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--- |
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license: bsd-3-clause |
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base_model: LongSafari/hyenadna-medium-450k-seqlen-hf |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- accuracy |
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model-index: |
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- name: hyenadna-medium-450k-seqlen-hf_ft_BioS45_1kbpHG19_DHSs_H3K27AC |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# hyenadna-medium-450k-seqlen-hf_ft_BioS45_1kbpHG19_DHSs_H3K27AC |
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This model is a fine-tuned version of [LongSafari/hyenadna-medium-450k-seqlen-hf](https://huggingface.co/LongSafari/hyenadna-medium-450k-seqlen-hf) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4842 |
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- F1 Score: 0.8087 |
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- Precision: 0.7837 |
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- Recall: 0.8355 |
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- Accuracy: 0.7939 |
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- Auc: 0.8682 |
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- Prc: 0.8657 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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| 0.5149 | 0.4205 | 500 | 0.4843 | 0.8028 | 0.7336 | 0.8863 | 0.7728 | 0.8553 | 0.8481 | |
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| 0.4584 | 0.8410 | 1000 | 0.4805 | 0.8075 | 0.7280 | 0.9065 | 0.7745 | 0.8632 | 0.8518 | |
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| 0.4489 | 1.2616 | 1500 | 0.4646 | 0.8115 | 0.7566 | 0.875 | 0.7880 | 0.8676 | 0.8583 | |
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| 0.4367 | 1.6821 | 2000 | 0.4406 | 0.8133 | 0.7810 | 0.8484 | 0.7968 | 0.8720 | 0.8645 | |
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| 0.4138 | 2.1026 | 2500 | 0.4448 | 0.8189 | 0.7825 | 0.8589 | 0.8019 | 0.8732 | 0.8666 | |
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| 0.3859 | 2.5231 | 3000 | 0.4459 | 0.8010 | 0.8019 | 0.8 | 0.7926 | 0.8681 | 0.8619 | |
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| 0.4117 | 2.9437 | 3500 | 0.4532 | 0.8040 | 0.8024 | 0.8056 | 0.7951 | 0.8699 | 0.8678 | |
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| 0.3722 | 3.3642 | 4000 | 0.4556 | 0.7973 | 0.7969 | 0.7976 | 0.7884 | 0.8665 | 0.8668 | |
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| 0.3493 | 3.7847 | 4500 | 0.4849 | 0.7944 | 0.8121 | 0.7774 | 0.7901 | 0.8704 | 0.8684 | |
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| 0.313 | 4.2052 | 5000 | 0.4842 | 0.8087 | 0.7837 | 0.8355 | 0.7939 | 0.8682 | 0.8657 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.0 |
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