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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-pt-pl50-2
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-pt-pl50-2
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: 0.3235
- Accuracy: 0.8747
- Precision: 0.8537
- Recall: 0.8388
- F1: 0.8457
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5384 | 1.0 | 122 | 0.4919 | 0.7368 | 0.6770 | 0.6538 | 0.6617 |
| 0.4212 | 2.0 | 244 | 0.4175 | 0.8246 | 0.7930 | 0.8359 | 0.8048 |
| 0.3413 | 3.0 | 366 | 0.3403 | 0.8446 | 0.8257 | 0.7851 | 0.8009 |
| 0.2888 | 4.0 | 488 | 0.3278 | 0.8471 | 0.8159 | 0.8143 | 0.8151 |
| 0.2577 | 5.0 | 610 | 0.3103 | 0.8596 | 0.8325 | 0.8257 | 0.8290 |
| 0.2495 | 6.0 | 732 | 0.3074 | 0.8672 | 0.8436 | 0.8310 | 0.8369 |
| 0.2391 | 7.0 | 854 | 0.3005 | 0.8672 | 0.8402 | 0.8385 | 0.8394 |
| 0.2177 | 8.0 | 976 | 0.2979 | 0.8697 | 0.8449 | 0.8378 | 0.8412 |
| 0.2102 | 9.0 | 1098 | 0.2961 | 0.8797 | 0.8549 | 0.8549 | 0.8549 |
| 0.2029 | 10.0 | 1220 | 0.3043 | 0.8697 | 0.8579 | 0.8178 | 0.8340 |
| 0.1829 | 11.0 | 1342 | 0.3059 | 0.8797 | 0.8572 | 0.8499 | 0.8534 |
| 0.184 | 12.0 | 1464 | 0.3002 | 0.8772 | 0.8609 | 0.8356 | 0.8467 |
| 0.1802 | 13.0 | 1586 | 0.2954 | 0.8897 | 0.8710 | 0.8595 | 0.8649 |
| 0.1684 | 14.0 | 1708 | 0.3008 | 0.8872 | 0.8634 | 0.8652 | 0.8643 |
| 0.1627 | 15.0 | 1830 | 0.3067 | 0.8872 | 0.8672 | 0.8577 | 0.8622 |
| 0.1581 | 16.0 | 1952 | 0.3107 | 0.8772 | 0.8514 | 0.8531 | 0.8522 |
| 0.1468 | 17.0 | 2074 | 0.3229 | 0.8772 | 0.8576 | 0.8406 | 0.8484 |
| 0.1433 | 18.0 | 2196 | 0.3247 | 0.8747 | 0.8537 | 0.8388 | 0.8457 |
| 0.1538 | 19.0 | 2318 | 0.3246 | 0.8747 | 0.8537 | 0.8388 | 0.8457 |
| 0.1412 | 20.0 | 2440 | 0.3235 | 0.8747 | 0.8537 | 0.8388 | 0.8457 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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