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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-base-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-base-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: 0.7536 |
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- Accuracy: 0.9048 |
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- Precision: 0.8798 |
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- Recall: 0.8976 |
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- F1: 0.8878 |
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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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| 0.4355 | 1.0 | 122 | 0.3243 | 0.8697 | 0.8538 | 0.8228 | 0.8359 | |
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| 0.2295 | 2.0 | 244 | 0.3047 | 0.8897 | 0.8625 | 0.8795 | 0.8701 | |
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| 0.1337 | 3.0 | 366 | 0.3747 | 0.8997 | 0.8778 | 0.8816 | 0.8797 | |
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| 0.1038 | 4.0 | 488 | 0.4188 | 0.8822 | 0.8518 | 0.8867 | 0.8651 | |
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| 0.072 | 5.0 | 610 | 0.6271 | 0.8872 | 0.8672 | 0.8577 | 0.8622 | |
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| 0.0462 | 6.0 | 732 | 0.6129 | 0.8897 | 0.8632 | 0.8770 | 0.8695 | |
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| 0.0459 | 7.0 | 854 | 0.5891 | 0.8897 | 0.8710 | 0.8595 | 0.8649 | |
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| 0.0391 | 8.0 | 976 | 0.5973 | 0.8872 | 0.8587 | 0.8802 | 0.8681 | |
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| 0.0307 | 9.0 | 1098 | 0.7087 | 0.8747 | 0.8441 | 0.8863 | 0.8585 | |
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| 0.0199 | 10.0 | 1220 | 0.7264 | 0.8972 | 0.8869 | 0.8598 | 0.8717 | |
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| 0.0105 | 11.0 | 1342 | 0.6738 | 0.8972 | 0.8767 | 0.8748 | 0.8757 | |
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| 0.0131 | 12.0 | 1464 | 0.7488 | 0.8997 | 0.8733 | 0.8941 | 0.8825 | |
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| 0.0102 | 13.0 | 1586 | 0.7155 | 0.8972 | 0.8708 | 0.8898 | 0.8793 | |
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| 0.0061 | 14.0 | 1708 | 0.7196 | 0.9073 | 0.8851 | 0.8944 | 0.8895 | |
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| 0.0138 | 15.0 | 1830 | 0.7618 | 0.9023 | 0.8773 | 0.8933 | 0.8846 | |
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| 0.0075 | 16.0 | 1952 | 0.7253 | 0.9048 | 0.8806 | 0.8951 | 0.8873 | |
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| 0.0063 | 17.0 | 2074 | 0.7560 | 0.9023 | 0.8782 | 0.8908 | 0.8841 | |
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| 0.0066 | 18.0 | 2196 | 0.7483 | 0.9023 | 0.8758 | 0.8983 | 0.8857 | |
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| 0.0023 | 19.0 | 2318 | 0.7535 | 0.9023 | 0.8773 | 0.8933 | 0.8846 | |
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| 0.0021 | 20.0 | 2440 | 0.7536 | 0.9048 | 0.8798 | 0.8976 | 0.8878 | |
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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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