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hyenadna-large-1m-seqlen-hf_ft_BioS2_1kbpHG19_DHSs_H3K27AC

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hyenadna
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README.md CHANGED
@@ -19,13 +19,13 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [LongSafari/hyenadna-large-1m-seqlen-hf](https://huggingface.co/LongSafari/hyenadna-large-1m-seqlen-hf) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4403
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- - F1 Score: 0.8020
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- - Precision: 0.7872
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- - Recall: 0.8174
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- - Accuracy: 0.7899
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- - Auc: 0.8710
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- - Prc: 0.8688
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  ## Model description
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@@ -57,34 +57,38 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | F1 Score | Precision | Recall | Accuracy | Auc | Prc |
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  |:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
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- | 0.5974 | 0.0841 | 500 | 0.5532 | 0.7514 | 0.7140 | 0.7929 | 0.7268 | 0.7820 | 0.7532 |
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- | 0.548 | 0.1682 | 1000 | 0.5440 | 0.7522 | 0.7435 | 0.7612 | 0.7389 | 0.8019 | 0.7764 |
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- | 0.5384 | 0.2524 | 1500 | 0.5349 | 0.7348 | 0.7490 | 0.7212 | 0.7290 | 0.8046 | 0.7780 |
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- | 0.5211 | 0.3365 | 2000 | 0.5228 | 0.7703 | 0.7313 | 0.8136 | 0.7473 | 0.8126 | 0.7947 |
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- | 0.5283 | 0.4206 | 2500 | 0.5132 | 0.7711 | 0.7528 | 0.7903 | 0.7557 | 0.8283 | 0.8123 |
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- | 0.513 | 0.5047 | 3000 | 0.5455 | 0.7519 | 0.7735 | 0.7315 | 0.7487 | 0.8260 | 0.8167 |
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- | 0.4974 | 0.5888 | 3500 | 0.4916 | 0.7850 | 0.7520 | 0.8210 | 0.7658 | 0.8395 | 0.8269 |
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- | 0.5102 | 0.6729 | 4000 | 0.4899 | 0.7858 | 0.7352 | 0.8439 | 0.7604 | 0.8394 | 0.8260 |
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- | 0.497 | 0.7571 | 4500 | 0.4934 | 0.7792 | 0.7718 | 0.7868 | 0.7678 | 0.8463 | 0.8385 |
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- | 0.5044 | 0.8412 | 5000 | 0.4894 | 0.7978 | 0.7335 | 0.8743 | 0.7692 | 0.8454 | 0.8309 |
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- | 0.4777 | 0.9253 | 5500 | 0.5010 | 0.7994 | 0.7250 | 0.8908 | 0.7672 | 0.8541 | 0.8498 |
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- | 0.4764 | 1.0094 | 6000 | 0.4893 | 0.7942 | 0.7160 | 0.8918 | 0.7594 | 0.8425 | 0.8365 |
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- | 0.4748 | 1.0935 | 6500 | 0.4867 | 0.8039 | 0.7243 | 0.9031 | 0.7705 | 0.8575 | 0.8433 |
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- | 0.4662 | 1.1777 | 7000 | 0.4811 | 0.7836 | 0.7782 | 0.7890 | 0.7730 | 0.8610 | 0.8555 |
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- | 0.4594 | 1.2618 | 7500 | 0.4748 | 0.8010 | 0.7284 | 0.8898 | 0.7699 | 0.8545 | 0.8445 |
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- | 0.4687 | 1.3459 | 8000 | 0.4773 | 0.8039 | 0.7069 | 0.9318 | 0.7633 | 0.8585 | 0.8492 |
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- | 0.4567 | 1.4300 | 8500 | 0.4644 | 0.8057 | 0.7409 | 0.8830 | 0.7783 | 0.8621 | 0.8565 |
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- | 0.4645 | 1.5141 | 9000 | 0.4632 | 0.8067 | 0.7453 | 0.8792 | 0.7806 | 0.8620 | 0.8575 |
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- | 0.4809 | 1.5983 | 9500 | 0.4545 | 0.8045 | 0.7640 | 0.8494 | 0.7850 | 0.8660 | 0.8632 |
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- | 0.4475 | 1.6824 | 10000 | 0.4696 | 0.8041 | 0.7171 | 0.9150 | 0.7678 | 0.8642 | 0.8623 |
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- | 0.4478 | 1.7665 | 10500 | 0.4508 | 0.7951 | 0.7819 | 0.8087 | 0.7830 | 0.8703 | 0.8687 |
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- | 0.4292 | 1.8506 | 11000 | 0.4718 | 0.8070 | 0.7712 | 0.8462 | 0.7892 | 0.8661 | 0.8582 |
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- | 0.4519 | 1.9347 | 11500 | 0.4660 | 0.8129 | 0.7520 | 0.8847 | 0.7880 | 0.8702 | 0.8664 |
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- | 0.4526 | 2.0188 | 12000 | 0.4788 | 0.7648 | 0.8242 | 0.7134 | 0.7715 | 0.8696 | 0.8698 |
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- | 0.4364 | 2.1030 | 12500 | 0.4629 | 0.8053 | 0.7926 | 0.8184 | 0.7939 | 0.8722 | 0.8684 |
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- | 0.4275 | 2.1871 | 13000 | 0.4489 | 0.8106 | 0.7573 | 0.8721 | 0.7879 | 0.8710 | 0.8676 |
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- | 0.4314 | 2.2712 | 13500 | 0.4543 | 0.8108 | 0.7809 | 0.8430 | 0.7951 | 0.8761 | 0.8711 |
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- | 0.4339 | 2.3553 | 14000 | 0.4403 | 0.8020 | 0.7872 | 0.8174 | 0.7899 | 0.8710 | 0.8688 |
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [LongSafari/hyenadna-large-1m-seqlen-hf](https://huggingface.co/LongSafari/hyenadna-large-1m-seqlen-hf) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4430
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+ - F1 Score: 0.8169
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+ - Precision: 0.7917
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+ - Recall: 0.8438
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+ - Accuracy: 0.7974
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+ - Auc: 0.8771
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+ - Prc: 0.8787
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | F1 Score | Precision | Recall | Accuracy | Auc | Prc |
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  |:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
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+ | 0.598 | 0.0840 | 500 | 0.5571 | 0.7836 | 0.6895 | 0.9075 | 0.7316 | 0.7867 | 0.7596 |
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+ | 0.5439 | 0.1680 | 1000 | 0.5309 | 0.7878 | 0.7135 | 0.8793 | 0.7462 | 0.8020 | 0.7858 |
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+ | 0.5302 | 0.2519 | 1500 | 0.5200 | 0.7724 | 0.7550 | 0.7905 | 0.7504 | 0.8137 | 0.8022 |
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+ | 0.5293 | 0.3359 | 2000 | 0.5046 | 0.7929 | 0.7520 | 0.8385 | 0.7654 | 0.8256 | 0.8146 |
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+ | 0.51 | 0.4199 | 2500 | 0.5110 | 0.7957 | 0.7146 | 0.8975 | 0.7531 | 0.8244 | 0.8170 |
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+ | 0.5205 | 0.5039 | 3000 | 0.5003 | 0.7980 | 0.7232 | 0.8899 | 0.7586 | 0.8281 | 0.8179 |
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+ | 0.5153 | 0.5878 | 3500 | 0.5101 | 0.7715 | 0.7793 | 0.7639 | 0.7576 | 0.8388 | 0.8324 |
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+ | 0.5097 | 0.6718 | 4000 | 0.4971 | 0.7986 | 0.7410 | 0.8658 | 0.7660 | 0.8402 | 0.8344 |
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+ | 0.5164 | 0.7558 | 4500 | 0.4982 | 0.7728 | 0.7979 | 0.7491 | 0.7640 | 0.8468 | 0.8401 |
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+ | 0.4978 | 0.8398 | 5000 | 0.4807 | 0.8065 | 0.7440 | 0.8805 | 0.7738 | 0.8499 | 0.8437 |
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+ | 0.4744 | 0.9237 | 5500 | 0.4778 | 0.7867 | 0.7886 | 0.7849 | 0.7721 | 0.8501 | 0.8480 |
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+ | 0.4762 | 1.0077 | 6000 | 0.4849 | 0.8097 | 0.7311 | 0.9072 | 0.7716 | 0.8552 | 0.8519 |
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+ | 0.4707 | 1.0917 | 6500 | 0.4599 | 0.8096 | 0.7700 | 0.8536 | 0.7850 | 0.8602 | 0.8547 |
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+ | 0.4612 | 1.1757 | 7000 | 0.4724 | 0.7988 | 0.7954 | 0.8021 | 0.7835 | 0.8597 | 0.8588 |
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+ | 0.4654 | 1.2597 | 7500 | 0.4670 | 0.8079 | 0.7584 | 0.8642 | 0.7798 | 0.8620 | 0.8616 |
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+ | 0.456 | 1.3436 | 8000 | 0.4669 | 0.8108 | 0.7797 | 0.8445 | 0.7889 | 0.8645 | 0.8652 |
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+ | 0.4655 | 1.4276 | 8500 | 0.4679 | 0.7951 | 0.8026 | 0.7877 | 0.7825 | 0.8647 | 0.8664 |
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+ | 0.4447 | 1.5116 | 9000 | 0.4554 | 0.8156 | 0.7648 | 0.8736 | 0.7884 | 0.8650 | 0.8631 |
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+ | 0.4663 | 1.5956 | 9500 | 0.4563 | 0.8035 | 0.7986 | 0.8084 | 0.7882 | 0.8682 | 0.8680 |
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+ | 0.4526 | 1.6795 | 10000 | 0.4769 | 0.7937 | 0.8004 | 0.7871 | 0.7808 | 0.8682 | 0.8700 |
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+ | 0.4457 | 1.7635 | 10500 | 0.4533 | 0.7992 | 0.8009 | 0.7974 | 0.7854 | 0.8687 | 0.8698 |
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+ | 0.4391 | 1.8475 | 11000 | 0.4493 | 0.8186 | 0.7700 | 0.8736 | 0.7926 | 0.8705 | 0.8687 |
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+ | 0.4448 | 1.9315 | 11500 | 0.4461 | 0.8193 | 0.7696 | 0.8758 | 0.7931 | 0.8715 | 0.8737 |
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+ | 0.4359 | 2.0155 | 12000 | 0.4450 | 0.8178 | 0.7773 | 0.8627 | 0.7941 | 0.8736 | 0.8763 |
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+ | 0.4268 | 2.0994 | 12500 | 0.4724 | 0.7931 | 0.8190 | 0.7689 | 0.7852 | 0.8732 | 0.8746 |
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+ | 0.4254 | 2.1834 | 13000 | 0.4438 | 0.8198 | 0.7623 | 0.8868 | 0.7912 | 0.8756 | 0.8726 |
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+ | 0.4289 | 2.2674 | 13500 | 0.4556 | 0.8203 | 0.7503 | 0.9047 | 0.7877 | 0.8726 | 0.8706 |
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+ | 0.4315 | 2.3514 | 14000 | 0.4621 | 0.8060 | 0.7606 | 0.8570 | 0.7790 | 0.8647 | 0.8630 |
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+ | 0.4226 | 2.4353 | 14500 | 0.5973 | 0.7535 | 0.8545 | 0.6739 | 0.7639 | 0.8696 | 0.8723 |
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+ | 0.4331 | 2.5193 | 15000 | 0.4589 | 0.8198 | 0.7959 | 0.8451 | 0.8010 | 0.8762 | 0.8770 |
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+ | 0.4216 | 2.6033 | 15500 | 0.5026 | 0.7937 | 0.8263 | 0.7636 | 0.7874 | 0.8758 | 0.8783 |
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+ | 0.4449 | 2.6873 | 16000 | 0.4430 | 0.8169 | 0.7917 | 0.8438 | 0.7974 | 0.8771 | 0.8787 |
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  ### Framework versions
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