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nucleotide-transformer-v2-250m-multi-species_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot

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End of training

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  1. README.md +13 -12
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -20,13 +20,13 @@ 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-v2-250m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-250m-multi-species) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.5602
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- - F1 Score: 0.7812
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- - Precision: 0.8065
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- - Recall: 0.7576
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- - Accuracy: 0.7627
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- - Auc: 0.8328
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- - Prc: 0.8241
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  ## Model description
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@@ -51,15 +51,16 @@ 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: 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 | Auc | Prc |
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- |:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
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- | 0.1788 | 8.3333 | 500 | 1.3537 | 0.7812 | 0.8065 | 0.7576 | 0.7627 | 0.8345 | 0.8250 |
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- | 0.0004 | 16.6667 | 1000 | 1.5602 | 0.7812 | 0.8065 | 0.7576 | 0.7627 | 0.8328 | 0.8241 |
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-250m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-250m-multi-species) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7399
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+ - F1 Score: 0.8084
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+ - Precision: 0.8006
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+ - Recall: 0.8164
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+ - Accuracy: 0.8013
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+ - Auc: 0.8735
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+ - Prc: 0.8637
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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: 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 | Auc | Prc |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
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+ | 0.5348 | 0.8403 | 500 | 0.4797 | 0.7964 | 0.7422 | 0.8590 | 0.7744 | 0.8621 | 0.8383 |
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+ | 0.3808 | 1.6807 | 1000 | 0.5203 | 0.7973 | 0.8081 | 0.7869 | 0.7946 | 0.8683 | 0.8507 |
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+ | 0.2366 | 2.5210 | 1500 | 0.7399 | 0.8084 | 0.8006 | 0.8164 | 0.8013 | 0.8735 | 0.8637 |
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  ### Framework versions
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