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nucleotide-transformer-v2-500m-multi-species_ft_BioS2_1kbpHG19_DHSs_H3K27AC

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  2. model.safetensors +1 -1
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 [InstaDeepAI/nucleotide-transformer-v2-500m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-500m-multi-species) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3527
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- - F1 Score: 0.8698
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- - Precision: 0.9037
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- - Recall: 0.8383
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- - Accuracy: 0.8672
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- - Auc: 0.9454
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- - Prc: 0.9446
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  ## Model description
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@@ -57,13 +57,19 @@ 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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- | 0.5177 | 0.1681 | 500 | 0.4434 | 0.8222 | 0.7423 | 0.9215 | 0.7892 | 0.8793 | 0.8778 |
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- | 0.4087 | 0.3362 | 1000 | 0.4053 | 0.8076 | 0.9103 | 0.7258 | 0.8171 | 0.9266 | 0.9251 |
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- | 0.3606 | 0.5044 | 1500 | 0.3174 | 0.8775 | 0.8766 | 0.8783 | 0.8702 | 0.9382 | 0.9345 |
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- | 0.3316 | 0.6725 | 2000 | 0.3042 | 0.8855 | 0.8552 | 0.9180 | 0.8744 | 0.9427 | 0.9415 |
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- | 0.3342 | 0.8406 | 2500 | 0.2957 | 0.8846 | 0.8799 | 0.8894 | 0.8773 | 0.9473 | 0.9471 |
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- | 0.3046 | 1.0087 | 3000 | 0.4441 | 0.8379 | 0.9329 | 0.7604 | 0.8443 | 0.9440 | 0.9453 |
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- | 0.2391 | 1.1769 | 3500 | 0.3527 | 0.8698 | 0.9037 | 0.8383 | 0.8672 | 0.9454 | 0.9446 |
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-500m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-500m-multi-species) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.0128
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+ - F1 Score: 0.8753
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+ - Precision: 0.8186
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+ - Recall: 0.9403
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+ - Accuracy: 0.8603
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+ - Auc: 0.9383
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+ - Prc: 0.9326
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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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+ | 0.534 | 0.1681 | 500 | 0.4570 | 0.7800 | 0.8228 | 0.7414 | 0.7820 | 0.8740 | 0.8666 |
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+ | 0.3972 | 0.3361 | 1000 | 0.3836 | 0.8574 | 0.8086 | 0.9126 | 0.8418 | 0.9181 | 0.9115 |
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+ | 0.3583 | 0.5042 | 1500 | 0.3394 | 0.8617 | 0.8135 | 0.9158 | 0.8467 | 0.9321 | 0.9322 |
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+ | 0.3405 | 0.6723 | 2000 | 0.3418 | 0.8664 | 0.8789 | 0.8542 | 0.8627 | 0.9379 | 0.9360 |
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+ | 0.323 | 0.8403 | 2500 | 0.3204 | 0.8565 | 0.9085 | 0.8101 | 0.8585 | 0.9453 | 0.9453 |
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+ | 0.3109 | 1.0084 | 3000 | 0.3163 | 0.8774 | 0.8875 | 0.8675 | 0.8736 | 0.9471 | 0.9477 |
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+ | 0.2315 | 1.1765 | 3500 | 0.3899 | 0.8803 | 0.8255 | 0.9429 | 0.8664 | 0.9473 | 0.9447 |
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+ | 0.2265 | 1.3445 | 4000 | 0.3476 | 0.8816 | 0.8476 | 0.9184 | 0.8714 | 0.9483 | 0.9489 |
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+ | 0.2286 | 1.5126 | 4500 | 0.3797 | 0.8587 | 0.9147 | 0.8091 | 0.8612 | 0.9445 | 0.9471 |
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+ | 0.23 | 1.6807 | 5000 | 0.3251 | 0.8845 | 0.8714 | 0.8981 | 0.8778 | 0.9486 | 0.9497 |
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+ | 0.2271 | 1.8487 | 5500 | 0.3160 | 0.8836 | 0.8579 | 0.9110 | 0.8750 | 0.9489 | 0.9489 |
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+ | 0.2199 | 2.0168 | 6000 | 0.4896 | 0.8836 | 0.8300 | 0.9445 | 0.8703 | 0.9477 | 0.9459 |
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+ | 0.2465 | 2.1849 | 6500 | 2.0128 | 0.8753 | 0.8186 | 0.9403 | 0.8603 | 0.9383 | 0.9326 |
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  ### Framework versions
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