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--- |
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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-pl10-2 |
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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-pl10-2 |
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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: 0.3132 |
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- Accuracy: 0.8947 |
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- Precision: 0.8743 |
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- Recall: 0.8705 |
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- F1: 0.8724 |
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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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| 0.5494 | 1.0 | 122 | 0.5033 | 0.7318 | 0.6683 | 0.6278 | 0.6369 | |
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| 0.4551 | 2.0 | 244 | 0.4276 | 0.7769 | 0.7434 | 0.7797 | 0.7522 | |
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| 0.3731 | 3.0 | 366 | 0.3508 | 0.8396 | 0.8294 | 0.7665 | 0.7879 | |
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| 0.3032 | 4.0 | 488 | 0.3237 | 0.8672 | 0.8353 | 0.8635 | 0.8466 | |
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| 0.2718 | 5.0 | 610 | 0.3102 | 0.8722 | 0.8445 | 0.8496 | 0.8470 | |
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| 0.2642 | 6.0 | 732 | 0.3006 | 0.8747 | 0.8451 | 0.8613 | 0.8524 | |
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| 0.2394 | 7.0 | 854 | 0.3013 | 0.8722 | 0.8544 | 0.8296 | 0.8404 | |
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| 0.2234 | 8.0 | 976 | 0.2904 | 0.8797 | 0.8572 | 0.8499 | 0.8534 | |
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| 0.2098 | 9.0 | 1098 | 0.2984 | 0.8897 | 0.8625 | 0.8795 | 0.8701 | |
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| 0.2029 | 10.0 | 1220 | 0.3189 | 0.8822 | 0.8762 | 0.8317 | 0.8495 | |
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| 0.1917 | 11.0 | 1342 | 0.2848 | 0.8847 | 0.8648 | 0.8534 | 0.8588 | |
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| 0.1797 | 12.0 | 1464 | 0.3003 | 0.8772 | 0.8535 | 0.8481 | 0.8507 | |
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| 0.1658 | 13.0 | 1586 | 0.3010 | 0.8847 | 0.8634 | 0.8559 | 0.8595 | |
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| 0.1551 | 14.0 | 1708 | 0.3077 | 0.8847 | 0.8589 | 0.8659 | 0.8623 | |
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| 0.1517 | 15.0 | 1830 | 0.3014 | 0.8947 | 0.8789 | 0.8630 | 0.8704 | |
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| 0.1532 | 16.0 | 1952 | 0.3067 | 0.8947 | 0.8718 | 0.8755 | 0.8737 | |
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| 0.136 | 17.0 | 2074 | 0.3174 | 0.8897 | 0.8670 | 0.8670 | 0.8670 | |
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| 0.1438 | 18.0 | 2196 | 0.3129 | 0.8897 | 0.8682 | 0.8645 | 0.8663 | |
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| 0.1507 | 19.0 | 2318 | 0.3165 | 0.8922 | 0.8734 | 0.8637 | 0.8683 | |
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| 0.1326 | 20.0 | 2440 | 0.3132 | 0.8947 | 0.8743 | 0.8705 | 0.8724 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.15.2 |
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