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update model card README.md

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@@ -17,12 +17,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8776
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- - Macro F1: 0.8019
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- - Precision: 0.8062
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- - Recall: 0.8128
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- - Kappa: 0.7158
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- - Accuracy: 0.8128
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall | Kappa | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 101 | 1.1216 | 0.6286 | 0.5851 | 0.7094 | 0.5110 | 0.7094 |
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- | No log | 2.0 | 203 | 0.9280 | 0.6797 | 0.6829 | 0.7438 | 0.5734 | 0.7438 |
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- | No log | 3.0 | 304 | 0.8106 | 0.7296 | 0.7227 | 0.7759 | 0.6335 | 0.7759 |
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- | No log | 4.0 | 406 | 0.7705 | 0.7814 | 0.7822 | 0.8030 | 0.6985 | 0.8030 |
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- | 0.9926 | 5.0 | 507 | 0.7413 | 0.7792 | 0.7719 | 0.8005 | 0.6899 | 0.8005 |
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- | 0.9926 | 6.0 | 609 | 0.7339 | 0.7930 | 0.7858 | 0.8128 | 0.7099 | 0.8128 |
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- | 0.9926 | 7.0 | 710 | 0.7714 | 0.7789 | 0.7768 | 0.7906 | 0.6863 | 0.7906 |
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- | 0.9926 | 8.0 | 812 | 0.7817 | 0.7938 | 0.7861 | 0.8128 | 0.7108 | 0.8128 |
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- | 0.9926 | 9.0 | 913 | 0.8029 | 0.7905 | 0.7885 | 0.8067 | 0.7046 | 0.8067 |
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- | 0.3377 | 10.0 | 1015 | 0.8392 | 0.7953 | 0.7922 | 0.8091 | 0.7090 | 0.8091 |
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- | 0.3377 | 11.0 | 1116 | 0.8399 | 0.7984 | 0.7981 | 0.8116 | 0.7139 | 0.8116 |
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- | 0.3377 | 12.0 | 1218 | 0.8519 | 0.8036 | 0.8099 | 0.8153 | 0.7184 | 0.8153 |
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- | 0.3377 | 13.0 | 1319 | 0.8683 | 0.8058 | 0.8167 | 0.8165 | 0.7206 | 0.8165 |
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- | 0.3377 | 14.0 | 1421 | 0.8745 | 0.8022 | 0.8068 | 0.8128 | 0.7160 | 0.8128 |
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- | 0.1504 | 14.93 | 1515 | 0.8776 | 0.8019 | 0.8062 | 0.8128 | 0.7158 | 0.8128 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0382
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+ - Macro F1: 0.7790
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+ - Precision: 0.7886
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+ - Recall: 0.7919
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+ - Kappa: 0.7004
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+ - Accuracy: 0.7919
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall | Kappa | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 101 | 1.2399 | 0.5520 | 0.5208 | 0.6687 | 0.4616 | 0.6687 |
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+ | No log | 2.0 | 203 | 1.0312 | 0.6595 | 0.6248 | 0.7291 | 0.5761 | 0.7291 |
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+ | No log | 3.0 | 304 | 0.9357 | 0.6954 | 0.7009 | 0.7488 | 0.6146 | 0.7488 |
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+ | No log | 4.0 | 406 | 0.8929 | 0.7333 | 0.7403 | 0.7722 | 0.6573 | 0.7722 |
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+ | 0.9705 | 5.0 | 507 | 0.8818 | 0.7479 | 0.7435 | 0.7746 | 0.6679 | 0.7746 |
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+ | 0.9705 | 6.0 | 609 | 0.8598 | 0.7634 | 0.7534 | 0.7833 | 0.6845 | 0.7833 |
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+ | 0.9705 | 7.0 | 710 | 0.9101 | 0.7592 | 0.7560 | 0.7734 | 0.6744 | 0.7734 |
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+ | 0.9705 | 8.0 | 812 | 0.9143 | 0.7759 | 0.7735 | 0.7931 | 0.6999 | 0.7931 |
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+ | 0.9705 | 9.0 | 913 | 0.9373 | 0.7771 | 0.7781 | 0.7956 | 0.7038 | 0.7956 |
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+ | 0.3052 | 10.0 | 1015 | 0.9829 | 0.7755 | 0.7837 | 0.7906 | 0.6982 | 0.7906 |
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+ | 0.3052 | 11.0 | 1116 | 0.9880 | 0.7716 | 0.7749 | 0.7845 | 0.6915 | 0.7845 |
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+ | 0.3052 | 12.0 | 1218 | 1.0277 | 0.7740 | 0.7784 | 0.7882 | 0.6950 | 0.7882 |
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+ | 0.3052 | 13.0 | 1319 | 1.0352 | 0.7779 | 0.7866 | 0.7906 | 0.6985 | 0.7906 |
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+ | 0.3052 | 14.0 | 1421 | 1.0407 | 0.7806 | 0.7901 | 0.7943 | 0.7030 | 0.7943 |
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+ | 0.1382 | 14.93 | 1515 | 1.0382 | 0.7790 | 0.7886 | 0.7919 | 0.7004 | 0.7919 |
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  ### Framework versions