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---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-pt-pl30-4
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-pl30-4
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: 0.3072
- Accuracy: 0.8822
- Precision: 0.8574
- Recall: 0.8592
- F1: 0.8583
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5438 | 1.0 | 122 | 0.4988 | 0.7218 | 0.6601 | 0.6507 | 0.6546 |
| 0.4428 | 2.0 | 244 | 0.3788 | 0.8446 | 0.8107 | 0.8226 | 0.8161 |
| 0.3441 | 3.0 | 366 | 0.3289 | 0.8596 | 0.8510 | 0.7982 | 0.8179 |
| 0.2986 | 4.0 | 488 | 0.2884 | 0.8797 | 0.8572 | 0.8499 | 0.8534 |
| 0.2667 | 5.0 | 610 | 0.2698 | 0.8772 | 0.8535 | 0.8481 | 0.8507 |
| 0.2524 | 6.0 | 732 | 0.2723 | 0.8847 | 0.8609 | 0.8609 | 0.8609 |
| 0.2343 | 7.0 | 854 | 0.3180 | 0.8647 | 0.8533 | 0.8092 | 0.8266 |
| 0.2212 | 8.0 | 976 | 0.2949 | 0.8822 | 0.8674 | 0.8417 | 0.8529 |
| 0.2142 | 9.0 | 1098 | 0.2828 | 0.8847 | 0.8697 | 0.8459 | 0.8565 |
| 0.1958 | 10.0 | 1220 | 0.2887 | 0.8697 | 0.8399 | 0.8528 | 0.8458 |
| 0.1855 | 11.0 | 1342 | 0.2868 | 0.8822 | 0.8548 | 0.8667 | 0.8603 |
| 0.1742 | 12.0 | 1464 | 0.2981 | 0.8747 | 0.8552 | 0.8363 | 0.8448 |
| 0.1601 | 13.0 | 1586 | 0.2930 | 0.8797 | 0.8539 | 0.8574 | 0.8556 |
| 0.1602 | 14.0 | 1708 | 0.2979 | 0.8797 | 0.8504 | 0.8699 | 0.8590 |
| 0.1497 | 15.0 | 1830 | 0.2969 | 0.8872 | 0.8606 | 0.8727 | 0.8662 |
| 0.1447 | 16.0 | 1952 | 0.2963 | 0.8847 | 0.8599 | 0.8634 | 0.8616 |
| 0.1394 | 17.0 | 2074 | 0.3018 | 0.8822 | 0.8564 | 0.8617 | 0.8590 |
| 0.1333 | 18.0 | 2196 | 0.3065 | 0.8822 | 0.8574 | 0.8592 | 0.8583 |
| 0.1406 | 19.0 | 2318 | 0.3062 | 0.8822 | 0.8574 | 0.8592 | 0.8583 |
| 0.1243 | 20.0 | 2440 | 0.3072 | 0.8822 | 0.8574 | 0.8592 | 0.8583 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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