generated_from_trainer

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clinico-bsc-bio-ehr-es-finetuned

This model is a fine-tuned version of joheras/bsc-bio-ehr-es-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 25 1.1489 0.0399 0.0623 0.0486 0.6528
No log 2.0 50 0.7106 0.1739 0.2070 0.1890 0.8063
No log 3.0 75 0.6518 0.2380 0.2724 0.2541 0.8202
No log 4.0 100 0.6281 0.3515 0.4562 0.3971 0.8288
No log 5.0 125 0.6029 0.3424 0.4372 0.3840 0.8357
No log 6.0 150 0.5911 0.3479 0.4446 0.3904 0.8344
No log 7.0 175 0.5809 0.3363 0.4382 0.3806 0.8429
No log 8.0 200 0.5792 0.3338 0.4593 0.3867 0.8442
No log 9.0 225 0.5980 0.3465 0.4984 0.4088 0.8531
No log 10.0 250 0.6144 0.3700 0.5333 0.4369 0.8511
No log 11.0 275 0.6376 0.3634 0.5280 0.4305 0.8445
No log 12.0 300 0.6668 0.3802 0.5396 0.4461 0.8454
No log 13.0 325 0.6618 0.3957 0.5692 0.4669 0.8506
No log 14.0 350 0.6622 0.3906 0.5713 0.4640 0.8563
No log 15.0 375 0.6637 0.4241 0.5808 0.4902 0.8565
No log 16.0 400 0.6884 0.4251 0.5903 0.4943 0.8594
No log 17.0 425 0.7183 0.4213 0.6051 0.4967 0.8517
No log 18.0 450 0.7387 0.4104 0.5977 0.4867 0.8545
No log 19.0 475 0.7256 0.4261 0.5998 0.4982 0.8563
0.346 20.0 500 0.7427 0.4178 0.6040 0.4940 0.8534
0.346 21.0 525 0.7562 0.4240 0.6008 0.4972 0.8569
0.346 22.0 550 0.7590 0.4038 0.6051 0.4844 0.8517
0.346 23.0 575 0.7677 0.4163 0.6146 0.4964 0.8573
0.346 24.0 600 0.8148 0.4127 0.6040 0.4904 0.8483
0.346 25.0 625 0.7992 0.4230 0.6030 0.4972 0.8533
0.346 26.0 650 0.8156 0.4203 0.6072 0.4968 0.8537
0.346 27.0 675 0.7999 0.4356 0.6103 0.5084 0.8562
0.346 28.0 700 0.8326 0.4379 0.6146 0.5114 0.8508
0.346 29.0 725 0.8394 0.4441 0.6209 0.5178 0.8542
0.346 30.0 750 0.8414 0.4373 0.6072 0.5084 0.8535
0.346 31.0 775 0.8363 0.4394 0.6082 0.5102 0.8566
0.346 32.0 800 0.8442 0.4536 0.6188 0.5234 0.8574
0.346 33.0 825 0.8470 0.4655 0.6199 0.5317 0.8608
0.346 34.0 850 0.8323 0.4647 0.6177 0.5304 0.8587
0.346 35.0 875 0.8590 0.4495 0.6199 0.5211 0.8573
0.346 36.0 900 0.8457 0.4542 0.6230 0.5254 0.8589
0.346 37.0 925 0.8720 0.4543 0.6251 0.5262 0.8552
0.346 38.0 950 0.8736 0.4562 0.6167 0.5245 0.8562
0.346 39.0 975 0.8710 0.4384 0.6199 0.5136 0.8543
0.0257 40.0 1000 0.8805 0.4416 0.6230 0.5169 0.8569
0.0257 41.0 1025 0.8963 0.4634 0.6209 0.5307 0.8536
0.0257 42.0 1050 0.8973 0.4619 0.6146 0.5274 0.8546
0.0257 43.0 1075 0.9123 0.4733 0.6188 0.5364 0.8571
0.0257 44.0 1100 0.9169 0.4570 0.6230 0.5273 0.8532
0.0257 45.0 1125 0.9094 0.4847 0.6357 0.5500 0.8592
0.0257 46.0 1150 0.9096 0.4761 0.6304 0.5425 0.8611
0.0257 47.0 1175 0.9074 0.4622 0.6262 0.5318 0.8590
0.0257 48.0 1200 0.9087 0.4536 0.6199 0.5239 0.8582
0.0257 49.0 1225 0.9412 0.4426 0.6103 0.5131 0.8580
0.0257 50.0 1250 0.9221 0.4435 0.6262 0.5193 0.8587
0.0257 51.0 1275 0.9232 0.4608 0.6199 0.5286 0.8578
0.0257 52.0 1300 0.9313 0.4696 0.6199 0.5344 0.8592
0.0257 53.0 1325 0.9340 0.4529 0.6241 0.5249 0.8603
0.0257 54.0 1350 0.9418 0.4599 0.6241 0.5296 0.8561
0.0257 55.0 1375 0.9428 0.4608 0.6146 0.5267 0.8579
0.0257 56.0 1400 0.9386 0.4728 0.6230 0.5376 0.8608
0.0257 57.0 1425 0.9467 0.4641 0.6209 0.5312 0.8579
0.0257 58.0 1450 0.9402 0.4639 0.6167 0.5295 0.8614
0.0257 59.0 1475 0.9389 0.4667 0.6220 0.5333 0.8601
0.0095 60.0 1500 0.9363 0.4633 0.6262 0.5326 0.8597
0.0095 61.0 1525 0.9302 0.4706 0.6251 0.5370 0.8604
0.0095 62.0 1550 0.9456 0.4707 0.6272 0.5378 0.8609
0.0095 63.0 1575 0.9470 0.4700 0.6283 0.5377 0.8602
0.0095 64.0 1600 0.9706 0.4609 0.6230 0.5299 0.8562
0.0095 65.0 1625 0.9710 0.4785 0.6230 0.5413 0.8567
0.0095 66.0 1650 0.9715 0.4806 0.6283 0.5446 0.8568
0.0095 67.0 1675 0.9638 0.4621 0.6177 0.5287 0.8586
0.0095 68.0 1700 0.9750 0.4754 0.6230 0.5393 0.8568
0.0095 69.0 1725 0.9856 0.4643 0.6251 0.5329 0.8554
0.0095 70.0 1750 0.9855 0.4512 0.6199 0.5222 0.8570
0.0095 71.0 1775 0.9811 0.4756 0.6272 0.5410 0.8563
0.0095 72.0 1800 0.9858 0.4679 0.6167 0.5321 0.8569
0.0095 73.0 1825 0.9794 0.4676 0.6241 0.5346 0.8580
0.0095 74.0 1850 0.9774 0.4772 0.6199 0.5393 0.8572
0.0095 75.0 1875 0.9772 0.4810 0.6272 0.5445 0.8580
0.0095 76.0 1900 0.9805 0.4757 0.6294 0.5418 0.8584
0.0095 77.0 1925 0.9782 0.4782 0.6251 0.5419 0.8585
0.0095 78.0 1950 0.9921 0.4731 0.6315 0.5409 0.8572
0.0095 79.0 1975 0.9797 0.4684 0.6188 0.5332 0.8599
0.0057 80.0 2000 0.9844 0.4747 0.6251 0.5397 0.8585
0.0057 81.0 2025 0.9824 0.4636 0.6251 0.5324 0.8579
0.0057 82.0 2050 0.9803 0.4765 0.6220 0.5396 0.8591
0.0057 83.0 2075 0.9834 0.4742 0.6304 0.5413 0.8607
0.0057 84.0 2100 0.9897 0.4727 0.6315 0.5407 0.8585
0.0057 85.0 2125 0.9835 0.4723 0.6220 0.5369 0.8573
0.0057 86.0 2150 0.9838 0.4773 0.6230 0.5405 0.8580
0.0057 87.0 2175 0.9879 0.4663 0.6220 0.5330 0.8579
0.0057 88.0 2200 0.9844 0.4806 0.6262 0.5438 0.8595
0.0057 89.0 2225 0.9903 0.4767 0.6262 0.5413 0.8588
0.0057 90.0 2250 0.9929 0.4806 0.6283 0.5446 0.8581
0.0057 91.0 2275 0.9947 0.4873 0.6294 0.5493 0.8576
0.0057 92.0 2300 0.9888 0.4713 0.6251 0.5374 0.8581
0.0057 93.0 2325 0.9869 0.4780 0.6315 0.5441 0.8586
0.0057 94.0 2350 0.9866 0.4807 0.6315 0.5459 0.8587
0.0057 95.0 2375 0.9892 0.4850 0.6315 0.5486 0.8587
0.0057 96.0 2400 0.9891 0.4735 0.6325 0.5416 0.8589
0.0057 97.0 2425 0.9883 0.4635 0.6294 0.5338 0.8593
0.0057 98.0 2450 0.9883 0.4670 0.6283 0.5358 0.8601
0.0057 99.0 2475 0.9889 0.4671 0.6304 0.5366 0.8601
0.0045 100.0 2500 0.9890 0.4656 0.6294 0.5352 0.8600

Framework versions