generated_from_trainer

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clinico-xlm-roberta-large-finetuned-augmented1

This model is a fine-tuned version of joheras/xlm-roberta-base-finetuned-clinais on the None 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 Precision Recall F1 Accuracy
No log 1.0 179 0.6074 0.3003 0.5057 0.3768 0.8147
No log 2.0 358 0.5758 0.3091 0.5263 0.3895 0.8415
0.4921 3.0 537 0.7089 0.3357 0.5503 0.4170 0.8438
0.4921 4.0 716 0.8219 0.3541 0.5789 0.4394 0.8391
0.4921 5.0 895 0.8857 0.4050 0.5950 0.4819 0.8507
0.0628 6.0 1074 0.9386 0.3888 0.5961 0.4706 0.8485
0.0628 7.0 1253 1.0302 0.4072 0.6201 0.4916 0.8458
0.0628 8.0 1432 0.9960 0.4352 0.6144 0.5095 0.8535
0.0195 9.0 1611 1.0593 0.4349 0.6190 0.5109 0.8546
0.0195 10.0 1790 1.1262 0.4366 0.6190 0.5121 0.8512
0.0195 11.0 1969 1.1968 0.4675 0.6178 0.5323 0.8498
0.0085 12.0 2148 1.1940 0.4368 0.6167 0.5114 0.8472
0.0085 13.0 2327 1.1795 0.4670 0.6224 0.5336 0.8588
0.0043 14.0 2506 1.2596 0.4791 0.6281 0.5436 0.8534
0.0043 15.0 2685 1.2958 0.4580 0.6236 0.5281 0.8504
0.0043 16.0 2864 1.2891 0.4977 0.6270 0.5549 0.8533
0.0022 17.0 3043 1.2992 0.4615 0.6178 0.5284 0.8504
0.0022 18.0 3222 1.3102 0.4681 0.5961 0.5244 0.8549
0.0022 19.0 3401 1.2654 0.4485 0.6178 0.5197 0.8602
0.0027 20.0 3580 1.3123 0.4420 0.6064 0.5113 0.8479
0.0027 21.0 3759 1.3100 0.4877 0.6350 0.5517 0.8554
0.0027 22.0 3938 1.3696 0.4683 0.6247 0.5353 0.8532
0.0013 23.0 4117 1.3392 0.4851 0.5973 0.5354 0.8536
0.0013 24.0 4296 1.3680 0.4946 0.6270 0.5530 0.8509
0.0013 25.0 4475 1.4145 0.4914 0.6236 0.5497 0.8512
0.0014 26.0 4654 1.2962 0.5 0.6327 0.5586 0.8634
0.0014 27.0 4833 1.3871 0.4743 0.6327 0.5422 0.8532
0.0014 28.0 5012 1.3761 0.4982 0.6430 0.5614 0.8562
0.0014 29.0 5191 1.3929 0.4764 0.6224 0.5397 0.8501
0.0014 30.0 5370 1.3716 0.4862 0.6270 0.5477 0.8558
0.0006 31.0 5549 1.3881 0.4573 0.6247 0.5280 0.8566
0.0006 32.0 5728 1.3783 0.4688 0.6350 0.5394 0.8591
0.0006 33.0 5907 1.4211 0.5060 0.6293 0.5609 0.8585
0.001 34.0 6086 1.4034 0.4819 0.6236 0.5436 0.8566
0.001 35.0 6265 1.4026 0.5168 0.6339 0.5694 0.8592
0.001 36.0 6444 1.4153 0.4904 0.6419 0.5560 0.8586
0.0003 37.0 6623 1.4071 0.5161 0.6224 0.5643 0.8569
0.0003 38.0 6802 1.4308 0.5046 0.6293 0.5601 0.8562
0.0003 39.0 6981 1.4089 0.5083 0.6339 0.5642 0.8597
0.0001 40.0 7160 1.4522 0.5093 0.6293 0.5629 0.8543
0.0001 41.0 7339 1.4427 0.4964 0.6224 0.5523 0.8545
0.0001 42.0 7518 1.4641 0.4759 0.6213 0.5390 0.8513
0.0001 43.0 7697 1.4405 0.5014 0.6293 0.5581 0.8595
0.0001 44.0 7876 1.4584 0.5060 0.6281 0.5605 0.8555
0.0002 45.0 8055 1.4847 0.5157 0.6201 0.5631 0.8526
0.0002 46.0 8234 1.4545 0.4977 0.6270 0.5549 0.8577
0.0002 47.0 8413 1.4482 0.5106 0.6339 0.5656 0.8570
0.0001 48.0 8592 1.4639 0.5074 0.6281 0.5613 0.8563
0.0001 49.0 8771 1.4643 0.5088 0.6270 0.5618 0.8563
0.0001 50.0 8950 1.4632 0.5074 0.6293 0.5618 0.8560

Framework versions