---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl10-1
  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-pl10-1

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: 0.3011
- Accuracy: 0.8972
- Precision: 0.8754
- Recall: 0.8773
- F1: 0.8764

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5535        | 1.0   | 122  | 0.5078          | 0.7268   | 0.6606    | 0.6242 | 0.6327 |
| 0.4682        | 2.0   | 244  | 0.4185          | 0.8170   | 0.7798    | 0.7756 | 0.7776 |
| 0.3849        | 3.0   | 366  | 0.3809          | 0.8170   | 0.7968    | 0.7380 | 0.7573 |
| 0.3127        | 4.0   | 488  | 0.3280          | 0.8571   | 0.8266    | 0.8314 | 0.8289 |
| 0.2869        | 5.0   | 610  | 0.3169          | 0.8622   | 0.8333    | 0.8350 | 0.8341 |
| 0.274         | 6.0   | 732  | 0.3218          | 0.8772   | 0.8467    | 0.8731 | 0.8576 |
| 0.2539        | 7.0   | 854  | 0.3038          | 0.8672   | 0.8378    | 0.8460 | 0.8417 |
| 0.2286        | 8.0   | 976  | 0.3202          | 0.8672   | 0.8479    | 0.8235 | 0.8342 |
| 0.2249        | 9.0   | 1098 | 0.2973          | 0.8872   | 0.8606    | 0.8727 | 0.8662 |
| 0.2083        | 10.0  | 1220 | 0.3128          | 0.8722   | 0.8602    | 0.8221 | 0.8377 |
| 0.1935        | 11.0  | 1342 | 0.2957          | 0.8922   | 0.8665    | 0.8788 | 0.8722 |
| 0.1859        | 12.0  | 1464 | 0.2869          | 0.8822   | 0.8548    | 0.8667 | 0.8603 |
| 0.1735        | 13.0  | 1586 | 0.3061          | 0.8797   | 0.8633    | 0.8399 | 0.8502 |
| 0.1804        | 14.0  | 1708 | 0.2955          | 0.8897   | 0.8632    | 0.8770 | 0.8695 |
| 0.1628        | 15.0  | 1830 | 0.2973          | 0.8972   | 0.8767    | 0.8748 | 0.8757 |
| 0.1619        | 16.0  | 1952 | 0.3023          | 0.8897   | 0.8618    | 0.8820 | 0.8707 |
| 0.1514        | 17.0  | 2074 | 0.2997          | 0.8972   | 0.8732    | 0.8823 | 0.8776 |
| 0.1503        | 18.0  | 2196 | 0.3002          | 0.8947   | 0.8718    | 0.8755 | 0.8737 |
| 0.154         | 19.0  | 2318 | 0.3031          | 0.8947   | 0.8730    | 0.8730 | 0.8730 |
| 0.1408        | 20.0  | 2440 | 0.3011          | 0.8972   | 0.8754    | 0.8773 | 0.8764 |


### Framework versions

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