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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: 0.7891 |
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- Accuracy: 0.8972 |
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- Precision: 0.8796 |
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- Recall: 0.8698 |
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- F1: 0.8745 |
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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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| 0.411 | 1.0 | 122 | 0.2751 | 0.8722 | 0.8474 | 0.8421 | 0.8446 | |
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| 0.2264 | 2.0 | 244 | 0.3037 | 0.8872 | 0.8574 | 0.8977 | 0.8719 | |
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| 0.1467 | 3.0 | 366 | 0.3442 | 0.8772 | 0.8465 | 0.8756 | 0.8582 | |
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| 0.0961 | 4.0 | 488 | 0.3737 | 0.8997 | 0.8819 | 0.8741 | 0.8778 | |
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| 0.0726 | 5.0 | 610 | 0.4306 | 0.8997 | 0.8835 | 0.8716 | 0.8772 | |
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| 0.0514 | 6.0 | 732 | 0.6449 | 0.8847 | 0.8546 | 0.8884 | 0.8677 | |
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| 0.0532 | 7.0 | 854 | 0.5595 | 0.8972 | 0.8754 | 0.8773 | 0.8764 | |
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| 0.0274 | 8.0 | 976 | 0.6728 | 0.8872 | 0.8687 | 0.8552 | 0.8615 | |
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| 0.0186 | 9.0 | 1098 | 0.6218 | 0.9073 | 0.8977 | 0.8744 | 0.8849 | |
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| 0.0121 | 10.0 | 1220 | 0.6576 | 0.8922 | 0.8766 | 0.8587 | 0.8669 | |
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| 0.0244 | 11.0 | 1342 | 0.7507 | 0.8972 | 0.8940 | 0.8523 | 0.8695 | |
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| 0.0062 | 12.0 | 1464 | 0.6859 | 0.8972 | 0.8849 | 0.8623 | 0.8724 | |
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| 0.0099 | 13.0 | 1586 | 0.6514 | 0.9073 | 0.8904 | 0.8844 | 0.8873 | |
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| 0.0087 | 14.0 | 1708 | 0.7604 | 0.8997 | 0.8852 | 0.8691 | 0.8765 | |
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| 0.0056 | 15.0 | 1830 | 0.7282 | 0.9023 | 0.8875 | 0.8733 | 0.8799 | |
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| 0.0063 | 16.0 | 1952 | 0.6987 | 0.9123 | 0.8965 | 0.8904 | 0.8934 | |
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| 0.0071 | 17.0 | 2074 | 0.7402 | 0.9048 | 0.8897 | 0.8776 | 0.8833 | |
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| 0.0023 | 18.0 | 2196 | 0.7846 | 0.8922 | 0.8719 | 0.8662 | 0.8690 | |
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| 0.0043 | 19.0 | 2318 | 0.7948 | 0.8922 | 0.8719 | 0.8662 | 0.8690 | |
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| 0.0021 | 20.0 | 2440 | 0.7891 | 0.8972 | 0.8796 | 0.8698 | 0.8745 | |
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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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