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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_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-large-1m-seqlen-hf_ft_BioS45_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: 0.4630 |
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- F1 Score: 0.8133 |
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- Precision: 0.7796 |
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- Recall: 0.85 |
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- Accuracy: 0.7964 |
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- Auc: 0.8733 |
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- Prc: 0.8740 |
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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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| 0.5578 | 0.2103 | 500 | 0.5077 | 0.7339 | 0.8087 | 0.6718 | 0.7459 | 0.8407 | 0.8258 | |
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| 0.5127 | 0.4207 | 1000 | 0.4832 | 0.7813 | 0.7861 | 0.7766 | 0.7732 | 0.8460 | 0.8411 | |
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| 0.4843 | 0.6310 | 1500 | 0.5096 | 0.7670 | 0.8214 | 0.7194 | 0.7720 | 0.8533 | 0.8519 | |
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| 0.4833 | 0.8414 | 2000 | 0.4942 | 0.8042 | 0.7317 | 0.8927 | 0.7732 | 0.8633 | 0.8521 | |
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| 0.4748 | 1.0517 | 2500 | 0.4991 | 0.7792 | 0.7997 | 0.7597 | 0.7753 | 0.8581 | 0.8511 | |
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| 0.4723 | 1.2621 | 3000 | 0.4819 | 0.7833 | 0.8276 | 0.7435 | 0.7854 | 0.8618 | 0.8531 | |
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| 0.474 | 1.4724 | 3500 | 0.4547 | 0.8026 | 0.8003 | 0.8048 | 0.7934 | 0.8717 | 0.8655 | |
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| 0.4531 | 1.6828 | 4000 | 0.4560 | 0.8197 | 0.7537 | 0.8984 | 0.7939 | 0.8696 | 0.8629 | |
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| 0.4601 | 1.8931 | 4500 | 0.4601 | 0.8135 | 0.7554 | 0.8815 | 0.7892 | 0.8658 | 0.8569 | |
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| 0.441 | 2.1035 | 5000 | 0.4680 | 0.8103 | 0.7545 | 0.875 | 0.7863 | 0.8610 | 0.8497 | |
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| 0.4267 | 2.3138 | 5500 | 0.4784 | 0.8150 | 0.7775 | 0.8565 | 0.7972 | 0.8705 | 0.8622 | |
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| 0.4336 | 2.5242 | 6000 | 0.4562 | 0.8032 | 0.7891 | 0.8177 | 0.7909 | 0.8684 | 0.8590 | |
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| 0.446 | 2.7345 | 6500 | 0.4630 | 0.8133 | 0.7796 | 0.85 | 0.7964 | 0.8733 | 0.8740 | |
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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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