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sohamtiwari3120
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scideberta-cs-finetuned-ner

Token Classification
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PyTorch
deberta
Generated from Trainer
Inference Endpoints
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@@ -15,18 +15,18 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [KISTI-AI/scideberta-cs](https://huggingface.co/KISTI-AI/scideberta-cs) on the generator dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6414
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- - Overall Precision: 0.6158
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- - Overall Recall: 0.7276
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- - Overall F1: 0.6670
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- - Overall Accuracy: 0.9605
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- - Datasetname F1: 0.6625
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- - Hyperparametername F1: 0.6329
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- - Hyperparametervalue F1: 0.6818
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- - Methodname F1: 0.7266
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- - Metricname F1: 0.6012
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- - Metricvalue F1: 0.8605
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- - Taskname F1: 0.5520
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  ## Model description
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@@ -57,31 +57,34 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Datasetname F1 | Hyperparametername F1 | Hyperparametervalue F1 | Methodname F1 | Metricname F1 | Metricvalue F1 | Taskname F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:---------------------:|:----------------------:|:-------------:|:-------------:|:--------------:|:-----------:|
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- | No log | 1.0 | 131 | 0.4391 | 0.4307 | 0.5791 | 0.4940 | 0.9418 | 0.5121 | 0.3565 | 0.5692 | 0.6528 | 0.4813 | 0.1724 | 0.3852 |
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- | No log | 2.0 | 262 | 0.3244 | 0.4782 | 0.6871 | 0.5639 | 0.9475 | 0.5549 | 0.4489 | 0.6189 | 0.7020 | 0.46 | 0.7179 | 0.4615 |
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- | No log | 3.0 | 393 | 0.3646 | 0.5799 | 0.6945 | 0.6320 | 0.9542 | 0.5917 | 0.5479 | 0.7064 | 0.7293 | 0.5325 | 0.7632 | 0.5420 |
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- | 0.3961 | 4.0 | 524 | 0.3175 | 0.5213 | 0.7362 | 0.6104 | 0.9547 | 0.6136 | 0.5317 | 0.6722 | 0.7065 | 0.4783 | 0.7660 | 0.5345 |
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- | 0.3961 | 5.0 | 655 | 0.3289 | 0.5268 | 0.7485 | 0.6183 | 0.9528 | 0.6333 | 0.5798 | 0.6560 | 0.6901 | 0.4796 | 0.7629 | 0.5232 |
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- | 0.3961 | 6.0 | 786 | 0.3767 | 0.5651 | 0.7239 | 0.6347 | 0.9567 | 0.6258 | 0.6199 | 0.6339 | 0.6858 | 0.5341 | 0.8140 | 0.5583 |
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- | 0.3961 | 7.0 | 917 | 0.3942 | 0.5490 | 0.7288 | 0.6263 | 0.9549 | 0.5902 | 0.5815 | 0.6781 | 0.7083 | 0.5137 | 0.8090 | 0.5198 |
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- | 0.0853 | 8.0 | 1048 | 0.4828 | 0.5565 | 0.7129 | 0.6251 | 0.9538 | 0.6049 | 0.5707 | 0.6667 | 0.6991 | 0.5222 | 0.7957 | 0.5440 |
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- | 0.0853 | 9.0 | 1179 | 0.4430 | 0.5290 | 0.7153 | 0.6082 | 0.9519 | 0.6071 | 0.5249 | 0.6364 | 0.6762 | 0.5444 | 0.7551 | 0.5614 |
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- | 0.0853 | 10.0 | 1310 | 0.5176 | 0.5878 | 0.7227 | 0.6483 | 0.9574 | 0.6335 | 0.6108 | 0.6960 | 0.6979 | 0.5549 | 0.8471 | 0.5603 |
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- | 0.0853 | 11.0 | 1441 | 0.4615 | 0.5634 | 0.7301 | 0.6360 | 0.9568 | 0.6341 | 0.5773 | 0.6809 | 0.6965 | 0.5731 | 0.7957 | 0.5455 |
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- | 0.0356 | 12.0 | 1572 | 0.5570 | 0.5990 | 0.7202 | 0.6540 | 0.9595 | 0.6497 | 0.6139 | 0.6725 | 0.7244 | 0.5568 | 0.8276 | 0.5611 |
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- | 0.0356 | 13.0 | 1703 | 0.5089 | 0.5964 | 0.7288 | 0.6560 | 0.9570 | 0.6624 | 0.6043 | 0.6991 | 0.7176 | 0.5380 | 0.7789 | 0.5973 |
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- | 0.0356 | 14.0 | 1834 | 0.5418 | 0.5887 | 0.7006 | 0.6398 | 0.9572 | 0.5882 | 0.5902 | 0.6667 | 0.7168 | 0.5731 | 0.8333 | 0.5487 |
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- | 0.0356 | 15.0 | 1965 | 0.5775 | 0.6002 | 0.7387 | 0.6623 | 0.9595 | 0.6584 | 0.6168 | 0.6754 | 0.7301 | 0.5629 | 0.8222 | 0.5939 |
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- | 0.0225 | 16.0 | 2096 | 0.5394 | 0.5727 | 0.7153 | 0.6361 | 0.9559 | 0.6543 | 0.5763 | 0.6842 | 0.6879 | 0.5562 | 0.7629 | 0.5677 |
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- | 0.0225 | 17.0 | 2227 | 0.5487 | 0.5793 | 0.7350 | 0.6479 | 0.9567 | 0.6429 | 0.6079 | 0.6784 | 0.7126 | 0.5746 | 0.8333 | 0.5424 |
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- | 0.0225 | 18.0 | 2358 | 0.5742 | 0.6102 | 0.7337 | 0.6663 | 0.9598 | 0.6541 | 0.6351 | 0.7168 | 0.7259 | 0.5747 | 0.7895 | 0.5727 |
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- | 0.0225 | 19.0 | 2489 | 0.6456 | 0.5996 | 0.7018 | 0.6467 | 0.9573 | 0.6460 | 0.6059 | 0.6512 | 0.6998 | 0.5833 | 0.8354 | 0.5714 |
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- | 0.0089 | 20.0 | 2620 | 0.5828 | 0.5882 | 0.7448 | 0.6573 | 0.9584 | 0.6424 | 0.6326 | 0.6814 | 0.7235 | 0.5856 | 0.7848 | 0.5546 |
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- | 0.0089 | 21.0 | 2751 | 0.6115 | 0.5735 | 0.7276 | 0.6414 | 0.9572 | 0.6625 | 0.6043 | 0.7022 | 0.7082 | 0.5514 | 0.6852 | 0.5417 |
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- | 0.0089 | 22.0 | 2882 | 0.7030 | 0.5988 | 0.7067 | 0.6483 | 0.9581 | 0.6752 | 0.5926 | 0.6697 | 0.7041 | 0.5424 | 0.8675 | 0.5818 |
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- | 0.0067 | 23.0 | 3013 | 0.6681 | 0.6040 | 0.7129 | 0.6539 | 0.9585 | 0.6369 | 0.6025 | 0.6757 | 0.7105 | 0.5952 | 0.8642 | 0.5676 |
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- | 0.0067 | 24.0 | 3144 | 0.6378 | 0.6078 | 0.7264 | 0.6618 | 0.9605 | 0.6503 | 0.6259 | 0.7168 | 0.7118 | 0.5765 | 0.8780 | 0.5495 |
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- | 0.0067 | 25.0 | 3275 | 0.6414 | 0.6158 | 0.7276 | 0.6670 | 0.9605 | 0.6625 | 0.6329 | 0.6818 | 0.7266 | 0.6012 | 0.8605 | 0.5520 |
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [KISTI-AI/scideberta-cs](https://huggingface.co/KISTI-AI/scideberta-cs) on the generator dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.8848
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+ - Overall Precision: 0.5492
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+ - Overall Recall: 0.6240
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+ - Overall F1: 0.5842
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+ - Overall Accuracy: 0.9552
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+ - Datasetname F1: 0.4590
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+ - Hyperparametername F1: 0.7273
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+ - Hyperparametervalue F1: 0.7937
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+ - Methodname F1: 0.6227
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+ - Metricname F1: 0.7597
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+ - Metricvalue F1: 0.6250
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+ - Taskname F1: 0.4348
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Datasetname F1 | Hyperparametername F1 | Hyperparametervalue F1 | Methodname F1 | Metricname F1 | Metricvalue F1 | Taskname F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:---------------------:|:----------------------:|:-------------:|:-------------:|:--------------:|:-----------:|
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+ | No log | 1.0 | 132 | 0.4127 | 0.3852 | 0.6646 | 0.4877 | 0.9411 | 0.3875 | 0.4690 | 0.6 | 0.6338 | 0.6438 | 0.5806 | 0.3670 |
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+ | No log | 2.0 | 264 | 0.3424 | 0.3447 | 0.6972 | 0.4613 | 0.9353 | 0.3204 | 0.4103 | 0.5600 | 0.5691 | 0.5848 | 0.7027 | 0.3594 |
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+ | No log | 3.0 | 396 | 0.3942 | 0.4767 | 0.6850 | 0.5621 | 0.9534 | 0.5385 | 0.6500 | 0.7429 | 0.6583 | 0.6437 | 0.6111 | 0.3830 |
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+ | 0.4541 | 4.0 | 528 | 0.3542 | 0.4516 | 0.7012 | 0.5494 | 0.9503 | 0.4127 | 0.6552 | 0.5417 | 0.6068 | 0.6243 | 0.4762 | 0.4895 |
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+ | 0.4541 | 5.0 | 660 | 0.4092 | 0.5076 | 0.6829 | 0.5823 | 0.9560 | 0.3857 | 0.5827 | 0.6933 | 0.6866 | 0.7465 | 0.6875 | 0.4865 |
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+ | 0.4541 | 6.0 | 792 | 0.4450 | 0.4465 | 0.6870 | 0.5412 | 0.9491 | 0.3613 | 0.5985 | 0.6506 | 0.6278 | 0.6667 | 0.6857 | 0.4332 |
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+ | 0.4541 | 7.0 | 924 | 0.4487 | 0.4985 | 0.6707 | 0.5719 | 0.9552 | 0.4407 | 0.6400 | 0.5789 | 0.6590 | 0.6980 | 0.7429 | 0.4667 |
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+ | 0.1083 | 8.0 | 1056 | 0.4361 | 0.5068 | 0.6850 | 0.5825 | 0.9569 | 0.4553 | 0.6457 | 0.7429 | 0.6667 | 0.6887 | 0.6875 | 0.4536 |
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+ | 0.1083 | 9.0 | 1188 | 0.5592 | 0.4954 | 0.6504 | 0.5624 | 0.9549 | 0.4538 | 0.6552 | 0.6753 | 0.6397 | 0.6581 | 0.7647 | 0.4118 |
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+ | 0.1083 | 10.0 | 1320 | 0.5272 | 0.4686 | 0.6667 | 0.5503 | 0.9497 | 0.3816 | 0.6074 | 0.7 | 0.6340 | 0.7347 | 0.7429 | 0.3917 |
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+ | 0.1083 | 11.0 | 1452 | 0.6108 | 0.5412 | 0.6809 | 0.6031 | 0.9562 | 0.4727 | 0.6724 | 0.7222 | 0.6615 | 0.7097 | 0.6857 | 0.5027 |
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+ | 0.0491 | 12.0 | 1584 | 0.7836 | 0.5481 | 0.6138 | 0.5791 | 0.9546 | 0.5043 | 0.6446 | 0.7246 | 0.6286 | 0.7347 | 0.7273 | 0.4217 |
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+ | 0.0491 | 13.0 | 1716 | 0.5258 | 0.4838 | 0.6667 | 0.5607 | 0.9527 | 0.4580 | 0.6299 | 0.6944 | 0.6234 | 0.7089 | 0.6667 | 0.4060 |
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+ | 0.0491 | 14.0 | 1848 | 0.6477 | 0.5487 | 0.6301 | 0.5866 | 0.9576 | 0.4685 | 0.6909 | 0.7692 | 0.6312 | 0.6528 | 0.7273 | 0.4773 |
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+ | 0.0491 | 15.0 | 1980 | 0.5891 | 0.5359 | 0.6972 | 0.6060 | 0.9577 | 0.4865 | 0.6777 | 0.7123 | 0.6667 | 0.7114 | 0.6875 | 0.4986 |
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+ | 0.0288 | 16.0 | 2112 | 0.6913 | 0.5510 | 0.6809 | 0.6091 | 0.9575 | 0.5053 | 0.6783 | 0.7463 | 0.7063 | 0.6853 | 0.6842 | 0.4602 |
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+ | 0.0288 | 17.0 | 2244 | 0.7530 | 0.5425 | 0.6484 | 0.5907 | 0.9572 | 0.5149 | 0.6446 | 0.8065 | 0.6796 | 0.6993 | 0.75 | 0.3974 |
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+ | 0.0288 | 18.0 | 2376 | 0.7542 | 0.5815 | 0.6524 | 0.6149 | 0.9594 | 0.5306 | 0.6667 | 0.7353 | 0.6918 | 0.7077 | 0.7273 | 0.4706 |
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+ | 0.0137 | 19.0 | 2508 | 0.7550 | 0.5529 | 0.6585 | 0.6011 | 0.9561 | 0.5333 | 0.6957 | 0.6765 | 0.6508 | 0.7746 | 0.7059 | 0.4389 |
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+ | 0.0137 | 20.0 | 2640 | 0.6984 | 0.5335 | 0.6789 | 0.5975 | 0.9538 | 0.4828 | 0.6721 | 0.7353 | 0.6382 | 0.7518 | 0.6667 | 0.4731 |
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+ | 0.0137 | 21.0 | 2772 | 0.6706 | 0.5221 | 0.7215 | 0.6058 | 0.9511 | 0.4640 | 0.6780 | 0.72 | 0.6389 | 0.7355 | 0.6667 | 0.5215 |
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+ | 0.0137 | 22.0 | 2904 | 0.7129 | 0.5533 | 0.6646 | 0.6039 | 0.9561 | 0.5091 | 0.7 | 0.6667 | 0.6553 | 0.7761 | 0.6111 | 0.4673 |
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+ | 0.0096 | 23.0 | 3036 | 0.7137 | 0.5601 | 0.6728 | 0.6113 | 0.9583 | 0.5185 | 0.6780 | 0.7879 | 0.6621 | 0.7328 | 0.6 | 0.4926 |
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+ | 0.0096 | 24.0 | 3168 | 0.6871 | 0.5235 | 0.6789 | 0.5912 | 0.9534 | 0.4828 | 0.6891 | 0.6667 | 0.6414 | 0.7310 | 0.7273 | 0.4676 |
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+ | 0.0096 | 25.0 | 3300 | 0.7823 | 0.5641 | 0.6524 | 0.6051 | 0.9567 | 0.4628 | 0.7009 | 0.7576 | 0.6716 | 0.7183 | 0.6875 | 0.4762 |
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+ | 0.0096 | 26.0 | 3432 | 0.7905 | 0.5512 | 0.6565 | 0.5993 | 0.9556 | 0.5143 | 0.7368 | 0.7463 | 0.6332 | 0.7121 | 0.6875 | 0.4531 |
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+ | 0.0061 | 27.0 | 3564 | 0.8666 | 0.5557 | 0.6585 | 0.6028 | 0.9553 | 0.4779 | 0.7130 | 0.7692 | 0.6689 | 0.7391 | 0.6667 | 0.4465 |
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+ | 0.0061 | 28.0 | 3696 | 0.8848 | 0.5492 | 0.6240 | 0.5842 | 0.9552 | 0.4590 | 0.7273 | 0.7937 | 0.6227 | 0.7597 | 0.6250 | 0.4348 |
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  ### Framework versions