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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-0 |
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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-0 |
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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: 0.2808 |
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- Accuracy: 0.8797 |
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- Precision: 0.8504 |
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- Recall: 0.8699 |
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- F1: 0.8590 |
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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: 1 |
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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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| 0.5437 | 1.0 | 122 | 0.4910 | 0.7368 | 0.7052 | 0.5813 | 0.5755 | |
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| 0.4527 | 2.0 | 244 | 0.3825 | 0.8321 | 0.7972 | 0.7987 | 0.7979 | |
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| 0.3443 | 3.0 | 366 | 0.3294 | 0.8622 | 0.8373 | 0.8250 | 0.8307 | |
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| 0.3026 | 4.0 | 488 | 0.3161 | 0.8722 | 0.8474 | 0.8421 | 0.8446 | |
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| 0.2848 | 5.0 | 610 | 0.3151 | 0.8647 | 0.8336 | 0.8492 | 0.8406 | |
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| 0.2605 | 6.0 | 732 | 0.2879 | 0.8772 | 0.8524 | 0.8506 | 0.8515 | |
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| 0.2292 | 7.0 | 854 | 0.2645 | 0.8797 | 0.8560 | 0.8524 | 0.8541 | |
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| 0.2196 | 8.0 | 976 | 0.2868 | 0.8772 | 0.8470 | 0.8706 | 0.8570 | |
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| 0.2076 | 9.0 | 1098 | 0.2795 | 0.8797 | 0.8496 | 0.8749 | 0.8602 | |
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| 0.201 | 10.0 | 1220 | 0.3010 | 0.8772 | 0.8465 | 0.8756 | 0.8582 | |
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| 0.1919 | 11.0 | 1342 | 0.2928 | 0.8747 | 0.8436 | 0.8738 | 0.8556 | |
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| 0.1866 | 12.0 | 1464 | 0.2660 | 0.8922 | 0.8644 | 0.8863 | 0.8739 | |
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| 0.1721 | 13.0 | 1586 | 0.2594 | 0.8922 | 0.8657 | 0.8813 | 0.8728 | |
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| 0.1734 | 14.0 | 1708 | 0.2487 | 0.8847 | 0.8581 | 0.8684 | 0.8629 | |
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| 0.1601 | 15.0 | 1830 | 0.2958 | 0.8847 | 0.8550 | 0.8834 | 0.8666 | |
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| 0.1586 | 16.0 | 1952 | 0.2719 | 0.8822 | 0.8548 | 0.8667 | 0.8603 | |
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| 0.1486 | 17.0 | 2074 | 0.2737 | 0.8772 | 0.8489 | 0.8606 | 0.8544 | |
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| 0.1474 | 18.0 | 2196 | 0.2678 | 0.8872 | 0.8634 | 0.8652 | 0.8643 | |
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| 0.1511 | 19.0 | 2318 | 0.2719 | 0.8797 | 0.8509 | 0.8674 | 0.8583 | |
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| 0.1296 | 20.0 | 2440 | 0.2808 | 0.8797 | 0.8504 | 0.8699 | 0.8590 | |
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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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