---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-base-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-base-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: 0.7891
- Accuracy: 0.8972
- Precision: 0.8796
- Recall: 0.8698
- F1: 0.8745

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.411         | 1.0   | 122  | 0.2751          | 0.8722   | 0.8474    | 0.8421 | 0.8446 |
| 0.2264        | 2.0   | 244  | 0.3037          | 0.8872   | 0.8574    | 0.8977 | 0.8719 |
| 0.1467        | 3.0   | 366  | 0.3442          | 0.8772   | 0.8465    | 0.8756 | 0.8582 |
| 0.0961        | 4.0   | 488  | 0.3737          | 0.8997   | 0.8819    | 0.8741 | 0.8778 |
| 0.0726        | 5.0   | 610  | 0.4306          | 0.8997   | 0.8835    | 0.8716 | 0.8772 |
| 0.0514        | 6.0   | 732  | 0.6449          | 0.8847   | 0.8546    | 0.8884 | 0.8677 |
| 0.0532        | 7.0   | 854  | 0.5595          | 0.8972   | 0.8754    | 0.8773 | 0.8764 |
| 0.0274        | 8.0   | 976  | 0.6728          | 0.8872   | 0.8687    | 0.8552 | 0.8615 |
| 0.0186        | 9.0   | 1098 | 0.6218          | 0.9073   | 0.8977    | 0.8744 | 0.8849 |
| 0.0121        | 10.0  | 1220 | 0.6576          | 0.8922   | 0.8766    | 0.8587 | 0.8669 |
| 0.0244        | 11.0  | 1342 | 0.7507          | 0.8972   | 0.8940    | 0.8523 | 0.8695 |
| 0.0062        | 12.0  | 1464 | 0.6859          | 0.8972   | 0.8849    | 0.8623 | 0.8724 |
| 0.0099        | 13.0  | 1586 | 0.6514          | 0.9073   | 0.8904    | 0.8844 | 0.8873 |
| 0.0087        | 14.0  | 1708 | 0.7604          | 0.8997   | 0.8852    | 0.8691 | 0.8765 |
| 0.0056        | 15.0  | 1830 | 0.7282          | 0.9023   | 0.8875    | 0.8733 | 0.8799 |
| 0.0063        | 16.0  | 1952 | 0.6987          | 0.9123   | 0.8965    | 0.8904 | 0.8934 |
| 0.0071        | 17.0  | 2074 | 0.7402          | 0.9048   | 0.8897    | 0.8776 | 0.8833 |
| 0.0023        | 18.0  | 2196 | 0.7846          | 0.8922   | 0.8719    | 0.8662 | 0.8690 |
| 0.0043        | 19.0  | 2318 | 0.7948          | 0.8922   | 0.8719    | 0.8662 | 0.8690 |
| 0.0021        | 20.0  | 2440 | 0.7891          | 0.8972   | 0.8796    | 0.8698 | 0.8745 |


### Framework versions

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