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--- |
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license: bsd-3-clause |
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base_model: LongSafari/hyenadna-small-32k-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-small-32k-seqlen-hf_ft_BioS2_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-small-32k-seqlen-hf_ft_BioS2_1kbpHG19_DHSs_H3K27AC |
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This model is a fine-tuned version of [LongSafari/hyenadna-small-32k-seqlen-hf](https://huggingface.co/LongSafari/hyenadna-small-32k-seqlen-hf) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4638 |
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- F1 Score: 0.8073 |
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- Precision: 0.8018 |
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- Recall: 0.8128 |
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- Accuracy: 0.7952 |
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- Auc: 0.8728 |
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- Prc: 0.8651 |
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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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| 0.5671 | 0.0841 | 500 | 0.5418 | 0.7721 | 0.7343 | 0.8141 | 0.7464 | 0.7985 | 0.7787 | |
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| 0.5022 | 0.1683 | 1000 | 0.5186 | 0.7826 | 0.7680 | 0.7978 | 0.7661 | 0.8279 | 0.8137 | |
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| 0.5022 | 0.2524 | 1500 | 0.5044 | 0.8036 | 0.7154 | 0.9168 | 0.7635 | 0.8360 | 0.8138 | |
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| 0.4865 | 0.3366 | 2000 | 0.4858 | 0.7887 | 0.7708 | 0.8074 | 0.7716 | 0.8440 | 0.8350 | |
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| 0.4913 | 0.4207 | 2500 | 0.4686 | 0.8062 | 0.7560 | 0.8635 | 0.7809 | 0.8553 | 0.8451 | |
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| 0.472 | 0.5049 | 3000 | 0.4715 | 0.8107 | 0.7494 | 0.8830 | 0.7824 | 0.8580 | 0.8462 | |
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| 0.4776 | 0.5890 | 3500 | 0.4581 | 0.8064 | 0.7682 | 0.8485 | 0.7849 | 0.8630 | 0.8574 | |
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| 0.4571 | 0.6732 | 4000 | 0.4620 | 0.8162 | 0.7506 | 0.8945 | 0.7874 | 0.8674 | 0.8548 | |
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| 0.4702 | 0.7573 | 4500 | 0.4550 | 0.8009 | 0.7995 | 0.8023 | 0.7895 | 0.8683 | 0.8600 | |
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| 0.4533 | 0.8415 | 5000 | 0.4493 | 0.8121 | 0.7891 | 0.8364 | 0.7957 | 0.8729 | 0.8617 | |
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| 0.4401 | 0.9256 | 5500 | 0.4472 | 0.8134 | 0.7823 | 0.8469 | 0.7949 | 0.8743 | 0.8662 | |
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| 0.4505 | 1.0098 | 6000 | 0.4459 | 0.8100 | 0.7892 | 0.8320 | 0.7940 | 0.8732 | 0.8668 | |
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| 0.3944 | 1.0939 | 6500 | 0.4638 | 0.8073 | 0.8018 | 0.8128 | 0.7952 | 0.8728 | 0.8651 | |
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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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