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nucleotide-transformer-v2-250m-multi-species_ft_BioS73_1kbpHG19_DHSs_H3K27AC

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  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -19,11 +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-v2-250m-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: 0.3535
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- - F1 Score: 0.8585
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- - Precision: 0.8595
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- - Recall: 0.8575
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- - Accuracy: 0.8497
 
 
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 1e-05
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- - train_batch_size: 32
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- - eval_batch_size: 32
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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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- | 0.3699 | 2.4752 | 500 | 0.3535 | 0.8585 | 0.8595 | 0.8575 | 0.8497 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.5967
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+ - F1 Score: 0.8904
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+ - Precision: 0.8843
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+ - Recall: 0.8966
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+ - Accuracy: 0.8822
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+ - Auc: 0.9398
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+ - Prc: 0.9372
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 1e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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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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+ | 0.4839 | 0.1864 | 500 | 0.4577 | 0.8288 | 0.7331 | 0.9532 | 0.7898 | 0.8989 | 0.8988 |
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+ | 0.3857 | 0.3727 | 1000 | 0.3786 | 0.8651 | 0.8327 | 0.9001 | 0.8502 | 0.9197 | 0.9135 |
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+ | 0.3682 | 0.5591 | 1500 | 0.3529 | 0.8689 | 0.8543 | 0.8841 | 0.8576 | 0.9310 | 0.9303 |
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+ | 0.362 | 0.7454 | 2000 | 0.3569 | 0.8709 | 0.8802 | 0.8617 | 0.8636 | 0.9355 | 0.9330 |
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+ | 0.3336 | 0.9318 | 2500 | 0.4081 | 0.8779 | 0.8023 | 0.9693 | 0.8561 | 0.9397 | 0.9373 |
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+ | 0.2989 | 1.1182 | 3000 | 0.4118 | 0.8799 | 0.8723 | 0.8876 | 0.8707 | 0.9424 | 0.9392 |
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+ | 0.2686 | 1.3045 | 3500 | 0.3579 | 0.8852 | 0.8569 | 0.9155 | 0.8733 | 0.9393 | 0.9343 |
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+ | 0.2696 | 1.4909 | 4000 | 0.3695 | 0.8733 | 0.8957 | 0.8520 | 0.8681 | 0.9439 | 0.9417 |
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+ | 0.2678 | 1.6772 | 4500 | 0.4433 | 0.8944 | 0.8756 | 0.9141 | 0.8848 | 0.9416 | 0.9379 |
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+ | 0.2744 | 1.8636 | 5000 | 0.3976 | 0.8922 | 0.8406 | 0.9504 | 0.8774 | 0.9418 | 0.9394 |
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+ | 0.2166 | 2.0499 | 5500 | 0.5432 | 0.8815 | 0.8802 | 0.8827 | 0.8733 | 0.9426 | 0.9406 |
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+ | 0.1426 | 2.2363 | 6000 | 0.5888 | 0.8988 | 0.8564 | 0.9455 | 0.8863 | 0.9424 | 0.9386 |
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+ | 0.1457 | 2.4227 | 6500 | 0.5963 | 0.8840 | 0.8819 | 0.8862 | 0.8759 | 0.9404 | 0.9370 |
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+ | 0.144 | 2.6090 | 7000 | 0.6159 | 0.8724 | 0.8962 | 0.8499 | 0.8673 | 0.9458 | 0.9454 |
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+ | 0.1279 | 2.7954 | 7500 | 0.5967 | 0.8904 | 0.8843 | 0.8966 | 0.8822 | 0.9398 | 0.9372 |
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  ### Framework versions
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