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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-pl5-1 |
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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-pl5-1 |
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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: 0.2868 |
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- Accuracy: 0.8847 |
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- Precision: 0.8599 |
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- Recall: 0.8634 |
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- F1: 0.8616 |
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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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| 0.5526 | 1.0 | 122 | 0.5135 | 0.7118 | 0.6438 | 0.6286 | 0.6339 | |
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| 0.4696 | 2.0 | 244 | 0.4522 | 0.7569 | 0.7328 | 0.7755 | 0.7369 | |
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| 0.3867 | 3.0 | 366 | 0.3466 | 0.8371 | 0.8297 | 0.7597 | 0.7824 | |
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| 0.3349 | 4.0 | 488 | 0.3128 | 0.8546 | 0.8395 | 0.7971 | 0.8137 | |
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| 0.2998 | 5.0 | 610 | 0.2932 | 0.8596 | 0.8293 | 0.8357 | 0.8324 | |
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| 0.2787 | 6.0 | 732 | 0.2855 | 0.8697 | 0.8419 | 0.8453 | 0.8436 | |
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| 0.2551 | 7.0 | 854 | 0.2898 | 0.8747 | 0.8438 | 0.8713 | 0.8550 | |
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| 0.2496 | 8.0 | 976 | 0.2936 | 0.8697 | 0.8653 | 0.8103 | 0.8309 | |
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| 0.2347 | 9.0 | 1098 | 0.2755 | 0.8847 | 0.8599 | 0.8634 | 0.8616 | |
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| 0.2199 | 10.0 | 1220 | 0.3038 | 0.8722 | 0.8675 | 0.8146 | 0.8347 | |
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| 0.2089 | 11.0 | 1342 | 0.2695 | 0.8822 | 0.8574 | 0.8592 | 0.8583 | |
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| 0.1992 | 12.0 | 1464 | 0.2710 | 0.8747 | 0.8488 | 0.8488 | 0.8488 | |
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| 0.1841 | 13.0 | 1586 | 0.2807 | 0.8722 | 0.8512 | 0.8346 | 0.8422 | |
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| 0.1808 | 14.0 | 1708 | 0.2822 | 0.8822 | 0.8548 | 0.8667 | 0.8603 | |
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| 0.1677 | 15.0 | 1830 | 0.2841 | 0.8747 | 0.8479 | 0.8513 | 0.8496 | |
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| 0.1683 | 16.0 | 1952 | 0.2821 | 0.8772 | 0.8496 | 0.8581 | 0.8537 | |
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| 0.1748 | 17.0 | 2074 | 0.2824 | 0.8797 | 0.8572 | 0.8499 | 0.8534 | |
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| 0.1566 | 18.0 | 2196 | 0.2847 | 0.8872 | 0.8606 | 0.8727 | 0.8662 | |
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| 0.1522 | 19.0 | 2318 | 0.2880 | 0.8822 | 0.8574 | 0.8592 | 0.8583 | |
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| 0.1566 | 20.0 | 2440 | 0.2868 | 0.8847 | 0.8599 | 0.8634 | 0.8616 | |
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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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