generated_from_trainer

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scideberta-cs-finetuned-ner

This model is a fine-tuned version of KISTI-AI/scideberta-cs on the generator dataset. It achieves the following results on the evaluation set:

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

Training results

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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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

Framework versions