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---
license: bsd-3-clause
base_model: LongSafari/hyenadna-small-32k-seqlen-hf
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: hyenadna-small-32k-seqlen-hf_ft_BioS74_1kbpHG19_DHSs_H3K27AC
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hyenadna-small-32k-seqlen-hf_ft_BioS74_1kbpHG19_DHSs_H3K27AC
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.
It achieves the following results on the evaluation set:
- Loss: 0.4605
- F1 Score: 0.8107
- Precision: 0.7689
- Recall: 0.8574
- Accuracy: 0.7904
- Auc: 0.8669
- Prc: 0.8594
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Precision | Recall | Accuracy | Auc | Prc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5565 | 0.1314 | 500 | 0.5060 | 0.7829 | 0.7556 | 0.8122 | 0.7641 | 0.8275 | 0.8077 |
| 0.4991 | 0.2629 | 1000 | 0.5124 | 0.7801 | 0.7916 | 0.7690 | 0.7731 | 0.8364 | 0.8255 |
| 0.494 | 0.3943 | 1500 | 0.4957 | 0.7822 | 0.8035 | 0.7619 | 0.7778 | 0.8460 | 0.8325 |
| 0.4752 | 0.5258 | 2000 | 0.5005 | 0.7964 | 0.7661 | 0.8292 | 0.7781 | 0.8496 | 0.8368 |
| 0.4546 | 0.6572 | 2500 | 0.4923 | 0.8041 | 0.7414 | 0.8785 | 0.7760 | 0.8515 | 0.8359 |
| 0.4858 | 0.7886 | 3000 | 0.4669 | 0.8017 | 0.7556 | 0.8538 | 0.7789 | 0.8495 | 0.8355 |
| 0.4677 | 0.9201 | 3500 | 0.4842 | 0.8019 | 0.7881 | 0.8162 | 0.7889 | 0.8583 | 0.8467 |
| 0.4695 | 1.0515 | 4000 | 0.4893 | 0.7893 | 0.8102 | 0.7695 | 0.7849 | 0.8616 | 0.8504 |
| 0.4576 | 1.1830 | 4500 | 0.4612 | 0.8078 | 0.7760 | 0.8423 | 0.7902 | 0.8599 | 0.8482 |
| 0.4574 | 1.3144 | 5000 | 0.4591 | 0.8122 | 0.7633 | 0.8679 | 0.7899 | 0.8629 | 0.8519 |
| 0.445 | 1.4458 | 5500 | 0.5035 | 0.7831 | 0.8194 | 0.7499 | 0.7825 | 0.8640 | 0.8589 |
| 0.4302 | 1.5773 | 6000 | 0.4984 | 0.8064 | 0.7856 | 0.8282 | 0.7917 | 0.8622 | 0.8502 |
| 0.4252 | 1.7087 | 6500 | 0.4651 | 0.8007 | 0.7973 | 0.8041 | 0.7904 | 0.8642 | 0.8551 |
| 0.4408 | 1.8402 | 7000 | 0.4837 | 0.7988 | 0.8103 | 0.7875 | 0.7923 | 0.8619 | 0.8567 |
| 0.4444 | 1.9716 | 7500 | 0.4605 | 0.8107 | 0.7689 | 0.8574 | 0.7904 | 0.8669 | 0.8594 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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