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---
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
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