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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_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-medium-160k-seqlen-hf_ft_BioS73_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: 0.5049 |
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- F1 Score: 0.8498 |
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- Precision: 0.8092 |
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- Recall: 0.8946 |
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- Accuracy: 0.8312 |
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- Auc: 0.9050 |
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- Prc: 0.8919 |
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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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| 0.4658 | 0.3726 | 500 | 0.4353 | 0.8344 | 0.7760 | 0.9022 | 0.8088 | 0.8792 | 0.8707 | |
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| 0.4465 | 0.7452 | 1000 | 0.4110 | 0.8362 | 0.8066 | 0.8680 | 0.8185 | 0.8881 | 0.8822 | |
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| 0.4114 | 1.1177 | 1500 | 0.3973 | 0.8398 | 0.7786 | 0.9113 | 0.8144 | 0.8952 | 0.8901 | |
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| 0.3962 | 1.4903 | 2000 | 0.4114 | 0.8288 | 0.8245 | 0.8331 | 0.8163 | 0.8981 | 0.8925 | |
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| 0.3888 | 1.8629 | 2500 | 0.3946 | 0.8529 | 0.7811 | 0.9392 | 0.8271 | 0.9034 | 0.8962 | |
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| 0.3713 | 2.2355 | 3000 | 0.3950 | 0.8505 | 0.7984 | 0.9099 | 0.8293 | 0.9022 | 0.8959 | |
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| 0.357 | 2.6080 | 3500 | 0.3787 | 0.8476 | 0.8081 | 0.8911 | 0.8289 | 0.9044 | 0.8980 | |
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| 0.3466 | 2.9806 | 4000 | 0.3876 | 0.8538 | 0.7936 | 0.9239 | 0.8312 | 0.9075 | 0.8995 | |
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| 0.3164 | 3.3532 | 4500 | 0.3935 | 0.8513 | 0.8097 | 0.8973 | 0.8327 | 0.9059 | 0.8937 | |
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| 0.33 | 3.7258 | 5000 | 0.4684 | 0.8245 | 0.8426 | 0.8073 | 0.8166 | 0.9083 | 0.9012 | |
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| 0.31 | 4.0984 | 5500 | 0.4464 | 0.8520 | 0.7870 | 0.9288 | 0.8278 | 0.9049 | 0.8975 | |
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| 0.2786 | 4.4709 | 6000 | 0.4071 | 0.8506 | 0.8159 | 0.8883 | 0.8334 | 0.9028 | 0.8933 | |
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| 0.2874 | 4.8435 | 6500 | 0.3797 | 0.8640 | 0.8040 | 0.9337 | 0.8431 | 0.9050 | 0.8913 | |
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| 0.2567 | 5.2161 | 7000 | 0.4544 | 0.8517 | 0.7925 | 0.9204 | 0.8289 | 0.8983 | 0.8855 | |
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| 0.2388 | 5.5887 | 7500 | 0.4834 | 0.8415 | 0.8340 | 0.8492 | 0.8293 | 0.9032 | 0.8919 | |
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| 0.2386 | 5.9613 | 8000 | 0.4892 | 0.8533 | 0.8033 | 0.9099 | 0.8330 | 0.9003 | 0.8847 | |
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| 0.1956 | 6.3338 | 8500 | 0.5595 | 0.8511 | 0.8348 | 0.8680 | 0.8379 | 0.8992 | 0.8811 | |
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| 0.2257 | 6.7064 | 9000 | 0.5049 | 0.8498 | 0.8092 | 0.8946 | 0.8312 | 0.9050 | 0.8919 | |
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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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