---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl10-4
  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-pl10-4

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: 0.2951
- Accuracy: 0.8847
- Precision: 0.8609
- Recall: 0.8609
- F1: 0.8609

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5448        | 1.0   | 122  | 0.5047          | 0.7243   | 0.6629    | 0.6524 | 0.6568 |
| 0.4527        | 2.0   | 244  | 0.4320          | 0.7945   | 0.7667    | 0.8121 | 0.7752 |
| 0.3603        | 3.0   | 366  | 0.3370          | 0.8471   | 0.8393    | 0.7768 | 0.7985 |
| 0.3081        | 4.0   | 488  | 0.2995          | 0.8722   | 0.8453    | 0.8471 | 0.8462 |
| 0.2793        | 5.0   | 610  | 0.3008          | 0.8747   | 0.8537    | 0.8388 | 0.8457 |
| 0.2526        | 6.0   | 732  | 0.2987          | 0.8697   | 0.8449    | 0.8378 | 0.8412 |
| 0.2478        | 7.0   | 854  | 0.3030          | 0.8772   | 0.8609    | 0.8356 | 0.8467 |
| 0.2337        | 8.0   | 976  | 0.2974          | 0.8672   | 0.8463    | 0.8260 | 0.8351 |
| 0.217         | 9.0   | 1098 | 0.2774          | 0.8722   | 0.8562    | 0.8271 | 0.8395 |
| 0.1966        | 10.0  | 1220 | 0.2846          | 0.8697   | 0.8411    | 0.8478 | 0.8443 |
| 0.199         | 11.0  | 1342 | 0.2910          | 0.8822   | 0.8639    | 0.8467 | 0.8545 |
| 0.187         | 12.0  | 1464 | 0.2871          | 0.8772   | 0.8609    | 0.8356 | 0.8467 |
| 0.1812        | 13.0  | 1586 | 0.2813          | 0.8797   | 0.8585    | 0.8474 | 0.8526 |
| 0.1633        | 14.0  | 1708 | 0.2957          | 0.8822   | 0.8555    | 0.8642 | 0.8596 |
| 0.1607        | 15.0  | 1830 | 0.2875          | 0.8922   | 0.8706    | 0.8687 | 0.8697 |
| 0.1584        | 16.0  | 1952 | 0.2859          | 0.8822   | 0.8610    | 0.8517 | 0.8561 |
| 0.1535        | 17.0  | 2074 | 0.2924          | 0.8847   | 0.8609    | 0.8609 | 0.8609 |
| 0.1432        | 18.0  | 2196 | 0.2966          | 0.8847   | 0.8599    | 0.8634 | 0.8616 |
| 0.1466        | 19.0  | 2318 | 0.2947          | 0.8822   | 0.8596    | 0.8542 | 0.8568 |
| 0.1411        | 20.0  | 2440 | 0.2951          | 0.8847   | 0.8609    | 0.8609 | 0.8609 |


### Framework versions

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