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:
- Loss: 0.9890
- Precision: 0.4656
- Recall: 0.6294
- F1: 0.5352
- Accuracy: 0.8600
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 | 1.1489 | 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
- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.12.1
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