---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl10-2
  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-pl10-2

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: 0.3248
- Accuracy: 0.8872
- Precision: 0.8672
- Recall: 0.8577
- F1: 0.8622

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5533        | 1.0   | 122  | 0.5134          | 0.7268   | 0.6606    | 0.6242 | 0.6327 |
| 0.4779        | 2.0   | 244  | 0.4950          | 0.7419   | 0.7054    | 0.7349 | 0.7122 |
| 0.4097        | 3.0   | 366  | 0.3772          | 0.8246   | 0.8093    | 0.7459 | 0.7665 |
| 0.3451        | 4.0   | 488  | 0.3511          | 0.8446   | 0.8105    | 0.8251 | 0.8170 |
| 0.2959        | 5.0   | 610  | 0.3201          | 0.8546   | 0.8239    | 0.8272 | 0.8255 |
| 0.2727        | 6.0   | 732  | 0.3176          | 0.8647   | 0.8325    | 0.8642 | 0.8447 |
| 0.2595        | 7.0   | 854  | 0.2959          | 0.8747   | 0.8451    | 0.8613 | 0.8524 |
| 0.2409        | 8.0   | 976  | 0.2833          | 0.8897   | 0.8710    | 0.8595 | 0.8649 |
| 0.2298        | 9.0   | 1098 | 0.2894          | 0.8772   | 0.8535    | 0.8481 | 0.8507 |
| 0.2221        | 10.0  | 1220 | 0.2884          | 0.8872   | 0.8687    | 0.8552 | 0.8615 |
| 0.1986        | 11.0  | 1342 | 0.2855          | 0.8847   | 0.8648    | 0.8534 | 0.8588 |
| 0.1964        | 12.0  | 1464 | 0.2921          | 0.8822   | 0.8694    | 0.8392 | 0.8521 |
| 0.1783        | 13.0  | 1586 | 0.3104          | 0.8897   | 0.8710    | 0.8595 | 0.8649 |
| 0.1788        | 14.0  | 1708 | 0.3015          | 0.8897   | 0.8640    | 0.8745 | 0.8689 |
| 0.172         | 15.0  | 1830 | 0.3012          | 0.8847   | 0.8634    | 0.8559 | 0.8595 |
| 0.1563        | 16.0  | 1952 | 0.3159          | 0.8897   | 0.8632    | 0.8770 | 0.8695 |
| 0.1512        | 17.0  | 2074 | 0.3249          | 0.8847   | 0.8679    | 0.8484 | 0.8573 |
| 0.151         | 18.0  | 2196 | 0.3245          | 0.8822   | 0.8624    | 0.8492 | 0.8553 |
| 0.1461        | 19.0  | 2318 | 0.3282          | 0.8872   | 0.8687    | 0.8552 | 0.8615 |
| 0.1555        | 20.0  | 2440 | 0.3248          | 0.8872   | 0.8672    | 0.8577 | 0.8622 |


### Framework versions

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