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--- |
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language: |
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- id |
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license: mit |
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base_model: indolem/indobert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: sentiment-pt-pl5-3 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# sentiment-pt-pl5-3 |
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This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co./indolem/indobert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2683 |
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- Accuracy: 0.9023 |
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- Precision: 0.8828 |
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- Recall: 0.8808 |
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- F1: 0.8818 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 30 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.5455 | 1.0 | 122 | 0.4877 | 0.7544 | 0.7053 | 0.6512 | 0.6639 | |
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| 0.4356 | 2.0 | 244 | 0.3537 | 0.8446 | 0.8103 | 0.8376 | 0.8210 | |
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| 0.3468 | 3.0 | 366 | 0.3416 | 0.8496 | 0.8326 | 0.7911 | 0.8073 | |
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| 0.3049 | 4.0 | 488 | 0.3126 | 0.8546 | 0.8324 | 0.8071 | 0.8180 | |
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| 0.2673 | 5.0 | 610 | 0.2919 | 0.8596 | 0.8299 | 0.8332 | 0.8315 | |
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| 0.2516 | 6.0 | 732 | 0.2823 | 0.8647 | 0.8387 | 0.8317 | 0.8351 | |
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| 0.2243 | 7.0 | 854 | 0.2688 | 0.8822 | 0.8530 | 0.8742 | 0.8622 | |
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| 0.2157 | 8.0 | 976 | 0.2641 | 0.8947 | 0.8807 | 0.8605 | 0.8697 | |
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| 0.2052 | 9.0 | 1098 | 0.2627 | 0.8847 | 0.8679 | 0.8484 | 0.8573 | |
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| 0.1864 | 10.0 | 1220 | 0.2881 | 0.8847 | 0.8737 | 0.8409 | 0.8548 | |
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| 0.1928 | 11.0 | 1342 | 0.2785 | 0.8872 | 0.8593 | 0.8777 | 0.8675 | |
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| 0.1804 | 12.0 | 1464 | 0.2506 | 0.8997 | 0.8871 | 0.8666 | 0.8759 | |
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| 0.1654 | 13.0 | 1586 | 0.2664 | 0.8997 | 0.8791 | 0.8791 | 0.8791 | |
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| 0.1567 | 14.0 | 1708 | 0.2661 | 0.9048 | 0.8798 | 0.8976 | 0.8878 | |
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| 0.1438 | 15.0 | 1830 | 0.2615 | 0.9098 | 0.8898 | 0.8937 | 0.8917 | |
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| 0.1472 | 16.0 | 1952 | 0.2555 | 0.9048 | 0.8838 | 0.8876 | 0.8857 | |
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| 0.1394 | 17.0 | 2074 | 0.2648 | 0.8997 | 0.8791 | 0.8791 | 0.8791 | |
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| 0.1387 | 18.0 | 2196 | 0.2630 | 0.9048 | 0.8826 | 0.8901 | 0.8862 | |
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| 0.1378 | 19.0 | 2318 | 0.2689 | 0.9048 | 0.8865 | 0.8826 | 0.8845 | |
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| 0.1365 | 20.0 | 2440 | 0.2683 | 0.9023 | 0.8828 | 0.8808 | 0.8818 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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