---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-seq_bn-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-seq_bn-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: 0.3205
- Accuracy: 0.8772
- Precision: 0.8609
- Recall: 0.8356
- F1: 0.8467

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5517        | 1.0   | 122  | 0.5131          | 0.7168   | 0.6513    | 0.6371 | 0.6424 |
| 0.4833        | 2.0   | 244  | 0.4657          | 0.7519   | 0.7088    | 0.7295 | 0.7159 |
| 0.4318        | 3.0   | 366  | 0.4056          | 0.8120   | 0.7729    | 0.7845 | 0.7781 |
| 0.3905        | 4.0   | 488  | 0.3811          | 0.8421   | 0.8092    | 0.8108 | 0.8100 |
| 0.3626        | 5.0   | 610  | 0.3652          | 0.8496   | 0.8186    | 0.8186 | 0.8186 |
| 0.3331        | 6.0   | 732  | 0.3646          | 0.8546   | 0.8214    | 0.8497 | 0.8325 |
| 0.3134        | 7.0   | 854  | 0.3440          | 0.8672   | 0.8412    | 0.8360 | 0.8385 |
| 0.2927        | 8.0   | 976  | 0.3412          | 0.8647   | 0.8359    | 0.8392 | 0.8376 |
| 0.2833        | 9.0   | 1098 | 0.3353          | 0.8647   | 0.8352    | 0.8417 | 0.8383 |
| 0.2672        | 10.0  | 1220 | 0.3296          | 0.8672   | 0.8367    | 0.8510 | 0.8432 |
| 0.2641        | 11.0  | 1342 | 0.3270          | 0.8772   | 0.8576    | 0.8406 | 0.8484 |
| 0.2549        | 12.0  | 1464 | 0.3352          | 0.8697   | 0.8558    | 0.8203 | 0.8350 |
| 0.2534        | 13.0  | 1586 | 0.3402          | 0.8697   | 0.8602    | 0.8153 | 0.8330 |
| 0.2389        | 14.0  | 1708 | 0.3208          | 0.8822   | 0.8574    | 0.8592 | 0.8583 |
| 0.2203        | 15.0  | 1830 | 0.3279          | 0.8747   | 0.8605    | 0.8288 | 0.8422 |
| 0.2298        | 16.0  | 1952 | 0.3175          | 0.8747   | 0.8552    | 0.8363 | 0.8448 |
| 0.2227        | 17.0  | 2074 | 0.3218          | 0.8747   | 0.8586    | 0.8313 | 0.8431 |
| 0.2225        | 18.0  | 2196 | 0.3178          | 0.8772   | 0.8524    | 0.8506 | 0.8515 |
| 0.2192        | 19.0  | 2318 | 0.3199          | 0.8772   | 0.8609    | 0.8356 | 0.8467 |
| 0.2229        | 20.0  | 2440 | 0.3205          | 0.8772   | 0.8609    | 0.8356 | 0.8467 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1