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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: 1.0032
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- - Macro F1: 0.7793
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- - Precision: 0.7728
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- - Recall: 0.7931
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- - Kappa: 0.6958
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- - Accuracy: 0.7931
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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 | 1.2101 | 0.5750 | 0.5001 | 0.6773 | 0.4642 | 0.6773 |
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- | No log | 2.0 | 203 | 1.1016 | 0.6347 | 0.6042 | 0.6958 | 0.5220 | 0.6958 |
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- | No log | 3.0 | 304 | 0.9035 | 0.7243 | 0.7468 | 0.7611 | 0.6371 | 0.7611 |
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- | No log | 4.0 | 406 | 0.8619 | 0.7558 | 0.7592 | 0.7734 | 0.6694 | 0.7734 |
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- | 0.9594 | 5.0 | 507 | 0.8535 | 0.7635 | 0.7580 | 0.7869 | 0.6811 | 0.7869 |
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- | 0.9594 | 6.0 | 609 | 0.8652 | 0.7656 | 0.7608 | 0.7857 | 0.6812 | 0.7857 |
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- | 0.9594 | 7.0 | 710 | 0.8698 | 0.7718 | 0.7646 | 0.7894 | 0.6894 | 0.7894 |
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- | 0.9594 | 8.0 | 812 | 0.8729 | 0.7807 | 0.7748 | 0.7919 | 0.6964 | 0.7919 |
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- | 0.9594 | 9.0 | 913 | 0.9407 | 0.7722 | 0.7691 | 0.7906 | 0.6875 | 0.7906 |
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- | 0.3127 | 10.0 | 1015 | 0.9625 | 0.7794 | 0.7764 | 0.7943 | 0.6967 | 0.7943 |
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- | 0.3127 | 11.0 | 1116 | 0.9848 | 0.7729 | 0.7671 | 0.7894 | 0.6872 | 0.7894 |
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- | 0.3127 | 12.0 | 1218 | 0.9788 | 0.7751 | 0.7670 | 0.7894 | 0.6913 | 0.7894 |
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- | 0.3127 | 13.0 | 1319 | 0.9980 | 0.7768 | 0.7717 | 0.7919 | 0.6924 | 0.7919 |
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- | 0.3127 | 14.0 | 1421 | 1.0069 | 0.7763 | 0.7721 | 0.7919 | 0.6924 | 0.7919 |
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- | 0.1402 | 14.93 | 1515 | 1.0032 | 0.7793 | 0.7728 | 0.7931 | 0.6958 | 0.7931 |
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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: 0.9723
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+ - Macro F1: 0.7972
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+ - Precision: 0.8006
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+ - Recall: 0.8116
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+ - Kappa: 0.7148
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+ - Accuracy: 0.8116
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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 | 1.2223 | 0.5901 | 0.5634 | 0.6884 | 0.4728 | 0.6884 |
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+ | No log | 2.0 | 203 | 1.0235 | 0.6570 | 0.6224 | 0.7303 | 0.5496 | 0.7303 |
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+ | No log | 3.0 | 304 | 0.9087 | 0.7101 | 0.7166 | 0.7623 | 0.6226 | 0.7623 |
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+ | No log | 4.0 | 406 | 0.8119 | 0.7500 | 0.7346 | 0.7808 | 0.6711 | 0.7808 |
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+ | 0.9826 | 5.0 | 507 | 0.8113 | 0.7821 | 0.7832 | 0.8030 | 0.6989 | 0.8030 |
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+ | 0.9826 | 6.0 | 609 | 0.8290 | 0.7765 | 0.7719 | 0.7943 | 0.6919 | 0.7943 |
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+ | 0.9826 | 7.0 | 710 | 0.8481 | 0.7756 | 0.7696 | 0.7980 | 0.6907 | 0.7980 |
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+ | 0.9826 | 8.0 | 812 | 0.8620 | 0.7820 | 0.7747 | 0.8030 | 0.6974 | 0.8030 |
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+ | 0.9826 | 9.0 | 913 | 0.8717 | 0.7891 | 0.7878 | 0.8042 | 0.7055 | 0.8042 |
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+ | 0.3153 | 10.0 | 1015 | 0.9027 | 0.7872 | 0.7879 | 0.8067 | 0.7070 | 0.8067 |
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+ | 0.3153 | 11.0 | 1116 | 0.9492 | 0.7902 | 0.7915 | 0.8067 | 0.7066 | 0.8067 |
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+ | 0.3153 | 12.0 | 1218 | 0.9462 | 0.7877 | 0.7850 | 0.8042 | 0.7037 | 0.8042 |
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+ | 0.3153 | 13.0 | 1319 | 0.9696 | 0.7881 | 0.7892 | 0.8030 | 0.7022 | 0.8030 |
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+ | 0.3153 | 14.0 | 1421 | 0.9658 | 0.7931 | 0.7975 | 0.8067 | 0.7090 | 0.8067 |
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+ | 0.1362 | 14.93 | 1515 | 0.9723 | 0.7972 | 0.8006 | 0.8116 | 0.7148 | 0.8116 |
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  ### Framework versions