|
--- |
|
language: |
|
- id |
|
license: mit |
|
base_model: indolem/indobert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
model-index: |
|
- name: sentiment-pt-pl10-0 |
|
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-pt-pl10-0 |
|
|
|
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: 0.2798 |
|
- Accuracy: 0.8972 |
|
- Precision: 0.8723 |
|
- Recall: 0.8848 |
|
- F1: 0.8781 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| 0.5568 | 1.0 | 122 | 0.4822 | 0.7243 | 0.6557 | 0.6074 | 0.6144 | |
|
| 0.4661 | 2.0 | 244 | 0.4453 | 0.7544 | 0.7241 | 0.7612 | 0.7304 | |
|
| 0.3875 | 3.0 | 366 | 0.3447 | 0.8622 | 0.8488 | 0.8075 | 0.8239 | |
|
| 0.318 | 4.0 | 488 | 0.3442 | 0.8496 | 0.8158 | 0.8436 | 0.8267 | |
|
| 0.2855 | 5.0 | 610 | 0.3349 | 0.8496 | 0.8158 | 0.8411 | 0.8260 | |
|
| 0.2638 | 6.0 | 732 | 0.3548 | 0.8371 | 0.8052 | 0.8472 | 0.8177 | |
|
| 0.2397 | 7.0 | 854 | 0.3254 | 0.8647 | 0.8326 | 0.8592 | 0.8434 | |
|
| 0.2428 | 8.0 | 976 | 0.2799 | 0.8922 | 0.8804 | 0.8537 | 0.8655 | |
|
| 0.2229 | 9.0 | 1098 | 0.2903 | 0.8722 | 0.8431 | 0.8546 | 0.8484 | |
|
| 0.2144 | 10.0 | 1220 | 0.2583 | 0.8972 | 0.8743 | 0.8798 | 0.8770 | |
|
| 0.1967 | 11.0 | 1342 | 0.2743 | 0.8822 | 0.8530 | 0.8742 | 0.8622 | |
|
| 0.1855 | 12.0 | 1464 | 0.2913 | 0.8772 | 0.8473 | 0.8681 | 0.8563 | |
|
| 0.1761 | 13.0 | 1586 | 0.2660 | 0.9023 | 0.8913 | 0.8683 | 0.8786 | |
|
| 0.1733 | 14.0 | 1708 | 0.2868 | 0.8822 | 0.8530 | 0.8742 | 0.8622 | |
|
| 0.1582 | 15.0 | 1830 | 0.2801 | 0.8847 | 0.8561 | 0.8759 | 0.8648 | |
|
| 0.1537 | 16.0 | 1952 | 0.3073 | 0.8747 | 0.8438 | 0.8713 | 0.8550 | |
|
| 0.1537 | 17.0 | 2074 | 0.2702 | 0.8972 | 0.8723 | 0.8848 | 0.8781 | |
|
| 0.1461 | 18.0 | 2196 | 0.2923 | 0.8947 | 0.8682 | 0.8855 | 0.8760 | |
|
| 0.1449 | 19.0 | 2318 | 0.2791 | 0.8947 | 0.8690 | 0.8830 | 0.8755 | |
|
| 0.1502 | 20.0 | 2440 | 0.2798 | 0.8972 | 0.8723 | 0.8848 | 0.8781 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.3.0+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.15.2 |
|
|
