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--- |
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license: bsd-3-clause |
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base_model: LongSafari/hyenadna-large-1m-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-large-1m-seqlen-hf_ft_BioS73_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-large-1m-seqlen-hf_ft_BioS73_1kbpHG19_DHSs_H3K27AC |
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This model is a fine-tuned version of [LongSafari/hyenadna-large-1m-seqlen-hf](https://huggingface.co/LongSafari/hyenadna-large-1m-seqlen-hf) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4445 |
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- F1 Score: 0.8449 |
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- Precision: 0.7906 |
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- Recall: 0.9071 |
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- Accuracy: 0.8222 |
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- Auc: 0.8958 |
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- Prc: 0.8902 |
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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: 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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| 0.504 | 0.1864 | 500 | 0.4797 | 0.8144 | 0.7226 | 0.9330 | 0.7730 | 0.8602 | 0.8528 | |
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| 0.4627 | 0.3727 | 1000 | 0.4431 | 0.8260 | 0.7718 | 0.8883 | 0.8002 | 0.8747 | 0.8687 | |
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| 0.4575 | 0.5591 | 1500 | 0.4477 | 0.8141 | 0.8161 | 0.8122 | 0.8021 | 0.8774 | 0.8745 | |
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| 0.4486 | 0.7454 | 2000 | 0.4197 | 0.8338 | 0.7739 | 0.9036 | 0.8077 | 0.8862 | 0.8791 | |
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| 0.4296 | 0.9318 | 2500 | 0.4227 | 0.8372 | 0.7763 | 0.9085 | 0.8114 | 0.8912 | 0.8804 | |
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| 0.4277 | 1.1182 | 3000 | 0.4181 | 0.8396 | 0.7713 | 0.9211 | 0.8122 | 0.8909 | 0.8864 | |
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| 0.4166 | 1.3045 | 3500 | 0.4481 | 0.8315 | 0.8159 | 0.8478 | 0.8166 | 0.8859 | 0.8803 | |
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| 0.4235 | 1.4909 | 4000 | 0.4253 | 0.8341 | 0.7819 | 0.8939 | 0.8103 | 0.8897 | 0.8854 | |
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| 0.4149 | 1.6772 | 4500 | 0.4432 | 0.8370 | 0.7690 | 0.9183 | 0.8092 | 0.8898 | 0.8820 | |
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| 0.4099 | 1.8636 | 5000 | 0.4597 | 0.8123 | 0.7279 | 0.9190 | 0.7734 | 0.8676 | 0.8681 | |
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| 0.4078 | 2.0499 | 5500 | 0.4425 | 0.8450 | 0.7918 | 0.9057 | 0.8226 | 0.8942 | 0.8910 | |
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| 0.4201 | 2.2363 | 6000 | 0.4509 | 0.8445 | 0.7781 | 0.9232 | 0.8185 | 0.8963 | 0.8886 | |
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| 0.3896 | 2.4227 | 6500 | 0.4153 | 0.8387 | 0.7627 | 0.9316 | 0.8088 | 0.8926 | 0.8856 | |
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| 0.4104 | 2.6090 | 7000 | 0.4066 | 0.8378 | 0.8024 | 0.8764 | 0.8189 | 0.8896 | 0.8833 | |
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| 0.395 | 2.7954 | 7500 | 0.4659 | 0.8371 | 0.8190 | 0.8561 | 0.8222 | 0.8969 | 0.8908 | |
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| 0.3646 | 2.9817 | 8000 | 0.4445 | 0.8449 | 0.7906 | 0.9071 | 0.8222 | 0.8958 | 0.8902 | |
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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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