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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-pl30-0 |
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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-pl30-0 |
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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: 0.2793 |
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- Accuracy: 0.8922 |
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- Precision: 0.8665 |
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- Recall: 0.8788 |
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- F1: 0.8722 |
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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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| 0.5424 | 1.0 | 122 | 0.4762 | 0.7419 | 0.6837 | 0.6473 | 0.6575 | |
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| 0.4345 | 2.0 | 244 | 0.4157 | 0.7895 | 0.7581 | 0.7986 | 0.7674 | |
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| 0.3391 | 3.0 | 366 | 0.3388 | 0.8546 | 0.8324 | 0.8071 | 0.8180 | |
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| 0.2837 | 4.0 | 488 | 0.3279 | 0.8622 | 0.8342 | 0.8325 | 0.8333 | |
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| 0.2761 | 5.0 | 610 | 0.3132 | 0.8647 | 0.8346 | 0.8442 | 0.8391 | |
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| 0.2459 | 6.0 | 732 | 0.3033 | 0.8747 | 0.8440 | 0.8688 | 0.8544 | |
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| 0.2321 | 7.0 | 854 | 0.2871 | 0.8822 | 0.8530 | 0.8742 | 0.8622 | |
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| 0.2206 | 8.0 | 976 | 0.2634 | 0.8822 | 0.8610 | 0.8517 | 0.8561 | |
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| 0.2067 | 9.0 | 1098 | 0.2634 | 0.8922 | 0.8694 | 0.8712 | 0.8703 | |
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| 0.192 | 10.0 | 1220 | 0.2696 | 0.8922 | 0.8873 | 0.8462 | 0.8631 | |
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| 0.1866 | 11.0 | 1342 | 0.2752 | 0.8972 | 0.8691 | 0.8973 | 0.8808 | |
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| 0.1786 | 12.0 | 1464 | 0.2652 | 0.8972 | 0.8708 | 0.8898 | 0.8793 | |
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| 0.1695 | 13.0 | 1586 | 0.2536 | 0.9073 | 0.8920 | 0.8819 | 0.8867 | |
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| 0.1664 | 14.0 | 1708 | 0.2737 | 0.8872 | 0.8587 | 0.8802 | 0.8681 | |
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| 0.1521 | 15.0 | 1830 | 0.2620 | 0.9023 | 0.8802 | 0.8858 | 0.8830 | |
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| 0.1494 | 16.0 | 1952 | 0.3030 | 0.8922 | 0.8630 | 0.8963 | 0.8761 | |
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| 0.1487 | 17.0 | 2074 | 0.2702 | 0.8922 | 0.8650 | 0.8838 | 0.8734 | |
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| 0.1494 | 18.0 | 2196 | 0.2763 | 0.8947 | 0.8676 | 0.8880 | 0.8766 | |
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| 0.1334 | 19.0 | 2318 | 0.2826 | 0.8922 | 0.8650 | 0.8838 | 0.8734 | |
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| 0.1325 | 20.0 | 2440 | 0.2793 | 0.8922 | 0.8665 | 0.8788 | 0.8722 | |
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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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