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---
license: bsd-3-clause
base_model: LongSafari/hyenadna-medium-450k-seqlen-hf
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: hyenadna-medium-450k-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-medium-450k-seqlen-hf_ft_BioS74_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [LongSafari/hyenadna-medium-450k-seqlen-hf](https://huggingface.co/LongSafari/hyenadna-medium-450k-seqlen-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4642
- F1 Score: 0.8034
- Precision: 0.7730
- Recall: 0.8363
- Accuracy: 0.7857
- Auc: 0.8626
- Prc: 0.8590
## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5486 | 0.2629 | 500 | 0.5108 | 0.7728 | 0.7601 | 0.7860 | 0.7581 | 0.8210 | 0.8101 |
| 0.4949 | 0.5258 | 1000 | 0.4955 | 0.7852 | 0.7800 | 0.7906 | 0.7736 | 0.8368 | 0.8242 |
| 0.4746 | 0.7886 | 1500 | 0.4766 | 0.7948 | 0.7728 | 0.8182 | 0.7789 | 0.8464 | 0.8356 |
| 0.4782 | 1.0515 | 2000 | 0.4691 | 0.7927 | 0.7933 | 0.7921 | 0.7831 | 0.8544 | 0.8480 |
| 0.4619 | 1.3144 | 2500 | 0.4738 | 0.8016 | 0.7614 | 0.8463 | 0.7807 | 0.8506 | 0.8398 |
| 0.45 | 1.5773 | 3000 | 0.4861 | 0.8063 | 0.7462 | 0.8769 | 0.7794 | 0.8530 | 0.8446 |
| 0.4429 | 1.8402 | 3500 | 0.4836 | 0.7840 | 0.8013 | 0.7675 | 0.7786 | 0.8553 | 0.8508 |
| 0.4459 | 2.1030 | 4000 | 0.4606 | 0.8006 | 0.7833 | 0.8187 | 0.7865 | 0.8616 | 0.8588 |
| 0.413 | 2.3659 | 4500 | 0.4687 | 0.7903 | 0.7931 | 0.7875 | 0.7812 | 0.8600 | 0.8572 |
| 0.4162 | 2.6288 | 5000 | 0.4616 | 0.8036 | 0.7786 | 0.8302 | 0.7875 | 0.8600 | 0.8555 |
| 0.4196 | 2.8917 | 5500 | 0.4642 | 0.8034 | 0.7730 | 0.8363 | 0.7857 | 0.8626 | 0.8590 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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