---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-500m-human-ref
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-500m-human-ref_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. -->

# nucleotide-transformer-500m-human-ref_ft_BioS74_1kbpHG19_DHSs_H3K27AC

This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-500m-human-ref](https://huggingface.co/InstaDeepAI/nucleotide-transformer-500m-human-ref) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6125
- F1 Score: 0.7156
- Precision: 0.8670
- Recall: 0.6092
- Accuracy: 0.7465
- Auc: 0.8787
- Prc: 0.8743

## 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    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5081        | 0.1314 | 500  | 0.4408          | 0.8188   | 0.7831    | 0.8579 | 0.8012   | 0.8742 | 0.8639 |
| 0.4762        | 0.2629 | 1000 | 0.4826          | 0.7878   | 0.8291    | 0.7504 | 0.7883   | 0.8815 | 0.8757 |
| 0.4636        | 0.3943 | 1500 | 0.4253          | 0.8274   | 0.7761    | 0.8860 | 0.8065   | 0.8850 | 0.8795 |
| 0.4474        | 0.5258 | 2000 | 0.4790          | 0.8260   | 0.7859    | 0.8704 | 0.8080   | 0.8846 | 0.8789 |
| 0.4353        | 0.6572 | 2500 | 0.4458          | 0.8261   | 0.7538    | 0.9136 | 0.7986   | 0.8820 | 0.8775 |
| 0.4545        | 0.7886 | 3000 | 0.6125          | 0.7156   | 0.8670    | 0.6092 | 0.7465   | 0.8787 | 0.8743 |


### Framework versions

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