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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-1 |
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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-1 |
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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: 0.7908 |
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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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| 0.3889 | 1.0 | 122 | 0.4200 | 0.8045 | 0.8255 | 0.6867 | 0.7110 | |
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| 0.2335 | 2.0 | 244 | 0.3136 | 0.8922 | 0.8644 | 0.8863 | 0.8739 | |
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| 0.1411 | 3.0 | 366 | 0.3569 | 0.8972 | 0.8781 | 0.8723 | 0.8751 | |
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| 0.1078 | 4.0 | 488 | 0.3537 | 0.9148 | 0.8923 | 0.9072 | 0.8992 | |
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| 0.0822 | 5.0 | 610 | 0.5069 | 0.8797 | 0.8795 | 0.8224 | 0.8439 | |
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| 0.0529 | 6.0 | 732 | 0.4262 | 0.9073 | 0.8862 | 0.8919 | 0.8890 | |
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| 0.0365 | 7.0 | 854 | 0.5586 | 0.8972 | 0.8743 | 0.8798 | 0.8770 | |
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| 0.033 | 8.0 | 976 | 0.5012 | 0.8947 | 0.8870 | 0.8530 | 0.8675 | |
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| 0.0248 | 9.0 | 1098 | 0.5833 | 0.8922 | 0.8873 | 0.8462 | 0.8631 | |
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| 0.0123 | 10.0 | 1220 | 0.6611 | 0.9023 | 0.8858 | 0.8758 | 0.8806 | |
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| 0.0088 | 11.0 | 1342 | 0.6936 | 0.8947 | 0.8847 | 0.8555 | 0.8682 | |
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| 0.0074 | 12.0 | 1464 | 0.6790 | 0.9023 | 0.8858 | 0.8758 | 0.8806 | |
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| 0.0141 | 13.0 | 1586 | 0.6981 | 0.8972 | 0.8830 | 0.8648 | 0.8731 | |
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| 0.0034 | 14.0 | 1708 | 0.7145 | 0.8972 | 0.8781 | 0.8723 | 0.8751 | |
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| 0.0059 | 15.0 | 1830 | 0.7304 | 0.8997 | 0.8871 | 0.8666 | 0.8759 | |
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| 0.0056 | 16.0 | 1952 | 0.7518 | 0.8997 | 0.8778 | 0.8816 | 0.8797 | |
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| 0.0039 | 17.0 | 2074 | 0.7390 | 0.9023 | 0.8893 | 0.8708 | 0.8793 | |
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| 0.004 | 18.0 | 2196 | 0.7641 | 0.9023 | 0.8875 | 0.8733 | 0.8799 | |
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| 0.007 | 19.0 | 2318 | 0.7848 | 0.9023 | 0.8875 | 0.8733 | 0.8799 | |
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| 0.0042 | 20.0 | 2440 | 0.7908 | 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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