---
license: bsd-3-clause
base_model: LongSafari/hyenadna-large-1m-seqlen-hf
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: hyenadna-large-1m-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-large-1m-seqlen-hf_ft_BioS45_1kbpHG19_DHSs_H3K27AC

This model is a fine-tuned version of [LongSafari/hyenadna-large-1m-seqlen-hf](https://huggingface.co/LongSafari/hyenadna-large-1m-seqlen-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4630
- F1 Score: 0.8133
- Precision: 0.7796
- Recall: 0.85
- Accuracy: 0.7964
- Auc: 0.8733
- Prc: 0.8740

## 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    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5578        | 0.2103 | 500  | 0.5077          | 0.7339   | 0.8087    | 0.6718 | 0.7459   | 0.8407 | 0.8258 |
| 0.5127        | 0.4207 | 1000 | 0.4832          | 0.7813   | 0.7861    | 0.7766 | 0.7732   | 0.8460 | 0.8411 |
| 0.4843        | 0.6310 | 1500 | 0.5096          | 0.7670   | 0.8214    | 0.7194 | 0.7720   | 0.8533 | 0.8519 |
| 0.4833        | 0.8414 | 2000 | 0.4942          | 0.8042   | 0.7317    | 0.8927 | 0.7732   | 0.8633 | 0.8521 |
| 0.4748        | 1.0517 | 2500 | 0.4991          | 0.7792   | 0.7997    | 0.7597 | 0.7753   | 0.8581 | 0.8511 |
| 0.4723        | 1.2621 | 3000 | 0.4819          | 0.7833   | 0.8276    | 0.7435 | 0.7854   | 0.8618 | 0.8531 |
| 0.474         | 1.4724 | 3500 | 0.4547          | 0.8026   | 0.8003    | 0.8048 | 0.7934   | 0.8717 | 0.8655 |
| 0.4531        | 1.6828 | 4000 | 0.4560          | 0.8197   | 0.7537    | 0.8984 | 0.7939   | 0.8696 | 0.8629 |
| 0.4601        | 1.8931 | 4500 | 0.4601          | 0.8135   | 0.7554    | 0.8815 | 0.7892   | 0.8658 | 0.8569 |
| 0.441         | 2.1035 | 5000 | 0.4680          | 0.8103   | 0.7545    | 0.875  | 0.7863   | 0.8610 | 0.8497 |
| 0.4267        | 2.3138 | 5500 | 0.4784          | 0.8150   | 0.7775    | 0.8565 | 0.7972   | 0.8705 | 0.8622 |
| 0.4336        | 2.5242 | 6000 | 0.4562          | 0.8032   | 0.7891    | 0.8177 | 0.7909   | 0.8684 | 0.8590 |
| 0.446         | 2.7345 | 6500 | 0.4630          | 0.8133   | 0.7796    | 0.85   | 0.7964   | 0.8733 | 0.8740 |


### Framework versions

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