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ner-2

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update model card README.md

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@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1791
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- - Precision: 0.5224
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- - Recall: 0.6222
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- - F1: 0.5680
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- - Accuracy: 0.9631
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  ## Model description
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@@ -54,36 +54,36 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 29 | 0.2584 | 0.0 | 0.0 | 0.0 | 0.9365 |
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- | No log | 2.0 | 58 | 0.2386 | 0.1364 | 0.0133 | 0.0243 | 0.9458 |
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- | No log | 3.0 | 87 | 0.2312 | 0.2368 | 0.04 | 0.0684 | 0.9466 |
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- | No log | 4.0 | 116 | 0.1806 | 0.2809 | 0.2222 | 0.2481 | 0.9422 |
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- | No log | 5.0 | 145 | 0.1446 | 0.4453 | 0.2711 | 0.3370 | 0.9558 |
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- | No log | 6.0 | 174 | 0.1575 | 0.3778 | 0.3022 | 0.3358 | 0.9493 |
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- | No log | 7.0 | 203 | 0.1255 | 0.5081 | 0.4178 | 0.4585 | 0.9601 |
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- | No log | 8.0 | 232 | 0.1290 | 0.4599 | 0.4844 | 0.4719 | 0.9596 |
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- | No log | 9.0 | 261 | 0.1383 | 0.4844 | 0.4844 | 0.4844 | 0.9597 |
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- | No log | 10.0 | 290 | 0.1534 | 0.4313 | 0.6133 | 0.5064 | 0.9519 |
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- | No log | 11.0 | 319 | 0.1575 | 0.4423 | 0.6133 | 0.5140 | 0.9560 |
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- | No log | 12.0 | 348 | 0.1437 | 0.5888 | 0.5156 | 0.5498 | 0.9670 |
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- | No log | 13.0 | 377 | 0.1605 | 0.5 | 0.5911 | 0.5418 | 0.9589 |
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- | No log | 14.0 | 406 | 0.1529 | 0.5459 | 0.5289 | 0.5372 | 0.9640 |
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- | No log | 15.0 | 435 | 0.1569 | 0.5097 | 0.5867 | 0.5455 | 0.9618 |
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- | No log | 16.0 | 464 | 0.1656 | 0.4980 | 0.5644 | 0.5292 | 0.9607 |
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- | No log | 17.0 | 493 | 0.1602 | 0.5583 | 0.5956 | 0.5763 | 0.9622 |
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- | 0.0843 | 18.0 | 522 | 0.1767 | 0.4897 | 0.6356 | 0.5532 | 0.9589 |
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- | 0.0843 | 19.0 | 551 | 0.1642 | 0.5551 | 0.6044 | 0.5787 | 0.9641 |
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- | 0.0843 | 20.0 | 580 | 0.1635 | 0.6418 | 0.5733 | 0.6056 | 0.9679 |
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- | 0.0843 | 21.0 | 609 | 0.1706 | 0.5423 | 0.6267 | 0.5814 | 0.9635 |
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- | 0.0843 | 22.0 | 638 | 0.1691 | 0.5437 | 0.6089 | 0.5744 | 0.9638 |
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- | 0.0843 | 23.0 | 667 | 0.1743 | 0.5357 | 0.6 | 0.5660 | 0.9631 |
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- | 0.0843 | 24.0 | 696 | 0.1800 | 0.5176 | 0.6533 | 0.5776 | 0.9627 |
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- | 0.0843 | 25.0 | 725 | 0.1789 | 0.5 | 0.6 | 0.5455 | 0.9620 |
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- | 0.0843 | 26.0 | 754 | 0.1754 | 0.5388 | 0.5867 | 0.5617 | 0.9638 |
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- | 0.0843 | 27.0 | 783 | 0.1797 | 0.5164 | 0.6311 | 0.5680 | 0.9627 |
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- | 0.0843 | 28.0 | 812 | 0.1816 | 0.5321 | 0.6267 | 0.5755 | 0.9633 |
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- | 0.0843 | 29.0 | 841 | 0.1793 | 0.5222 | 0.6267 | 0.5697 | 0.9631 |
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- | 0.0843 | 30.0 | 870 | 0.1791 | 0.5224 | 0.6222 | 0.5680 | 0.9631 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1804
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+ - Precision: 0.6443
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+ - Recall: 0.5708
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+ - F1: 0.6053
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+ - Accuracy: 0.9691
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 29 | 0.2727 | 0.0 | 0.0 | 0.0 | 0.9392 |
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+ | No log | 2.0 | 58 | 0.2246 | 0.1163 | 0.0228 | 0.0382 | 0.9383 |
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+ | No log | 3.0 | 87 | 0.1744 | 0.3718 | 0.1324 | 0.1953 | 0.9480 |
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+ | No log | 4.0 | 116 | 0.1492 | 0.4734 | 0.3653 | 0.4124 | 0.9569 |
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+ | No log | 5.0 | 145 | 0.1472 | 0.4905 | 0.4703 | 0.4802 | 0.9581 |
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+ | No log | 6.0 | 174 | 0.1320 | 0.5403 | 0.5205 | 0.5302 | 0.9618 |
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+ | No log | 7.0 | 203 | 0.1423 | 0.5922 | 0.5571 | 0.5741 | 0.9667 |
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+ | No log | 8.0 | 232 | 0.1616 | 0.5838 | 0.5251 | 0.5529 | 0.9648 |
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+ | No log | 9.0 | 261 | 0.1443 | 0.6082 | 0.5388 | 0.5714 | 0.9676 |
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+ | No log | 10.0 | 290 | 0.1681 | 0.5990 | 0.5662 | 0.5822 | 0.9654 |
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+ | No log | 11.0 | 319 | 0.1611 | 0.4853 | 0.6027 | 0.5377 | 0.9599 |
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+ | No log | 12.0 | 348 | 0.1751 | 0.4887 | 0.5936 | 0.5361 | 0.9588 |
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+ | No log | 13.0 | 377 | 0.1796 | 0.4819 | 0.6073 | 0.5374 | 0.9593 |
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+ | No log | 14.0 | 406 | 0.1609 | 0.6760 | 0.5525 | 0.6080 | 0.9699 |
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+ | No log | 15.0 | 435 | 0.1821 | 0.5136 | 0.6027 | 0.5546 | 0.9606 |
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+ | No log | 16.0 | 464 | 0.1581 | 0.6462 | 0.5753 | 0.6087 | 0.9691 |
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+ | No log | 17.0 | 493 | 0.1582 | 0.6531 | 0.5845 | 0.6169 | 0.9692 |
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+ | 0.0763 | 18.0 | 522 | 0.1641 | 0.5574 | 0.6210 | 0.5875 | 0.9648 |
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+ | 0.0763 | 19.0 | 551 | 0.1681 | 0.5671 | 0.5982 | 0.5822 | 0.9663 |
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+ | 0.0763 | 20.0 | 580 | 0.1710 | 0.5917 | 0.5890 | 0.5904 | 0.9667 |
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+ | 0.0763 | 21.0 | 609 | 0.1794 | 0.6703 | 0.5662 | 0.6139 | 0.9702 |
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+ | 0.0763 | 22.0 | 638 | 0.1759 | 0.6103 | 0.5936 | 0.6019 | 0.9672 |
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+ | 0.0763 | 23.0 | 667 | 0.1762 | 0.6298 | 0.5982 | 0.6136 | 0.9687 |
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+ | 0.0763 | 24.0 | 696 | 0.1811 | 0.6176 | 0.5753 | 0.5957 | 0.9681 |
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+ | 0.0763 | 25.0 | 725 | 0.1793 | 0.6337 | 0.5845 | 0.6081 | 0.9696 |
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+ | 0.0763 | 26.0 | 754 | 0.1794 | 0.6796 | 0.5616 | 0.615 | 0.9702 |
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+ | 0.0763 | 27.0 | 783 | 0.1776 | 0.6293 | 0.5890 | 0.6085 | 0.9692 |
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+ | 0.0763 | 28.0 | 812 | 0.1796 | 0.6443 | 0.5708 | 0.6053 | 0.9694 |
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+ | 0.0763 | 29.0 | 841 | 0.1803 | 0.6410 | 0.5708 | 0.6039 | 0.9692 |
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+ | 0.0763 | 30.0 | 870 | 0.1804 | 0.6443 | 0.5708 | 0.6053 | 0.9691 |
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  ### Framework versions