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BERT_large_with_preprocessing_grid_search

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update model card README.md

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@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.0736
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- - Precision: 0.0187
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  - Recall: 0.125
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- - F1: 0.0326
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- - Accuracy: 0.1497
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  ## Model description
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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: 16
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  - eval_batch_size: 16
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  - seed: 42
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 2.1138 | 1.0 | 510 | 2.0795 | 0.0041 | 0.125 | 0.0080 | 0.0329 |
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- | 2.1114 | 2.0 | 1020 | 2.0853 | 0.0194 | 0.125 | 0.0336 | 0.1551 |
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- | 2.106 | 3.0 | 1530 | 2.0806 | 0.0345 | 0.125 | 0.0541 | 0.2759 |
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- | 2.1015 | 4.0 | 2040 | 2.0758 | 0.0284 | 0.125 | 0.0462 | 0.2268 |
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- | 2.0997 | 5.0 | 2550 | 2.0808 | 0.0041 | 0.125 | 0.0080 | 0.0329 |
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- | 2.0998 | 6.0 | 3060 | 2.0754 | 0.0284 | 0.125 | 0.0462 | 0.2268 |
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- | 2.099 | 7.0 | 3570 | 2.0737 | 0.0194 | 0.125 | 0.0336 | 0.1551 |
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- | 2.0945 | 8.0 | 4080 | 2.0812 | 0.0045 | 0.125 | 0.0086 | 0.0358 |
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- | 2.0986 | 9.0 | 4590 | 2.0731 | 0.0187 | 0.125 | 0.0326 | 0.1497 |
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- | 2.0958 | 10.0 | 5100 | 2.0736 | 0.0187 | 0.125 | 0.0326 | 0.1497 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.0732
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+ - Precision: 0.0194
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  - Recall: 0.125
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+ - F1: 0.0336
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+ - Accuracy: 0.1551
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 2.1235 | 1.0 | 510 | 2.0738 | 0.0284 | 0.125 | 0.0462 | 0.2268 |
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+ | 2.1058 | 2.0 | 1020 | 2.0805 | 0.0194 | 0.125 | 0.0336 | 0.1551 |
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+ | 2.1039 | 3.0 | 1530 | 2.0780 | 0.0345 | 0.125 | 0.0541 | 0.2759 |
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+ | 2.1045 | 4.0 | 2040 | 2.0734 | 0.0284 | 0.125 | 0.0462 | 0.2268 |
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+ | 2.0963 | 5.0 | 2550 | 2.0779 | 0.0041 | 0.125 | 0.0080 | 0.0329 |
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+ | 2.0975 | 6.0 | 3060 | 2.0750 | 0.0284 | 0.125 | 0.0462 | 0.2268 |
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+ | 2.0944 | 7.0 | 3570 | 2.0734 | 0.0194 | 0.125 | 0.0336 | 0.1551 |
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+ | 2.1004 | 8.0 | 4080 | 2.0820 | 0.0029 | 0.125 | 0.0056 | 0.0231 |
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+ | 2.0974 | 9.0 | 4590 | 2.0724 | 0.0187 | 0.125 | 0.0326 | 0.1497 |
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+ | 2.0936 | 10.0 | 5100 | 2.0732 | 0.0194 | 0.125 | 0.0336 | 0.1551 |
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  ### Framework versions