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DNABERT-2-117M_ft_BioS73_1kbpHG19_DHSs_H3K27AC

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bert
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custom_code
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End of training

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  1. README.md +27 -11
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -18,11 +18,13 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [zhihan1996/DNABERT-2-117M](https://huggingface.co/zhihan1996/DNABERT-2-117M) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4973
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- - F1 Score: 0.7531
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- - Precision: 0.8075
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- - Recall: 0.7056
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- - Accuracy: 0.7540
 
 
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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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- | 0.587 | 2.4752 | 500 | 0.4973 | 0.7531 | 0.8075 | 0.7056 | 0.7540 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [zhihan1996/DNABERT-2-117M](https://huggingface.co/zhihan1996/DNABERT-2-117M) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4847
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+ - F1 Score: 0.8003
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+ - Precision: 0.8578
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+ - Recall: 0.75
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+ - Accuracy: 0.8002
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+ - Auc: 0.8937
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+ - Prc: 0.8857
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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: 16
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+ - eval_batch_size: 16
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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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+ | 0.6224 | 0.3726 | 500 | 0.7088 | 0.7453 | 0.5976 | 0.9902 | 0.6388 | 0.8483 | 0.8530 |
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+ | 0.5527 | 0.7452 | 1000 | 0.4761 | 0.8021 | 0.7657 | 0.8422 | 0.7782 | 0.8540 | 0.8575 |
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+ | 0.5232 | 1.1177 | 1500 | 0.4819 | 0.7961 | 0.8003 | 0.7919 | 0.7835 | 0.8598 | 0.8622 |
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+ | 0.5198 | 1.4903 | 2000 | 0.4690 | 0.8087 | 0.7914 | 0.8268 | 0.7913 | 0.8641 | 0.8664 |
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+ | 0.5012 | 1.8629 | 2500 | 0.4830 | 0.7676 | 0.8429 | 0.7046 | 0.7723 | 0.8693 | 0.8699 |
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+ | 0.4683 | 2.2355 | 3000 | 0.4665 | 0.8259 | 0.7532 | 0.9141 | 0.7943 | 0.8768 | 0.8756 |
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+ | 0.4689 | 2.6080 | 3500 | 0.4254 | 0.8285 | 0.7695 | 0.8973 | 0.8017 | 0.8845 | 0.8808 |
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+ | 0.435 | 2.9806 | 4000 | 0.4477 | 0.8340 | 0.7540 | 0.9330 | 0.8017 | 0.8893 | 0.8834 |
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+ | 0.4208 | 3.3532 | 4500 | 0.4703 | 0.8134 | 0.8368 | 0.7912 | 0.8062 | 0.8892 | 0.8829 |
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+ | 0.4145 | 3.7258 | 5000 | 0.5007 | 0.7988 | 0.8421 | 0.7598 | 0.7958 | 0.8921 | 0.8850 |
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+ | 0.4031 | 4.0984 | 5500 | 0.4230 | 0.8363 | 0.7945 | 0.8827 | 0.8155 | 0.8921 | 0.8844 |
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+ | 0.3825 | 4.4709 | 6000 | 0.4299 | 0.8397 | 0.7611 | 0.9365 | 0.8092 | 0.8936 | 0.8853 |
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+ | 0.3875 | 4.8435 | 6500 | 0.5066 | 0.7971 | 0.8664 | 0.7381 | 0.7995 | 0.8946 | 0.8871 |
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+ | 0.3771 | 5.2161 | 7000 | 0.6022 | 0.7480 | 0.8870 | 0.6466 | 0.7674 | 0.8949 | 0.8869 |
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+ | 0.363 | 5.5887 | 7500 | 0.4847 | 0.8003 | 0.8578 | 0.75 | 0.8002 | 0.8937 | 0.8857 |
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  ### Framework versions
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