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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-3 |
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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-3 |
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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: 0.8030 |
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- Accuracy: 0.9023 |
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- Precision: 0.8875 |
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- Recall: 0.8733 |
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- F1: 0.8799 |
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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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| 0.4092 | 1.0 | 122 | 0.3457 | 0.8521 | 0.8930 | 0.7554 | 0.7892 | |
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| 0.2282 | 2.0 | 244 | 0.2584 | 0.8922 | 0.8749 | 0.8612 | 0.8676 | |
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| 0.138 | 3.0 | 366 | 0.4417 | 0.8797 | 0.8825 | 0.8199 | 0.8430 | |
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| 0.0837 | 4.0 | 488 | 0.4037 | 0.9023 | 0.8893 | 0.8708 | 0.8793 | |
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| 0.0426 | 5.0 | 610 | 0.5462 | 0.9048 | 0.8806 | 0.8951 | 0.8873 | |
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| 0.0502 | 6.0 | 732 | 0.5626 | 0.8897 | 0.8618 | 0.8820 | 0.8707 | |
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| 0.0242 | 7.0 | 854 | 0.6241 | 0.9073 | 0.8977 | 0.8744 | 0.8849 | |
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| 0.0217 | 8.0 | 976 | 0.7096 | 0.8872 | 0.8579 | 0.8852 | 0.8692 | |
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| 0.0229 | 9.0 | 1098 | 0.6115 | 0.9123 | 0.8910 | 0.9004 | 0.8955 | |
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| 0.0109 | 10.0 | 1220 | 0.7575 | 0.8972 | 0.8796 | 0.8698 | 0.8745 | |
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| 0.0068 | 11.0 | 1342 | 0.7537 | 0.9073 | 0.8938 | 0.8794 | 0.8861 | |
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| 0.0131 | 12.0 | 1464 | 0.7247 | 0.8972 | 0.8732 | 0.8823 | 0.8776 | |
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| 0.0101 | 13.0 | 1586 | 0.7928 | 0.8972 | 0.8754 | 0.8773 | 0.8764 | |
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| 0.0061 | 14.0 | 1708 | 0.7849 | 0.9073 | 0.8875 | 0.8894 | 0.8884 | |
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| 0.0135 | 15.0 | 1830 | 0.7816 | 0.8972 | 0.8830 | 0.8648 | 0.8731 | |
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| 0.0081 | 16.0 | 1952 | 0.7727 | 0.8972 | 0.8767 | 0.8748 | 0.8757 | |
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| 0.0027 | 17.0 | 2074 | 0.8128 | 0.8972 | 0.8754 | 0.8773 | 0.8764 | |
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| 0.0041 | 18.0 | 2196 | 0.8081 | 0.9023 | 0.8828 | 0.8808 | 0.8818 | |
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| 0.0018 | 19.0 | 2318 | 0.8039 | 0.9023 | 0.8893 | 0.8708 | 0.8793 | |
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| 0.0025 | 20.0 | 2440 | 0.8030 | 0.9023 | 0.8875 | 0.8733 | 0.8799 | |
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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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