---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-2.5b-multi-species
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-2.5b-multi-species_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-2.5b-multi-species_ft_BioS74_1kbpHG19_DHSs_H3K27AC

This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-2.5b-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-2.5b-multi-species) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6420
- F1 Score: 0.8190
- Precision: 0.8800
- Recall: 0.7659
- Accuracy: 0.8228
- Auc: 0.9177
- Prc: 0.9208

## 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    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5322        | 0.1314 | 500  | 0.4247          | 0.8242   | 0.8041    | 0.8453 | 0.8112   | 0.8883 | 0.8826 |
| 0.4778        | 0.2629 | 1000 | 0.5435          | 0.6778   | 0.9073    | 0.5409 | 0.7307   | 0.8958 | 0.8905 |
| 0.4503        | 0.3943 | 1500 | 0.3962          | 0.8223   | 0.8531    | 0.7936 | 0.8204   | 0.9106 | 0.9098 |
| 0.4075        | 0.5258 | 2000 | 0.4478          | 0.8457   | 0.7944    | 0.9041 | 0.8272   | 0.9127 | 0.9153 |
| 0.3929        | 0.6572 | 2500 | 0.3822          | 0.8488   | 0.7917    | 0.9146 | 0.8293   | 0.9193 | 0.9210 |
| 0.4237        | 0.7886 | 3000 | 0.3594          | 0.8401   | 0.8435    | 0.8368 | 0.8333   | 0.9193 | 0.9206 |
| 0.3992        | 0.9201 | 3500 | 0.3716          | 0.8437   | 0.8376    | 0.8498 | 0.8351   | 0.9221 | 0.9231 |
| 0.3819        | 1.0515 | 4000 | 0.6420          | 0.8190   | 0.8800    | 0.7659 | 0.8228   | 0.9177 | 0.9208 |


### Framework versions

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