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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-pl10-4
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-pl10-4
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: 0.2951
- Accuracy: 0.8847
- Precision: 0.8609
- Recall: 0.8609
- F1: 0.8609
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5448 | 1.0 | 122 | 0.5047 | 0.7243 | 0.6629 | 0.6524 | 0.6568 |
| 0.4527 | 2.0 | 244 | 0.4320 | 0.7945 | 0.7667 | 0.8121 | 0.7752 |
| 0.3603 | 3.0 | 366 | 0.3370 | 0.8471 | 0.8393 | 0.7768 | 0.7985 |
| 0.3081 | 4.0 | 488 | 0.2995 | 0.8722 | 0.8453 | 0.8471 | 0.8462 |
| 0.2793 | 5.0 | 610 | 0.3008 | 0.8747 | 0.8537 | 0.8388 | 0.8457 |
| 0.2526 | 6.0 | 732 | 0.2987 | 0.8697 | 0.8449 | 0.8378 | 0.8412 |
| 0.2478 | 7.0 | 854 | 0.3030 | 0.8772 | 0.8609 | 0.8356 | 0.8467 |
| 0.2337 | 8.0 | 976 | 0.2974 | 0.8672 | 0.8463 | 0.8260 | 0.8351 |
| 0.217 | 9.0 | 1098 | 0.2774 | 0.8722 | 0.8562 | 0.8271 | 0.8395 |
| 0.1966 | 10.0 | 1220 | 0.2846 | 0.8697 | 0.8411 | 0.8478 | 0.8443 |
| 0.199 | 11.0 | 1342 | 0.2910 | 0.8822 | 0.8639 | 0.8467 | 0.8545 |
| 0.187 | 12.0 | 1464 | 0.2871 | 0.8772 | 0.8609 | 0.8356 | 0.8467 |
| 0.1812 | 13.0 | 1586 | 0.2813 | 0.8797 | 0.8585 | 0.8474 | 0.8526 |
| 0.1633 | 14.0 | 1708 | 0.2957 | 0.8822 | 0.8555 | 0.8642 | 0.8596 |
| 0.1607 | 15.0 | 1830 | 0.2875 | 0.8922 | 0.8706 | 0.8687 | 0.8697 |
| 0.1584 | 16.0 | 1952 | 0.2859 | 0.8822 | 0.8610 | 0.8517 | 0.8561 |
| 0.1535 | 17.0 | 2074 | 0.2924 | 0.8847 | 0.8609 | 0.8609 | 0.8609 |
| 0.1432 | 18.0 | 2196 | 0.2966 | 0.8847 | 0.8599 | 0.8634 | 0.8616 |
| 0.1466 | 19.0 | 2318 | 0.2947 | 0.8822 | 0.8596 | 0.8542 | 0.8568 |
| 0.1411 | 20.0 | 2440 | 0.2951 | 0.8847 | 0.8609 | 0.8609 | 0.8609 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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