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--- |
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license: bsd-3-clause |
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base_model: LongSafari/hyenadna-medium-160k-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-160k-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-160k-seqlen-hf_ft_BioS45_1kbpHG19_DHSs_H3K27AC |
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This model is a fine-tuned version of [LongSafari/hyenadna-medium-160k-seqlen-hf](https://huggingface.co/LongSafari/hyenadna-medium-160k-seqlen-hf) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5104 |
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- F1 Score: 0.8100 |
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- Precision: 0.8002 |
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- Recall: 0.8202 |
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- Accuracy: 0.7993 |
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- Auc: 0.8765 |
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- Prc: 0.8748 |
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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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| 0.5145 | 0.4205 | 500 | 0.4663 | 0.7945 | 0.8003 | 0.7887 | 0.7871 | 0.8575 | 0.8420 | |
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| 0.4678 | 0.8410 | 1000 | 0.4667 | 0.8075 | 0.7334 | 0.8984 | 0.7766 | 0.8691 | 0.8628 | |
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| 0.4586 | 1.2616 | 1500 | 0.4759 | 0.8157 | 0.7463 | 0.8992 | 0.7880 | 0.8674 | 0.8598 | |
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| 0.4467 | 1.6821 | 2000 | 0.4349 | 0.8134 | 0.7961 | 0.8315 | 0.8010 | 0.8798 | 0.8751 | |
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| 0.4298 | 2.1026 | 2500 | 0.4558 | 0.8170 | 0.7480 | 0.9 | 0.7897 | 0.8749 | 0.8679 | |
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| 0.4068 | 2.5231 | 3000 | 0.4374 | 0.7982 | 0.8139 | 0.7831 | 0.7934 | 0.8830 | 0.8793 | |
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| 0.4299 | 2.9437 | 3500 | 0.4358 | 0.8117 | 0.8033 | 0.8202 | 0.8014 | 0.8816 | 0.8787 | |
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| 0.3994 | 3.3642 | 4000 | 0.4543 | 0.8120 | 0.8287 | 0.7960 | 0.8077 | 0.8815 | 0.8781 | |
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| 0.3742 | 3.7847 | 4500 | 0.4463 | 0.8139 | 0.7948 | 0.8339 | 0.8010 | 0.8796 | 0.8733 | |
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| 0.345 | 4.2052 | 5000 | 0.5104 | 0.8100 | 0.8002 | 0.8202 | 0.7993 | 0.8765 | 0.8748 | |
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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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