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bart-large-lora

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@@ -19,21 +19,21 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6647
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- - Accuracy: 0.7901
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- - Precision: 0.7852
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- - Recall: 0.7901
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- - Precision Macro: 0.6664
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- - Recall Macro: 0.6485
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- - Macro Fpr: 0.0194
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- - Weighted Fpr: 0.0186
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- - Weighted Specificity: 0.9735
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- - Macro Specificity: 0.9842
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- - Weighted Sensitivity: 0.7901
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- - Macro Sensitivity: 0.6485
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- - F1 Micro: 0.7901
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- - F1 Macro: 0.6250
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- - F1 Weighted: 0.7804
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  ## Model description
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@@ -66,21 +66,21 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
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- | No log | 1.0 | 160 | 1.3205 | 0.6112 | 0.5322 | 0.6112 | 0.2887 | 0.3024 | 0.0464 | 0.0435 | 0.9266 | 0.9692 | 0.6112 | 0.3024 | 0.6112 | 0.2871 | 0.5575 |
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- | No log | 2.0 | 321 | 0.8875 | 0.6995 | 0.6728 | 0.6995 | 0.3822 | 0.4254 | 0.0306 | 0.0298 | 0.9609 | 0.9774 | 0.6995 | 0.4254 | 0.6995 | 0.3948 | 0.6808 |
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- | No log | 3.0 | 482 | 0.8427 | 0.7064 | 0.6952 | 0.7064 | 0.4131 | 0.4442 | 0.0295 | 0.0288 | 0.9641 | 0.9780 | 0.7064 | 0.4442 | 0.7064 | 0.3969 | 0.6752 |
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- | 1.2895 | 4.0 | 643 | 0.7719 | 0.7273 | 0.7132 | 0.7273 | 0.4198 | 0.4598 | 0.0264 | 0.0261 | 0.9690 | 0.9798 | 0.7273 | 0.4598 | 0.7273 | 0.4284 | 0.7167 |
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- | 1.2895 | 5.0 | 803 | 0.7388 | 0.7506 | 0.7400 | 0.7506 | 0.5733 | 0.5165 | 0.0239 | 0.0232 | 0.9697 | 0.9814 | 0.7506 | 0.5165 | 0.7506 | 0.5072 | 0.7368 |
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- | 1.2895 | 6.0 | 964 | 0.7526 | 0.7444 | 0.7337 | 0.7444 | 0.5703 | 0.5230 | 0.0247 | 0.0239 | 0.9691 | 0.9809 | 0.7444 | 0.5230 | 0.7444 | 0.5088 | 0.7268 |
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- | 0.7332 | 7.0 | 1125 | 0.7082 | 0.7552 | 0.7436 | 0.7552 | 0.5665 | 0.5728 | 0.0233 | 0.0226 | 0.9712 | 0.9818 | 0.7552 | 0.5728 | 0.7552 | 0.5609 | 0.7461 |
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- | 0.7332 | 8.0 | 1286 | 0.7161 | 0.7583 | 0.7489 | 0.7583 | 0.5641 | 0.5975 | 0.0228 | 0.0223 | 0.9721 | 0.9820 | 0.7583 | 0.5975 | 0.7583 | 0.5756 | 0.7503 |
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- | 0.7332 | 9.0 | 1446 | 0.6831 | 0.7777 | 0.7587 | 0.7777 | 0.5781 | 0.6069 | 0.0208 | 0.0200 | 0.9715 | 0.9833 | 0.7777 | 0.6069 | 0.7777 | 0.5875 | 0.7653 |
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- | 0.6167 | 10.0 | 1607 | 0.6683 | 0.7862 | 0.7714 | 0.7862 | 0.5917 | 0.6174 | 0.0198 | 0.0191 | 0.9728 | 0.9839 | 0.7862 | 0.6174 | 0.7862 | 0.5987 | 0.7754 |
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- | 0.6167 | 11.0 | 1768 | 0.6885 | 0.7761 | 0.7628 | 0.7761 | 0.5817 | 0.6220 | 0.0210 | 0.0202 | 0.9723 | 0.9832 | 0.7761 | 0.6220 | 0.7761 | 0.5946 | 0.7642 |
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- | 0.6167 | 12.0 | 1929 | 0.6830 | 0.7870 | 0.7826 | 0.7870 | 0.6627 | 0.6464 | 0.0197 | 0.0190 | 0.9734 | 0.9840 | 0.7870 | 0.6464 | 0.7870 | 0.6214 | 0.7764 |
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- | 0.5314 | 13.0 | 2089 | 0.6605 | 0.7916 | 0.7770 | 0.7916 | 0.5965 | 0.6358 | 0.0192 | 0.0185 | 0.9741 | 0.9844 | 0.7916 | 0.6358 | 0.7916 | 0.6111 | 0.7818 |
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- | 0.5314 | 14.0 | 2250 | 0.6614 | 0.7909 | 0.7794 | 0.7909 | 0.6368 | 0.6478 | 0.0193 | 0.0185 | 0.9729 | 0.9842 | 0.7909 | 0.6478 | 0.7909 | 0.6261 | 0.7803 |
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- | 0.5314 | 14.93 | 2400 | 0.6647 | 0.7901 | 0.7852 | 0.7901 | 0.6664 | 0.6485 | 0.0194 | 0.0186 | 0.9735 | 0.9842 | 0.7901 | 0.6485 | 0.7901 | 0.6250 | 0.7804 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6655
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+ - Accuracy: 0.7963
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+ - Precision: 0.7841
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+ - Recall: 0.7963
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+ - Precision Macro: 0.5968
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+ - Recall Macro: 0.6325
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+ - Macro Fpr: 0.0186
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+ - Weighted Fpr: 0.0179
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+ - Weighted Specificity: 0.9749
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+ - Macro Specificity: 0.9847
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+ - Weighted Sensitivity: 0.7963
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+ - Macro Sensitivity: 0.6325
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+ - F1 Micro: 0.7963
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+ - F1 Macro: 0.6074
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+ - F1 Weighted: 0.7859
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
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+ | No log | 1.0 | 160 | 1.2642 | 0.6313 | 0.5477 | 0.6313 | 0.3009 | 0.3127 | 0.0428 | 0.0400 | 0.9351 | 0.9711 | 0.6313 | 0.3127 | 0.6313 | 0.2941 | 0.5769 |
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+ | No log | 2.0 | 321 | 0.8962 | 0.7119 | 0.6939 | 0.7119 | 0.3937 | 0.4525 | 0.0285 | 0.0281 | 0.9669 | 0.9786 | 0.7119 | 0.4525 | 0.7119 | 0.4107 | 0.6960 |
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+ | No log | 3.0 | 482 | 0.8204 | 0.7196 | 0.6953 | 0.7196 | 0.3974 | 0.4468 | 0.0278 | 0.0271 | 0.9653 | 0.9790 | 0.7196 | 0.4468 | 0.7196 | 0.3998 | 0.6885 |
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+ | 1.2731 | 4.0 | 643 | 0.7519 | 0.7436 | 0.7186 | 0.7436 | 0.4131 | 0.4673 | 0.0244 | 0.0240 | 0.9695 | 0.9809 | 0.7436 | 0.4673 | 0.7436 | 0.4272 | 0.7248 |
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+ | 1.2731 | 5.0 | 803 | 0.7364 | 0.7475 | 0.7524 | 0.7475 | 0.6132 | 0.5050 | 0.0243 | 0.0236 | 0.9679 | 0.9810 | 0.7475 | 0.5050 | 0.7475 | 0.4905 | 0.7286 |
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+ | 1.2731 | 6.0 | 964 | 0.7273 | 0.7514 | 0.7423 | 0.7514 | 0.5784 | 0.5258 | 0.0237 | 0.0231 | 0.9699 | 0.9814 | 0.7514 | 0.5258 | 0.7514 | 0.5150 | 0.7311 |
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+ | 0.7243 | 7.0 | 1125 | 0.6993 | 0.7645 | 0.7478 | 0.7645 | 0.5498 | 0.5565 | 0.0222 | 0.0215 | 0.9721 | 0.9824 | 0.7645 | 0.5565 | 0.7645 | 0.5453 | 0.7538 |
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+ | 0.7243 | 8.0 | 1286 | 0.6952 | 0.7769 | 0.7639 | 0.7769 | 0.5682 | 0.5888 | 0.0207 | 0.0201 | 0.9731 | 0.9833 | 0.7769 | 0.5888 | 0.7769 | 0.5700 | 0.7649 |
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+ | 0.7243 | 9.0 | 1446 | 0.6759 | 0.7823 | 0.7708 | 0.7823 | 0.5764 | 0.5877 | 0.0201 | 0.0195 | 0.9739 | 0.9838 | 0.7823 | 0.5877 | 0.7823 | 0.5699 | 0.7697 |
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+ | 0.6098 | 10.0 | 1607 | 0.6705 | 0.7847 | 0.7720 | 0.7847 | 0.5899 | 0.6176 | 0.0199 | 0.0192 | 0.9732 | 0.9839 | 0.7847 | 0.6176 | 0.7847 | 0.5935 | 0.7724 |
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+ | 0.6098 | 11.0 | 1768 | 0.6794 | 0.7909 | 0.7737 | 0.7909 | 0.5882 | 0.6237 | 0.0193 | 0.0185 | 0.9736 | 0.9843 | 0.7909 | 0.6237 | 0.7909 | 0.5988 | 0.7773 |
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+ | 0.6098 | 12.0 | 1929 | 0.6836 | 0.7909 | 0.7816 | 0.7909 | 0.5973 | 0.6285 | 0.0192 | 0.0185 | 0.9742 | 0.9843 | 0.7909 | 0.6285 | 0.7909 | 0.6034 | 0.7802 |
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+ | 0.5239 | 13.0 | 2089 | 0.6508 | 0.7932 | 0.7783 | 0.7932 | 0.5965 | 0.6273 | 0.0189 | 0.0183 | 0.9738 | 0.9845 | 0.7932 | 0.6273 | 0.7932 | 0.6046 | 0.7821 |
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+ | 0.5239 | 14.0 | 2250 | 0.6588 | 0.7963 | 0.7823 | 0.7963 | 0.5957 | 0.6290 | 0.0186 | 0.0179 | 0.9746 | 0.9847 | 0.7963 | 0.6290 | 0.7963 | 0.6055 | 0.7852 |
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+ | 0.5239 | 14.93 | 2400 | 0.6655 | 0.7963 | 0.7841 | 0.7963 | 0.5968 | 0.6325 | 0.0186 | 0.0179 | 0.9749 | 0.9847 | 0.7963 | 0.6325 | 0.7963 | 0.6074 | 0.7859 |
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  ### Framework versions