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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-unipelt-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-unipelt-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: 0.2811 |
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- Accuracy: 0.9023 |
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- Precision: 0.8773 |
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- Recall: 0.8933 |
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- F1: 0.8846 |
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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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| 0.5459 | 1.0 | 122 | 0.4639 | 0.7469 | 0.6922 | 0.6459 | 0.6573 | |
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| 0.4335 | 2.0 | 244 | 0.4108 | 0.7845 | 0.7552 | 0.7975 | 0.7634 | |
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| 0.3375 | 3.0 | 366 | 0.3283 | 0.8596 | 0.8347 | 0.8207 | 0.8272 | |
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| 0.2801 | 4.0 | 488 | 0.3202 | 0.8596 | 0.8278 | 0.8432 | 0.8347 | |
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| 0.2572 | 5.0 | 610 | 0.3109 | 0.8747 | 0.8438 | 0.8713 | 0.8550 | |
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| 0.2339 | 6.0 | 732 | 0.3074 | 0.8672 | 0.8353 | 0.8660 | 0.8473 | |
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| 0.2249 | 7.0 | 854 | 0.2915 | 0.8672 | 0.8353 | 0.8660 | 0.8473 | |
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| 0.193 | 8.0 | 976 | 0.2540 | 0.8972 | 0.8781 | 0.8723 | 0.8751 | |
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| 0.1899 | 9.0 | 1098 | 0.2636 | 0.8822 | 0.8526 | 0.8767 | 0.8628 | |
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| 0.1801 | 10.0 | 1220 | 0.2371 | 0.9073 | 0.8840 | 0.8969 | 0.8900 | |
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| 0.157 | 11.0 | 1342 | 0.2567 | 0.8997 | 0.8733 | 0.8941 | 0.8825 | |
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| 0.1553 | 12.0 | 1464 | 0.2593 | 0.8972 | 0.8708 | 0.8898 | 0.8793 | |
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| 0.1381 | 13.0 | 1586 | 0.2490 | 0.9173 | 0.9010 | 0.8990 | 0.9000 | |
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| 0.1476 | 14.0 | 1708 | 0.2701 | 0.8997 | 0.8740 | 0.8916 | 0.8819 | |
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| 0.1447 | 15.0 | 1830 | 0.2611 | 0.9123 | 0.8899 | 0.9029 | 0.8960 | |
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| 0.1336 | 16.0 | 1952 | 0.3100 | 0.8997 | 0.8718 | 0.9016 | 0.8840 | |
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| 0.1192 | 17.0 | 2074 | 0.2935 | 0.8972 | 0.8696 | 0.8948 | 0.8803 | |
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| 0.1247 | 18.0 | 2196 | 0.2869 | 0.9023 | 0.8765 | 0.8958 | 0.8851 | |
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| 0.117 | 19.0 | 2318 | 0.2761 | 0.9023 | 0.8773 | 0.8933 | 0.8846 | |
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| 0.1092 | 20.0 | 2440 | 0.2811 | 0.9023 | 0.8773 | 0.8933 | 0.8846 | |
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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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