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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: 0.9723
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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
 
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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: 0.9948
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+ - Macro F1: 0.7856
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+ - Precision: 0.7820
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+ - Recall: 0.7956
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+ - Kappa: 0.6940
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+ - Accuracy: 0.7956
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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 | 1.1562 | 0.6031 | 0.5561 | 0.7044 | 0.4967 | 0.7044 |
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+ | No log | 2.0 | 203 | 0.9119 | 0.7151 | 0.7107 | 0.7672 | 0.6236 | 0.7672 |
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+ | No log | 3.0 | 304 | 0.8493 | 0.7280 | 0.7139 | 0.7734 | 0.6381 | 0.7734 |
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+ | No log | 4.0 | 406 | 0.8087 | 0.7455 | 0.7632 | 0.7648 | 0.6421 | 0.7648 |
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+ | 0.9431 | 5.0 | 507 | 0.7735 | 0.7779 | 0.7741 | 0.7931 | 0.6858 | 0.7931 |
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+ | 0.9431 | 6.0 | 609 | 0.8201 | 0.7753 | 0.7735 | 0.7869 | 0.6797 | 0.7869 |
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+ | 0.9431 | 7.0 | 710 | 0.8564 | 0.7886 | 0.7883 | 0.8017 | 0.7004 | 0.8017 |
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+ | 0.9431 | 8.0 | 812 | 0.8712 | 0.7799 | 0.7754 | 0.7894 | 0.6854 | 0.7894 |
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+ | 0.9431 | 9.0 | 913 | 0.9142 | 0.7775 | 0.7751 | 0.7869 | 0.6811 | 0.7869 |
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+ | 0.2851 | 10.0 | 1015 | 0.9007 | 0.7820 | 0.7764 | 0.7943 | 0.6913 | 0.7943 |
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+ | 0.2851 | 11.0 | 1116 | 0.9425 | 0.7859 | 0.7825 | 0.7956 | 0.6940 | 0.7956 |
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+ | 0.2851 | 12.0 | 1218 | 0.9798 | 0.7815 | 0.7797 | 0.7906 | 0.6869 | 0.7906 |
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+ | 0.2851 | 13.0 | 1319 | 0.9895 | 0.7895 | 0.7860 | 0.7993 | 0.7003 | 0.7993 |
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+ | 0.2851 | 14.0 | 1421 | 0.9872 | 0.7854 | 0.7813 | 0.7943 | 0.6935 | 0.7943 |
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+ | 0.1273 | 14.93 | 1515 | 0.9948 | 0.7856 | 0.7820 | 0.7956 | 0.6940 | 0.7956 |
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  ### Framework versions