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update model card README.md

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@@ -17,9 +17,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.9614
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  - Accuracy: 0.5103
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- - F1: 0.4923
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  ## Model description
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@@ -50,36 +50,36 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | 1.4993 | 1.0 | 15 | 1.4646 | 0.3379 | 0.1707 |
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- | 1.4661 | 2.0 | 30 | 1.4345 | 0.3379 | 0.1827 |
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- | 1.4397 | 3.0 | 45 | 1.3804 | 0.3793 | 0.2763 |
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- | 1.3817 | 4.0 | 60 | 1.3284 | 0.3931 | 0.2855 |
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- | 1.3375 | 5.0 | 75 | 1.2819 | 0.4207 | 0.3629 |
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- | 1.3073 | 6.0 | 90 | 1.2493 | 0.4621 | 0.4363 |
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- | 1.3085 | 7.0 | 105 | 1.2250 | 0.4828 | 0.4577 |
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- | 1.2545 | 8.0 | 120 | 1.2133 | 0.4966 | 0.4758 |
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- | 1.29 | 9.0 | 135 | 1.1806 | 0.5034 | 0.4776 |
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- | 1.2587 | 10.0 | 150 | 1.1522 | 0.5034 | 0.4764 |
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- | 1.2009 | 11.0 | 165 | 1.1269 | 0.4966 | 0.4760 |
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- | 1.2258 | 12.0 | 180 | 1.1133 | 0.4966 | 0.4734 |
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- | 1.1466 | 13.0 | 195 | 1.0942 | 0.5034 | 0.4699 |
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- | 1.1569 | 14.0 | 210 | 1.0735 | 0.5034 | 0.4793 |
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- | 1.1194 | 15.0 | 225 | 1.0616 | 0.5034 | 0.4832 |
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- | 1.0909 | 16.0 | 240 | 1.0529 | 0.5034 | 0.4560 |
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- | 1.153 | 17.0 | 255 | 1.0334 | 0.5034 | 0.4822 |
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- | 1.0086 | 18.0 | 270 | 1.0246 | 0.5034 | 0.4765 |
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- | 1.1102 | 19.0 | 285 | 1.0111 | 0.5103 | 0.4920 |
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- | 1.0967 | 20.0 | 300 | 1.0024 | 0.5103 | 0.4952 |
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- | 1.0265 | 21.0 | 315 | 0.9922 | 0.5103 | 0.4937 |
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- | 1.0377 | 22.0 | 330 | 0.9848 | 0.5103 | 0.4908 |
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- | 1.0156 | 23.0 | 345 | 0.9794 | 0.5103 | 0.4972 |
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- | 1.0807 | 24.0 | 360 | 0.9796 | 0.5103 | 0.4928 |
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- | 1.051 | 25.0 | 375 | 0.9726 | 0.5103 | 0.4831 |
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- | 0.9827 | 26.0 | 390 | 0.9675 | 0.5103 | 0.4972 |
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- | 1.0228 | 27.0 | 405 | 0.9646 | 0.5103 | 0.4951 |
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- | 1.0013 | 28.0 | 420 | 0.9627 | 0.5103 | 0.4950 |
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- | 0.9963 | 29.0 | 435 | 0.9617 | 0.5103 | 0.4938 |
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- | 0.9897 | 30.0 | 450 | 0.9614 | 0.5103 | 0.4923 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.9616
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  - Accuracy: 0.5103
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+ - F1: 0.4983
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | 1.5782 | 1.0 | 15 | 1.4989 | 0.3448 | 0.2657 |
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+ | 1.5021 | 2.0 | 30 | 1.4732 | 0.3655 | 0.2645 |
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+ | 1.4674 | 3.0 | 45 | 1.4384 | 0.3448 | 0.2525 |
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+ | 1.4277 | 4.0 | 60 | 1.4140 | 0.3517 | 0.2751 |
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+ | 1.4341 | 5.0 | 75 | 1.3905 | 0.3379 | 0.2546 |
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+ | 1.3698 | 6.0 | 90 | 1.3697 | 0.3724 | 0.2936 |
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+ | 1.4233 | 7.0 | 105 | 1.3196 | 0.3862 | 0.3073 |
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+ | 1.3112 | 8.0 | 120 | 1.3048 | 0.4552 | 0.3958 |
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+ | 1.372 | 9.0 | 135 | 1.2548 | 0.4138 | 0.3385 |
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+ | 1.3284 | 10.0 | 150 | 1.2020 | 0.4759 | 0.4287 |
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+ | 1.2412 | 11.0 | 165 | 1.1672 | 0.4966 | 0.4594 |
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+ | 1.2508 | 12.0 | 180 | 1.1453 | 0.4897 | 0.4740 |
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+ | 1.1843 | 13.0 | 195 | 1.1172 | 0.4966 | 0.4784 |
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+ | 1.1694 | 14.0 | 210 | 1.1006 | 0.4966 | 0.4785 |
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+ | 1.1438 | 15.0 | 225 | 1.0763 | 0.5034 | 0.4851 |
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+ | 1.1066 | 16.0 | 240 | 1.0603 | 0.5034 | 0.4815 |
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+ | 1.1357 | 17.0 | 255 | 1.0435 | 0.5034 | 0.4821 |
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+ | 1.0352 | 18.0 | 270 | 1.0358 | 0.5034 | 0.4803 |
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+ | 1.1355 | 19.0 | 285 | 1.0183 | 0.5103 | 0.4941 |
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+ | 1.063 | 20.0 | 300 | 1.0063 | 0.5103 | 0.4957 |
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+ | 1.0329 | 21.0 | 315 | 0.9960 | 0.5103 | 0.4989 |
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+ | 1.063 | 22.0 | 330 | 0.9867 | 0.5103 | 0.4989 |
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+ | 1.0289 | 23.0 | 345 | 0.9821 | 0.5103 | 0.4980 |
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+ | 1.0624 | 24.0 | 360 | 0.9816 | 0.5103 | 0.4942 |
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+ | 1.0404 | 25.0 | 375 | 0.9723 | 0.5103 | 0.4939 |
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+ | 0.9791 | 26.0 | 390 | 0.9693 | 0.5103 | 0.4985 |
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+ | 1.0365 | 27.0 | 405 | 0.9663 | 0.5103 | 0.4980 |
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+ | 1.0129 | 28.0 | 420 | 0.9637 | 0.5103 | 0.5002 |
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+ | 0.9844 | 29.0 | 435 | 0.9617 | 0.5103 | 0.4997 |
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+ | 1.0049 | 30.0 | 450 | 0.9616 | 0.5103 | 0.4983 |
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  ### Framework versions