---
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_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-medium-160k-seqlen-hf_ft_BioS73_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: 0.5049
- F1 Score: 0.8498
- Precision: 0.8092
- Recall: 0.8946
- Accuracy: 0.8312
- Auc: 0.9050
- Prc: 0.8919

## 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    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.4658        | 0.3726 | 500  | 0.4353          | 0.8344   | 0.7760    | 0.9022 | 0.8088   | 0.8792 | 0.8707 |
| 0.4465        | 0.7452 | 1000 | 0.4110          | 0.8362   | 0.8066    | 0.8680 | 0.8185   | 0.8881 | 0.8822 |
| 0.4114        | 1.1177 | 1500 | 0.3973          | 0.8398   | 0.7786    | 0.9113 | 0.8144   | 0.8952 | 0.8901 |
| 0.3962        | 1.4903 | 2000 | 0.4114          | 0.8288   | 0.8245    | 0.8331 | 0.8163   | 0.8981 | 0.8925 |
| 0.3888        | 1.8629 | 2500 | 0.3946          | 0.8529   | 0.7811    | 0.9392 | 0.8271   | 0.9034 | 0.8962 |
| 0.3713        | 2.2355 | 3000 | 0.3950          | 0.8505   | 0.7984    | 0.9099 | 0.8293   | 0.9022 | 0.8959 |
| 0.357         | 2.6080 | 3500 | 0.3787          | 0.8476   | 0.8081    | 0.8911 | 0.8289   | 0.9044 | 0.8980 |
| 0.3466        | 2.9806 | 4000 | 0.3876          | 0.8538   | 0.7936    | 0.9239 | 0.8312   | 0.9075 | 0.8995 |
| 0.3164        | 3.3532 | 4500 | 0.3935          | 0.8513   | 0.8097    | 0.8973 | 0.8327   | 0.9059 | 0.8937 |
| 0.33          | 3.7258 | 5000 | 0.4684          | 0.8245   | 0.8426    | 0.8073 | 0.8166   | 0.9083 | 0.9012 |
| 0.31          | 4.0984 | 5500 | 0.4464          | 0.8520   | 0.7870    | 0.9288 | 0.8278   | 0.9049 | 0.8975 |
| 0.2786        | 4.4709 | 6000 | 0.4071          | 0.8506   | 0.8159    | 0.8883 | 0.8334   | 0.9028 | 0.8933 |
| 0.2874        | 4.8435 | 6500 | 0.3797          | 0.8640   | 0.8040    | 0.9337 | 0.8431   | 0.9050 | 0.8913 |
| 0.2567        | 5.2161 | 7000 | 0.4544          | 0.8517   | 0.7925    | 0.9204 | 0.8289   | 0.8983 | 0.8855 |
| 0.2388        | 5.5887 | 7500 | 0.4834          | 0.8415   | 0.8340    | 0.8492 | 0.8293   | 0.9032 | 0.8919 |
| 0.2386        | 5.9613 | 8000 | 0.4892          | 0.8533   | 0.8033    | 0.9099 | 0.8330   | 0.9003 | 0.8847 |
| 0.1956        | 6.3338 | 8500 | 0.5595          | 0.8511   | 0.8348    | 0.8680 | 0.8379   | 0.8992 | 0.8811 |
| 0.2257        | 6.7064 | 9000 | 0.5049          | 0.8498   | 0.8092    | 0.8946 | 0.8312   | 0.9050 | 0.8919 |


### Framework versions

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