---
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_BioS73_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_BioS73_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: 0.4833
- F1 Score: 0.8364
- Precision: 0.8120
- Recall: 0.8624
- Accuracy: 0.8200
- Auc: 0.8967
- Prc: 0.8849

## 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    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.481         | 0.1864 | 500  | 0.4460          | 0.8302   | 0.7734    | 0.8959 | 0.8043   | 0.8756 | 0.8562 |
| 0.4329        | 0.3727 | 1000 | 0.4339          | 0.8343   | 0.8056    | 0.8652 | 0.8166   | 0.8840 | 0.8728 |
| 0.4273        | 0.5591 | 1500 | 0.4282          | 0.8317   | 0.8105    | 0.8541 | 0.8155   | 0.8879 | 0.8797 |
| 0.4207        | 0.7454 | 2000 | 0.4154          | 0.8442   | 0.7975    | 0.8966 | 0.8233   | 0.8906 | 0.8784 |
| 0.4072        | 0.9318 | 2500 | 0.4407          | 0.8344   | 0.8149    | 0.8547 | 0.8189   | 0.8893 | 0.8806 |
| 0.3884        | 1.1182 | 3000 | 0.4272          | 0.8471   | 0.7887    | 0.9148 | 0.8237   | 0.8936 | 0.8821 |
| 0.3692        | 1.3045 | 3500 | 0.4085          | 0.8412   | 0.8148    | 0.8694 | 0.8248   | 0.8956 | 0.8854 |
| 0.3814        | 1.4909 | 4000 | 0.3987          | 0.8379   | 0.8210    | 0.8554 | 0.8233   | 0.8977 | 0.8878 |
| 0.3628        | 1.6772 | 4500 | 0.4825          | 0.8441   | 0.8040    | 0.8883 | 0.8248   | 0.8972 | 0.8885 |
| 0.3816        | 1.8636 | 5000 | 0.4157          | 0.8482   | 0.8041    | 0.8973 | 0.8286   | 0.8944 | 0.8817 |
| 0.3462        | 2.0499 | 5500 | 0.4931          | 0.8302   | 0.8431    | 0.8177 | 0.8215   | 0.8970 | 0.8868 |
| 0.338         | 2.2363 | 6000 | 0.4842          | 0.8464   | 0.7970    | 0.9022 | 0.8252   | 0.8941 | 0.8802 |
| 0.3356        | 2.4227 | 6500 | 0.4493          | 0.8434   | 0.8034    | 0.8876 | 0.8241   | 0.8896 | 0.8750 |
| 0.3416        | 2.6090 | 7000 | 0.4466          | 0.8478   | 0.7920    | 0.9120 | 0.8252   | 0.8902 | 0.8744 |
| 0.3339        | 2.7954 | 7500 | 0.4833          | 0.8364   | 0.8120    | 0.8624 | 0.8200   | 0.8967 | 0.8849 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0