | 0.8438    | 0.8403 | 0.8420 |
| 0.1413        | 20.0  | 2440 | 0.3434          | 0.8722   | 0.8485    | 0.8396 | 0.8438 |


### Framework versions

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