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nucleotide-transformer-2.5b-1000g_ft_Hepg2_1kbpHG19_DHSs_H3K27AC

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  1. README.md +50 -9
  2. model-00001-of-00003.safetensors +1 -1
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README.md CHANGED
@@ -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 [InstaDeepAI/nucleotide-transformer-2.5b-1000g](https://huggingface.co/InstaDeepAI/nucleotide-transformer-2.5b-1000g) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4911
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- - F1 Score: 0.8530
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- - Precision: 0.9084
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- - Recall: 0.8041
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- - Accuracy: 0.8470
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  ## Model description
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@@ -48,14 +48,55 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 3
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  - mixed_precision_training: Native AMP
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | F1 Score | Precision | Recall | Accuracy |
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- |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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- | 0.3896 | 1.8657 | 500 | 0.4911 | 0.8530 | 0.9084 | 0.8041 | 0.8470 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-2.5b-1000g](https://huggingface.co/InstaDeepAI/nucleotide-transformer-2.5b-1000g) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2831
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+ - F1 Score: 0.8768
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+ - Precision: 0.8600
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+ - Recall: 0.8943
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+ - Accuracy: 0.8661
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 20
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  - mixed_precision_training: Native AMP
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | F1 Score | Precision | Recall | Accuracy |
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+ |:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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+ | 0.45 | 0.1864 | 500 | 0.4118 | 0.8434 | 0.8428 | 0.8439 | 0.8329 |
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+ | 0.3862 | 0.3729 | 1000 | 0.3639 | 0.8621 | 0.8498 | 0.8747 | 0.8508 |
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+ | 0.3975 | 0.5593 | 1500 | 0.3904 | 0.8690 | 0.8097 | 0.9377 | 0.8493 |
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+ | 0.3681 | 0.7457 | 2000 | 0.3590 | 0.8738 | 0.8395 | 0.9111 | 0.8598 |
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+ | 0.3567 | 0.9321 | 2500 | 0.4015 | 0.8733 | 0.8063 | 0.9524 | 0.8527 |
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+ | 0.2808 | 1.1186 | 3000 | 0.6744 | 0.8251 | 0.9155 | 0.7509 | 0.8303 |
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+ | 0.2164 | 1.3050 | 3500 | 0.5770 | 0.8747 | 0.8233 | 0.9328 | 0.8575 |
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+ | 0.2104 | 1.4914 | 4000 | 0.5941 | 0.8817 | 0.8416 | 0.9258 | 0.8676 |
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+ | 0.2115 | 1.6779 | 4500 | 0.5546 | 0.8755 | 0.8542 | 0.8978 | 0.8639 |
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+ | 0.2075 | 1.8643 | 5000 | 0.5431 | 0.8701 | 0.8754 | 0.8649 | 0.8624 |
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+ | 0.176 | 2.0507 | 5500 | 0.9443 | 0.8696 | 0.8666 | 0.8726 | 0.8605 |
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+ | 0.0508 | 2.2371 | 6000 | 0.8301 | 0.8777 | 0.8743 | 0.8810 | 0.8691 |
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+ | 0.0488 | 2.4236 | 6500 | 0.8394 | 0.8783 | 0.8584 | 0.8992 | 0.8672 |
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+ | 0.0567 | 2.6100 | 7000 | 1.1510 | 0.8785 | 0.8122 | 0.9566 | 0.8590 |
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+ | 0.0588 | 2.7964 | 7500 | 0.8502 | 0.8568 | 0.8912 | 0.8251 | 0.8530 |
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+ | 0.0541 | 2.9828 | 8000 | 1.4982 | 0.8013 | 0.9155 | 0.7124 | 0.8116 |
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+ | 0.0119 | 3.1693 | 8500 | 1.2710 | 0.8699 | 0.8838 | 0.8565 | 0.8635 |
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+ | 0.019 | 3.3557 | 9000 | 1.1534 | 0.8753 | 0.8663 | 0.8845 | 0.8657 |
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+ | 0.0267 | 3.5421 | 9500 | 1.3990 | 0.8719 | 0.7909 | 0.9713 | 0.8478 |
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+ | 0.0318 | 3.7286 | 10000 | 1.3099 | 0.8697 | 0.8444 | 0.8964 | 0.8568 |
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+ | 0.0562 | 3.9150 | 10500 | 0.8772 | 0.8662 | 0.8739 | 0.8586 | 0.8586 |
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+ | 0.0231 | 4.1014 | 11000 | 1.1277 | 0.8851 | 0.8398 | 0.9356 | 0.8706 |
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+ | 0.0281 | 4.2878 | 11500 | 1.0566 | 0.8828 | 0.8442 | 0.9251 | 0.8691 |
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+ | 0.0149 | 4.4743 | 12000 | 1.2550 | 0.8705 | 0.8485 | 0.8936 | 0.8583 |
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+ | 0.0278 | 4.6607 | 12500 | 1.1960 | 0.8279 | 0.8984 | 0.7677 | 0.8299 |
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+ | 0.0265 | 4.8471 | 13000 | 1.2293 | 0.8588 | 0.8826 | 0.8362 | 0.8534 |
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+ | 0.0319 | 5.0336 | 13500 | 1.1519 | 0.8838 | 0.8663 | 0.9020 | 0.8736 |
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+ | 0.0226 | 5.2200 | 14000 | 1.1934 | 0.8803 | 0.8609 | 0.9006 | 0.8695 |
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+ | 0.0127 | 5.4064 | 14500 | 1.3537 | 0.8854 | 0.8404 | 0.9356 | 0.8709 |
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+ | 0.0299 | 5.5928 | 15000 | 1.1620 | 0.8514 | 0.8926 | 0.8139 | 0.8486 |
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+ | 0.0259 | 5.7793 | 15500 | 1.2213 | 0.8827 | 0.8360 | 0.9349 | 0.8676 |
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+ | 0.0236 | 5.9657 | 16000 | 1.1988 | 0.8777 | 0.8552 | 0.9013 | 0.8661 |
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+ | 0.013 | 6.1521 | 16500 | 1.4039 | 0.8788 | 0.8511 | 0.9083 | 0.8665 |
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+ | 0.0174 | 6.3386 | 17000 | 1.1774 | 0.8787 | 0.8565 | 0.9020 | 0.8672 |
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+ | 0.0197 | 6.5250 | 17500 | 1.3383 | 0.8732 | 0.8536 | 0.8936 | 0.8616 |
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+ | 0.0185 | 6.7114 | 18000 | 1.1582 | 0.8679 | 0.8694 | 0.8663 | 0.8594 |
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+ | 0.0136 | 6.8978 | 18500 | 1.3086 | 0.8532 | 0.8923 | 0.8174 | 0.8501 |
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+ | 0.0072 | 7.0843 | 19000 | 1.2864 | 0.8733 | 0.8643 | 0.8824 | 0.8635 |
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+ | 0.0031 | 7.2707 | 19500 | 1.4482 | 0.8729 | 0.8616 | 0.8845 | 0.8627 |
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+ | 0.0202 | 7.4571 | 20000 | 1.4021 | 0.8721 | 0.8573 | 0.8873 | 0.8612 |
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+ | 0.021 | 7.6435 | 20500 | 1.2587 | 0.8807 | 0.8493 | 0.9146 | 0.8680 |
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+ | 0.0097 | 7.8300 | 21000 | 1.2831 | 0.8768 | 0.8600 | 0.8943 | 0.8661 |
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  ### Framework versions
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