---
license: bsd-3-clause
base_model: LongSafari/hyenadna-medium-160k-seqlen-hf
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: hyenadna-medium-160k-seqlen-hf_ft_BioS45_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-160k-seqlen-hf_ft_BioS45_1kbpHG19_DHSs_H3K27AC

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.
It achieves the following results on the evaluation set:
- Loss: 0.5104
- F1 Score: 0.8100
- Precision: 0.8002
- Recall: 0.8202
- Accuracy: 0.7993
- Auc: 0.8765
- Prc: 0.8748

## 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    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5145        | 0.4205 | 500  | 0.4663          | 0.7945   | 0.8003    | 0.7887 | 0.7871   | 0.8575 | 0.8420 |
| 0.4678        | 0.8410 | 1000 | 0.4667          | 0.8075   | 0.7334    | 0.8984 | 0.7766   | 0.8691 | 0.8628 |
| 0.4586        | 1.2616 | 1500 | 0.4759          | 0.8157   | 0.7463    | 0.8992 | 0.7880   | 0.8674 | 0.8598 |
| 0.4467        | 1.6821 | 2000 | 0.4349          | 0.8134   | 0.7961    | 0.8315 | 0.8010   | 0.8798 | 0.8751 |
| 0.4298        | 2.1026 | 2500 | 0.4558          | 0.8170   | 0.7480    | 0.9    | 0.7897   | 0.8749 | 0.8679 |
| 0.4068        | 2.5231 | 3000 | 0.4374          | 0.7982   | 0.8139    | 0.7831 | 0.7934   | 0.8830 | 0.8793 |
| 0.4299        | 2.9437 | 3500 | 0.4358          | 0.8117   | 0.8033    | 0.8202 | 0.8014   | 0.8816 | 0.8787 |
| 0.3994        | 3.3642 | 4000 | 0.4543          | 0.8120   | 0.8287    | 0.7960 | 0.8077   | 0.8815 | 0.8781 |
| 0.3742        | 3.7847 | 4500 | 0.4463          | 0.8139   | 0.7948    | 0.8339 | 0.8010   | 0.8796 | 0.8733 |
| 0.345         | 4.2052 | 5000 | 0.5104          | 0.8100   | 0.8002    | 0.8202 | 0.7993   | 0.8765 | 0.8748 |


### Framework versions

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