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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: 0.8105 |
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- Accuracy: 0.9023 |
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- Precision: 0.8828 |
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- Recall: 0.8808 |
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- F1: 0.8818 |
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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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| 0.4267 | 1.0 | 122 | 0.3586 | 0.8797 | 0.8892 | 0.8149 | 0.8409 | |
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| 0.2234 | 2.0 | 244 | 0.3668 | 0.8697 | 0.8395 | 0.8853 | 0.8539 | |
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| 0.126 | 3.0 | 366 | 0.4554 | 0.8922 | 0.8632 | 0.8938 | 0.8756 | |
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| 0.0886 | 4.0 | 488 | 0.4441 | 0.9073 | 0.8957 | 0.8769 | 0.8855 | |
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| 0.0611 | 5.0 | 610 | 0.4923 | 0.9048 | 0.8881 | 0.8801 | 0.8839 | |
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| 0.0366 | 6.0 | 732 | 0.6796 | 0.8997 | 0.8748 | 0.8891 | 0.8814 | |
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| 0.0358 | 7.0 | 854 | 0.5746 | 0.9048 | 0.8935 | 0.8726 | 0.8821 | |
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| 0.0272 | 8.0 | 976 | 0.5953 | 0.8947 | 0.8718 | 0.8755 | 0.8737 | |
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| 0.0231 | 9.0 | 1098 | 0.6506 | 0.8997 | 0.8891 | 0.8641 | 0.8752 | |
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| 0.0141 | 10.0 | 1220 | 0.6854 | 0.9023 | 0.8814 | 0.8833 | 0.8824 | |
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| 0.023 | 11.0 | 1342 | 0.7218 | 0.9023 | 0.8814 | 0.8833 | 0.8824 | |
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| 0.0067 | 12.0 | 1464 | 0.7695 | 0.9023 | 0.8814 | 0.8833 | 0.8824 | |
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| 0.0064 | 13.0 | 1586 | 0.9004 | 0.8797 | 0.8496 | 0.8749 | 0.8602 | |
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| 0.0103 | 14.0 | 1708 | 0.7978 | 0.9023 | 0.8792 | 0.8883 | 0.8835 | |
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| 0.0072 | 15.0 | 1830 | 0.8251 | 0.8997 | 0.8791 | 0.8791 | 0.8791 | |
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| 0.0054 | 16.0 | 1952 | 0.7715 | 0.9023 | 0.8814 | 0.8833 | 0.8824 | |
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| 0.0038 | 17.0 | 2074 | 0.7821 | 0.9073 | 0.8920 | 0.8819 | 0.8867 | |
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| 0.0021 | 18.0 | 2196 | 0.8211 | 0.8972 | 0.8754 | 0.8773 | 0.8764 | |
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| 0.0022 | 19.0 | 2318 | 0.8162 | 0.8997 | 0.8791 | 0.8791 | 0.8791 | |
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| 0.0027 | 20.0 | 2440 | 0.8105 | 0.9023 | 0.8828 | 0.8808 | 0.8818 | |
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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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