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bert-finetuned-sla

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bert
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update model card README.md

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@@ -17,10 +17,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2965
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- - F1: 0.7121
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- - Roc Auc: 0.8162
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- - Accuracy: 0.5098
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  ## Model description
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@@ -51,26 +51,26 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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- | No log | 1.0 | 30 | 0.5013 | 0.1842 | 0.5497 | 0.0784 |
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- | No log | 2.0 | 60 | 0.4369 | 0.15 | 0.5337 | 0.0784 |
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- | No log | 3.0 | 90 | 0.3724 | 0.5794 | 0.7141 | 0.4118 |
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- | No log | 4.0 | 120 | 0.3463 | 0.6560 | 0.7738 | 0.4314 |
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- | No log | 5.0 | 150 | 0.3212 | 0.6452 | 0.7664 | 0.4314 |
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- | No log | 6.0 | 180 | 0.3092 | 0.6190 | 0.7539 | 0.4510 |
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- | No log | 7.0 | 210 | 0.3096 | 0.6772 | 0.7885 | 0.4902 |
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- | No log | 8.0 | 240 | 0.3025 | 0.6870 | 0.7997 | 0.4706 |
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- | No log | 9.0 | 270 | 0.3118 | 0.6875 | 0.7958 | 0.4902 |
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- | No log | 10.0 | 300 | 0.2965 | 0.7121 | 0.8162 | 0.5098 |
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- | No log | 11.0 | 330 | 0.2971 | 0.7023 | 0.8088 | 0.4902 |
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- | No log | 12.0 | 360 | 0.3071 | 0.6866 | 0.8036 | 0.4510 |
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- | No log | 13.0 | 390 | 0.3015 | 0.6718 | 0.7907 | 0.4510 |
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- | No log | 14.0 | 420 | 0.3087 | 0.6718 | 0.7907 | 0.4314 |
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- | No log | 15.0 | 450 | 0.2978 | 0.6970 | 0.8071 | 0.4706 |
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- | No log | 16.0 | 480 | 0.3058 | 0.6718 | 0.7907 | 0.4510 |
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- | 0.219 | 17.0 | 510 | 0.3039 | 0.6769 | 0.7924 | 0.4510 |
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- | 0.219 | 18.0 | 540 | 0.3015 | 0.6870 | 0.7997 | 0.4706 |
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- | 0.219 | 19.0 | 570 | 0.3011 | 0.6870 | 0.7997 | 0.4706 |
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- | 0.219 | 20.0 | 600 | 0.3014 | 0.6870 | 0.7997 | 0.4706 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3274
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+ - F1: 0.6555
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+ - Roc Auc: 0.7660
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+ - Accuracy: 0.5294
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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+ | No log | 1.0 | 30 | 0.4994 | 0.0 | 0.5 | 0.0 |
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+ | No log | 2.0 | 60 | 0.4408 | 0.0 | 0.5 | 0.0 |
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+ | No log | 3.0 | 90 | 0.3761 | 0.4444 | 0.6462 | 0.1961 |
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+ | No log | 4.0 | 120 | 0.3438 | 0.6496 | 0.7604 | 0.4706 |
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+ | No log | 5.0 | 150 | 0.3274 | 0.6555 | 0.7660 | 0.5294 |
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+ | No log | 6.0 | 180 | 0.3093 | 0.6557 | 0.7699 | 0.4706 |
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+ | No log | 7.0 | 210 | 0.3083 | 0.6560 | 0.7738 | 0.5098 |
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+ | No log | 8.0 | 240 | 0.3030 | 0.6457 | 0.7703 | 0.4706 |
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+ | No log | 9.0 | 270 | 0.3096 | 0.6667 | 0.7811 | 0.4902 |
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+ | No log | 10.0 | 300 | 0.2976 | 0.6718 | 0.7907 | 0.5098 |
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+ | No log | 11.0 | 330 | 0.2986 | 0.6769 | 0.7924 | 0.5294 |
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+ | No log | 12.0 | 360 | 0.3046 | 0.6562 | 0.7777 | 0.5098 |
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+ | No log | 13.0 | 390 | 0.2988 | 0.6870 | 0.7997 | 0.4902 |
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+ | No log | 14.0 | 420 | 0.3026 | 0.6769 | 0.7924 | 0.5098 |
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+ | No log | 15.0 | 450 | 0.3005 | 0.6870 | 0.7997 | 0.5098 |
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+ | No log | 16.0 | 480 | 0.3012 | 0.6822 | 0.7941 | 0.5098 |
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+ | 0.2216 | 17.0 | 510 | 0.3013 | 0.6977 | 0.8032 | 0.5294 |
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+ | 0.2216 | 18.0 | 540 | 0.3033 | 0.6977 | 0.8032 | 0.5294 |
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+ | 0.2216 | 19.0 | 570 | 0.3024 | 0.6977 | 0.8032 | 0.5294 |
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+ | 0.2216 | 20.0 | 600 | 0.3027 | 0.6923 | 0.8015 | 0.5098 |
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  ### Framework versions