generated_from_trainer

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clinico-bsc-bio-ehr-es-finetuned-v2

This model is a fine-tuned version of joheras/bsc-bio-ehr-es-finetuned-clinais-v2 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 25 1.2290 0.0070 0.0137 0.0093 0.5710
No log 2.0 50 0.7436 0.1540 0.1943 0.1718 0.7956
No log 3.0 75 0.6477 0.2407 0.3126 0.2719 0.8209
No log 4.0 100 0.6309 0.3249 0.4467 0.3762 0.8267
No log 5.0 125 0.6076 0.3437 0.4572 0.3924 0.8286
No log 6.0 150 0.6041 0.3445 0.4551 0.3922 0.8328
No log 7.0 175 0.5999 0.3242 0.4382 0.3727 0.8376
No log 8.0 200 0.6016 0.3551 0.5005 0.4154 0.8355
No log 9.0 225 0.6019 0.3628 0.5375 0.4332 0.8523
No log 10.0 250 0.6052 0.3340 0.5259 0.4085 0.8463
No log 11.0 275 0.6518 0.3405 0.5354 0.4163 0.8449
No log 12.0 300 0.6613 0.3851 0.5660 0.4583 0.8500
No log 13.0 325 0.6725 0.3737 0.5702 0.4515 0.8488
No log 14.0 350 0.6770 0.3889 0.5892 0.4685 0.8528
No log 15.0 375 0.6816 0.3742 0.5702 0.4519 0.8471
No log 16.0 400 0.6998 0.3830 0.5808 0.4616 0.8498
No log 17.0 425 0.7434 0.3789 0.5882 0.4609 0.8474
No log 18.0 450 0.7543 0.4176 0.6051 0.4942 0.8504
No log 19.0 475 0.7537 0.4196 0.6114 0.4976 0.8521
0.3481 20.0 500 0.7482 0.4102 0.6199 0.4937 0.8489
0.3481 21.0 525 0.7488 0.4203 0.6241 0.5023 0.8545
0.3481 22.0 550 0.7603 0.4097 0.6325 0.4973 0.8548
0.3481 23.0 575 0.7809 0.4333 0.6177 0.5094 0.8522
0.3481 24.0 600 0.7873 0.4317 0.6272 0.5114 0.8545
0.3481 25.0 625 0.7914 0.4368 0.6241 0.5139 0.8581
0.3481 26.0 650 0.8077 0.4508 0.6241 0.5235 0.8526
0.3481 27.0 675 0.8091 0.4639 0.6304 0.5345 0.8589
0.3481 28.0 700 0.8218 0.4543 0.6346 0.5295 0.8558
0.3481 29.0 725 0.8316 0.4522 0.6241 0.5244 0.8545
0.3481 30.0 750 0.8444 0.4434 0.6325 0.5213 0.8535
0.3481 31.0 775 0.8208 0.4506 0.6209 0.5222 0.8559
0.3481 32.0 800 0.8425 0.4572 0.6262 0.5285 0.8554
0.3481 33.0 825 0.8872 0.4453 0.6272 0.5208 0.8494
0.3481 34.0 850 0.8533 0.4635 0.6167 0.5292 0.8579
0.3481 35.0 875 0.8927 0.4529 0.6188 0.5230 0.8532
0.3481 36.0 900 0.8919 0.4659 0.6199 0.5319 0.8517
0.3481 37.0 925 0.8971 0.4480 0.6272 0.5227 0.8544
0.3481 38.0 950 0.9032 0.4708 0.6378 0.5417 0.8541
0.3481 39.0 975 0.8805 0.4449 0.6315 0.5220 0.8550
0.0239 40.0 1000 0.8927 0.4580 0.6283 0.5298 0.8567
0.0239 41.0 1025 0.9016 0.4906 0.6315 0.5522 0.8587
0.0239 42.0 1050 0.9078 0.4691 0.6336 0.5391 0.8572
0.0239 43.0 1075 0.9275 0.4748 0.6272 0.5405 0.8534
0.0239 44.0 1100 0.9360 0.4559 0.6272 0.528 0.8508
0.0239 45.0 1125 0.9357 0.4645 0.6357 0.5368 0.8568
0.0239 46.0 1150 0.9359 0.4730 0.6294 0.5401 0.8541
0.0239 47.0 1175 0.9388 0.4730 0.6294 0.5401 0.8550
0.0239 48.0 1200 0.9322 0.4832 0.6389 0.5503 0.8593
0.0239 49.0 1225 0.9519 0.4755 0.6262 0.5406 0.8543
0.0239 50.0 1250 0.9503 0.4674 0.6283 0.5360 0.8515
0.0239 51.0 1275 0.9547 0.4773 0.6336 0.5445 0.8520
0.0239 52.0 1300 0.9460 0.4767 0.6367 0.5452 0.8550
0.0239 53.0 1325 0.9537 0.4704 0.6283 0.5380 0.8552
0.0239 54.0 1350 0.9485 0.4759 0.6367 0.5447 0.8553
0.0239 55.0 1375 0.9503 0.4737 0.6367 0.5432 0.8570
0.0239 56.0 1400 0.9585 0.4890 0.6346 0.5524 0.8542
0.0239 57.0 1425 0.9532 0.4864 0.6420 0.5535 0.8572
0.0239 58.0 1450 0.9555 0.4734 0.6399 0.5442 0.8578
0.0239 59.0 1475 0.9683 0.4704 0.6304 0.5388 0.8517
0.009 60.0 1500 0.9736 0.4755 0.6357 0.5441 0.8547
0.009 61.0 1525 0.9656 0.4871 0.6399 0.5532 0.8553
0.009 62.0 1550 0.9735 0.4705 0.6315 0.5392 0.8531
0.009 63.0 1575 0.9700 0.4757 0.6420 0.5465 0.8568
0.009 64.0 1600 0.9749 0.4908 0.6452 0.5575 0.8539
0.009 65.0 1625 0.9815 0.5021 0.6389 0.5623 0.8536
0.009 66.0 1650 0.9829 0.4895 0.6399 0.5547 0.8553
0.009 67.0 1675 0.9950 0.4845 0.6420 0.5522 0.8537
0.009 68.0 1700 0.9822 0.5033 0.6452 0.5655 0.8566
0.009 69.0 1725 0.9747 0.5037 0.6526 0.5685 0.8594
0.009 70.0 1750 0.9825 0.4841 0.6410 0.5516 0.8560
0.009 71.0 1775 0.9839 0.4936 0.6484 0.5605 0.8561
0.009 72.0 1800 0.9855 0.4873 0.6494 0.5568 0.8570
0.009 73.0 1825 0.9845 0.4924 0.6494 0.5601 0.8567
0.009 74.0 1850 0.9842 0.4880 0.6463 0.5561 0.8561
0.009 75.0 1875 0.9892 0.4853 0.6441 0.5535 0.8559
0.009 76.0 1900 0.9899 0.4865 0.6463 0.5551 0.8568
0.009 77.0 1925 0.9861 0.4947 0.6420 0.5588 0.8582
0.009 78.0 1950 0.9875 0.4774 0.6473 0.5495 0.8596
0.009 79.0 1975 0.9827 0.5033 0.6473 0.5663 0.8600
0.0056 80.0 2000 0.9876 0.4734 0.6473 0.5468 0.8602
0.0056 81.0 2025 0.9957 0.4834 0.6463 0.5531 0.8574
0.0056 82.0 2050 0.9883 0.4766 0.6463 0.5486 0.8569
0.0056 83.0 2075 0.9904 0.4903 0.6420 0.5560 0.8567
0.0056 84.0 2100 0.9920 0.4829 0.6410 0.5508 0.8572
0.0056 85.0 2125 0.9817 0.4947 0.6463 0.5604 0.8600
0.0056 86.0 2150 0.9837 0.4900 0.6484 0.5582 0.8601
0.0056 87.0 2175 0.9908 0.4852 0.6420 0.5527 0.8572
0.0056 88.0 2200 0.9873 0.5049 0.6484 0.5677 0.8590
0.0056 89.0 2225 0.9924 0.4843 0.6367 0.5502 0.8560
0.0056 90.0 2250 0.9897 0.4794 0.6378 0.5473 0.8574
0.0056 91.0 2275 0.9956 0.4779 0.6389 0.5468 0.8569
0.0056 92.0 2300 1.0018 0.4829 0.6410 0.5508 0.8567
0.0056 93.0 2325 1.0021 0.4753 0.6410 0.5459 0.8562
0.0056 94.0 2350 0.9968 0.4817 0.6410 0.5501 0.8575
0.0056 95.0 2375 0.9940 0.4799 0.6441 0.5500 0.8584
0.0056 96.0 2400 0.9964 0.4780 0.6420 0.5480 0.8577
0.0056 97.0 2425 1.0001 0.4785 0.6463 0.5499 0.8578
0.0056 98.0 2450 0.9989 0.4797 0.6473 0.5510 0.8582
0.0056 99.0 2475 0.9995 0.4762 0.6452 0.5480 0.8572
0.0043 100.0 2500 0.9998 0.4800 0.6452 0.5505 0.8566

Framework versions