---
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-rf16-0
  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-rf16-0

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: 0.3129
- Accuracy: 0.8747
- Precision: 0.8479
- Recall: 0.8513
- F1: 0.8496

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5551        | 1.0   | 122  | 0.4975          | 0.7168   | 0.6485    | 0.6271 | 0.6337 |
| 0.4753        | 2.0   | 244  | 0.4682          | 0.7419   | 0.7118    | 0.7474 | 0.7171 |
| 0.412         | 3.0   | 366  | 0.3882          | 0.8271   | 0.7994    | 0.7676 | 0.7804 |
| 0.3498        | 4.0   | 488  | 0.3691          | 0.8421   | 0.8098    | 0.8083 | 0.8091 |
| 0.3361        | 5.0   | 610  | 0.3795          | 0.8145   | 0.7789    | 0.8113 | 0.7897 |
| 0.3081        | 6.0   | 732  | 0.4142          | 0.7970   | 0.7665    | 0.8089 | 0.7761 |
| 0.2918        | 7.0   | 854  | 0.3555          | 0.8471   | 0.8130    | 0.8393 | 0.8235 |
| 0.2739        | 8.0   | 976  | 0.3317          | 0.8647   | 0.8439    | 0.8217 | 0.8315 |
| 0.2586        | 9.0   | 1098 | 0.3596          | 0.8571   | 0.8243    | 0.8464 | 0.8336 |
| 0.2509        | 10.0  | 1220 | 0.3299          | 0.8622   | 0.8326    | 0.8375 | 0.8349 |
| 0.2468        | 11.0  | 1342 | 0.3224          | 0.8672   | 0.8393    | 0.8410 | 0.8402 |
| 0.2372        | 12.0  | 1464 | 0.3294          | 0.8571   | 0.8251    | 0.8389 | 0.8314 |
| 0.2305        | 13.0  | 1586 | 0.3134          | 0.8697   | 0.8449    | 0.8378 | 0.8412 |
| 0.2249        | 14.0  | 1708 | 0.3225          | 0.8697   | 0.8399    | 0.8528 | 0.8458 |
| 0.2193        | 15.0  | 1830 | 0.3188          | 0.8747   | 0.8471    | 0.8538 | 0.8503 |
| 0.2061        | 16.0  | 1952 | 0.3392          | 0.8521   | 0.8186    | 0.8404 | 0.8278 |
| 0.21          | 17.0  | 2074 | 0.3122          | 0.8797   | 0.8560    | 0.8524 | 0.8541 |
| 0.2112        | 18.0  | 2196 | 0.3332          | 0.8546   | 0.8216    | 0.8422 | 0.8303 |
| 0.2002        | 19.0  | 2318 | 0.3121          | 0.8772   | 0.8524    | 0.8506 | 0.8515 |
| 0.2041        | 20.0  | 2440 | 0.3129          | 0.8747   | 0.8479    | 0.8513 | 0.8496 |


### Framework versions

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