<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
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:
- Loss: 0.9998
- Precision: 0.4800
- Recall: 0.6452
- F1: 0.5505
- Accuracy: 0.8566
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:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
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
- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.12.1