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roberta-base

Text Classification
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TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
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End of training

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  1. README.md +30 -30
  2. model.safetensors +1 -1
  3. runs/Apr14_03-57-02_3e1d3a604afe/events.out.tfevents.1713067023.3e1d3a604afe.34.4 +2 -2
README.md CHANGED
@@ -17,21 +17,21 @@ should probably proofread and complete it, then remove this comment. -->
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  This model was trained from scratch on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.3313
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- - Accuracy: 0.6344
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- - Precision: 0.5926
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- - Recall: 0.6344
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- - Precision Macro: 0.3231
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- - Recall Macro: 0.3898
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- - Macro Fpr: 0.0398
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- - Weighted Fpr: 0.0395
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- - Weighted Specificity: 0.9525
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- - Macro Specificity: 0.9725
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- - Weighted Sensitivity: 0.6344
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- - Macro Sensitivity: 0.3898
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- - F1 Micro: 0.6344
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- - F1 Macro: 0.3416
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- - F1 Weighted: 0.6051
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  ## Model description
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@@ -63,21 +63,21 @@ 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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- | 2.2214 | 1.0 | 643 | 2.1982 | 0.2455 | 0.0603 | 0.2455 | 0.0164 | 0.0667 | 0.0667 | 0.1800 | 0.7545 | 0.9333 | 0.2455 | 0.0667 | 0.2455 | 0.0263 | 0.0968 |
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- | 2.1462 | 2.0 | 1286 | 1.9635 | 0.3362 | 0.1591 | 0.3362 | 0.0531 | 0.1155 | 0.0909 | 0.1236 | 0.8494 | 0.9457 | 0.3362 | 0.1155 | 0.3362 | 0.0655 | 0.2010 |
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- | 1.995 | 3.0 | 1929 | 1.9322 | 0.3555 | 0.2341 | 0.3555 | 0.0926 | 0.1435 | 0.0954 | 0.1146 | 0.8912 | 0.9498 | 0.3555 | 0.1435 | 0.3555 | 0.0931 | 0.2542 |
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- | 1.8909 | 4.0 | 2572 | 1.8875 | 0.3788 | 0.2875 | 0.3788 | 0.1270 | 0.2191 | 0.0871 | 0.1049 | 0.9094 | 0.9525 | 0.3788 | 0.2191 | 0.3788 | 0.1450 | 0.2977 |
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- | 1.7621 | 5.0 | 3215 | 1.7169 | 0.4284 | 0.3747 | 0.4284 | 0.1820 | 0.2270 | 0.0743 | 0.0870 | 0.9092 | 0.9558 | 0.4284 | 0.2270 | 0.4284 | 0.1745 | 0.3594 |
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- | 1.6254 | 6.0 | 3858 | 1.8831 | 0.4500 | 0.3948 | 0.4500 | 0.1878 | 0.2545 | 0.0794 | 0.0803 | 0.9240 | 0.9583 | 0.4500 | 0.2545 | 0.4500 | 0.1859 | 0.3970 |
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- | 1.4139 | 7.0 | 4501 | 1.6625 | 0.4640 | 0.4516 | 0.4640 | 0.2328 | 0.2949 | 0.0717 | 0.0762 | 0.9379 | 0.9601 | 0.4640 | 0.2949 | 0.4640 | 0.2285 | 0.4298 |
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- | 1.2681 | 8.0 | 5144 | 1.4462 | 0.5383 | 0.5269 | 0.5383 | 0.2766 | 0.3381 | 0.0560 | 0.0577 | 0.9451 | 0.9656 | 0.5383 | 0.3381 | 0.5383 | 0.2703 | 0.4888 |
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- | 1.2351 | 9.0 | 5787 | 1.3803 | 0.5515 | 0.5183 | 0.5515 | 0.2800 | 0.3399 | 0.0540 | 0.0549 | 0.9418 | 0.9662 | 0.5515 | 0.3399 | 0.5515 | 0.2922 | 0.5183 |
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- | 1.1161 | 10.0 | 6430 | 1.3406 | 0.5747 | 0.5342 | 0.5747 | 0.2900 | 0.3575 | 0.0502 | 0.0502 | 0.9453 | 0.9680 | 0.5747 | 0.3575 | 0.5747 | 0.3016 | 0.5351 |
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- | 0.9747 | 11.0 | 7073 | 1.3810 | 0.6150 | 0.5606 | 0.6150 | 0.3084 | 0.3747 | 0.0444 | 0.0428 | 0.9437 | 0.9706 | 0.6150 | 0.3747 | 0.6150 | 0.3262 | 0.5766 |
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- | 0.9145 | 12.0 | 7716 | 1.2736 | 0.6197 | 0.5857 | 0.6197 | 0.3199 | 0.3808 | 0.0421 | 0.0420 | 0.9508 | 0.9714 | 0.6197 | 0.3808 | 0.6197 | 0.3330 | 0.5913 |
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- | 0.8879 | 13.0 | 8359 | 1.3463 | 0.6352 | 0.5915 | 0.6352 | 0.3195 | 0.3877 | 0.0398 | 0.0394 | 0.9527 | 0.9725 | 0.6352 | 0.3877 | 0.6352 | 0.3368 | 0.6019 |
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- | 0.8034 | 14.0 | 9002 | 1.3203 | 0.6313 | 0.5913 | 0.6313 | 0.3223 | 0.3887 | 0.0402 | 0.0400 | 0.9523 | 0.9722 | 0.6313 | 0.3887 | 0.6313 | 0.3408 | 0.6029 |
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- | 0.7438 | 15.0 | 9645 | 1.3313 | 0.6344 | 0.5926 | 0.6344 | 0.3231 | 0.3898 | 0.0398 | 0.0395 | 0.9525 | 0.9725 | 0.6344 | 0.3898 | 0.6344 | 0.3416 | 0.6051 |
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  ### Framework versions
 
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  This model was trained from scratch on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.4960
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+ - Accuracy: 0.8187
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+ - Precision: 0.8203
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+ - Recall: 0.8187
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+ - Precision Macro: 0.7709
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+ - Recall Macro: 0.7766
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+ - Macro Fpr: 0.0161
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+ - Weighted Fpr: 0.0156
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+ - Weighted Specificity: 0.9773
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+ - Macro Specificity: 0.9864
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+ - Weighted Sensitivity: 0.8187
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+ - Macro Sensitivity: 0.7766
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+ - F1 Micro: 0.8187
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+ - F1 Macro: 0.7673
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+ - F1 Weighted: 0.8188
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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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+ | 1.3582 | 1.0 | 643 | 1.0091 | 0.6871 | 0.6460 | 0.6871 | 0.4145 | 0.4143 | 0.0338 | 0.0315 | 0.9470 | 0.9756 | 0.6871 | 0.4143 | 0.6871 | 0.4011 | 0.6558 |
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+ | 0.8822 | 2.0 | 1286 | 0.8978 | 0.7227 | 0.7088 | 0.7227 | 0.4656 | 0.5103 | 0.0271 | 0.0267 | 0.9708 | 0.9796 | 0.7227 | 0.5103 | 0.7227 | 0.4745 | 0.7052 |
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+ | 0.7632 | 3.0 | 1929 | 1.1579 | 0.7351 | 0.7535 | 0.7351 | 0.6166 | 0.5729 | 0.0251 | 0.0251 | 0.9646 | 0.9800 | 0.7351 | 0.5729 | 0.7351 | 0.5561 | 0.7214 |
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+ | 0.5103 | 4.0 | 2572 | 0.8608 | 0.7901 | 0.8043 | 0.7901 | 0.6758 | 0.6482 | 0.0189 | 0.0186 | 0.9751 | 0.9843 | 0.7901 | 0.6482 | 0.7901 | 0.6239 | 0.7866 |
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+ | 0.4212 | 5.0 | 3215 | 1.1346 | 0.7668 | 0.7546 | 0.7668 | 0.5721 | 0.5941 | 0.0216 | 0.0213 | 0.9723 | 0.9826 | 0.7668 | 0.5941 | 0.7668 | 0.5717 | 0.7546 |
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+ | 0.3384 | 6.0 | 3858 | 1.1846 | 0.7870 | 0.8033 | 0.7870 | 0.7459 | 0.7345 | 0.0193 | 0.0190 | 0.9754 | 0.9842 | 0.7870 | 0.7345 | 0.7870 | 0.7302 | 0.7893 |
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+ | 0.2476 | 7.0 | 4501 | 1.1805 | 0.8125 | 0.8057 | 0.8125 | 0.7293 | 0.7175 | 0.0169 | 0.0162 | 0.9730 | 0.9857 | 0.8125 | 0.7175 | 0.8125 | 0.7214 | 0.8076 |
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+ | 0.1763 | 8.0 | 5144 | 1.3168 | 0.8009 | 0.8136 | 0.8009 | 0.7409 | 0.7448 | 0.0179 | 0.0174 | 0.9752 | 0.9851 | 0.8009 | 0.7448 | 0.8009 | 0.7378 | 0.8023 |
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+ | 0.1345 | 9.0 | 5787 | 1.3788 | 0.8133 | 0.8249 | 0.8133 | 0.7776 | 0.7864 | 0.0165 | 0.0161 | 0.9766 | 0.9860 | 0.8133 | 0.7864 | 0.8133 | 0.7718 | 0.8155 |
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+ | 0.123 | 10.0 | 6430 | 1.4001 | 0.8087 | 0.8132 | 0.8087 | 0.7727 | 0.7841 | 0.0172 | 0.0166 | 0.9755 | 0.9856 | 0.8087 | 0.7841 | 0.8087 | 0.7660 | 0.8081 |
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+ | 0.0668 | 11.0 | 7073 | 1.4279 | 0.8172 | 0.8155 | 0.8172 | 0.7789 | 0.7834 | 0.0165 | 0.0157 | 0.9747 | 0.9861 | 0.8172 | 0.7834 | 0.8172 | 0.7713 | 0.8143 |
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+ | 0.0606 | 12.0 | 7716 | 1.5104 | 0.8149 | 0.8186 | 0.8149 | 0.7756 | 0.7845 | 0.0166 | 0.0160 | 0.9757 | 0.9860 | 0.8149 | 0.7845 | 0.8149 | 0.7701 | 0.8153 |
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+ | 0.0396 | 13.0 | 8359 | 1.4985 | 0.8156 | 0.8163 | 0.8156 | 0.7824 | 0.7854 | 0.0165 | 0.0159 | 0.9759 | 0.9861 | 0.8156 | 0.7854 | 0.8156 | 0.7748 | 0.8143 |
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+ | 0.0191 | 14.0 | 9002 | 1.5025 | 0.8172 | 0.8199 | 0.8172 | 0.7659 | 0.7763 | 0.0162 | 0.0157 | 0.9776 | 0.9863 | 0.8172 | 0.7763 | 0.8172 | 0.7660 | 0.8178 |
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+ | 0.013 | 15.0 | 9645 | 1.4960 | 0.8187 | 0.8203 | 0.8187 | 0.7709 | 0.7766 | 0.0161 | 0.0156 | 0.9773 | 0.9864 | 0.8187 | 0.7766 | 0.8187 | 0.7673 | 0.8188 |
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  ### Framework versions
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