---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-v2-500m-multi-species
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-v2-500m-multi-species_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. -->

# nucleotide-transformer-v2-500m-multi-species_ft_BioS73_1kbpHG19_DHSs_H3K27AC

This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-500m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-500m-multi-species) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7558
- F1 Score: 0.8878
- Precision: 0.8944
- Recall: 0.8813
- Accuracy: 0.8811
- Auc: 0.9434
- Prc: 0.9416

## 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    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.4249        | 0.3726 | 500  | 0.3524          | 0.8663   | 0.8348    | 0.9001 | 0.8517   | 0.9271 | 0.9269 |
| 0.3396        | 0.7452 | 1000 | 0.3162          | 0.8815   | 0.8701    | 0.8932 | 0.8718   | 0.9411 | 0.9405 |
| 0.2838        | 1.1177 | 1500 | 0.2938          | 0.8907   | 0.8666    | 0.9162 | 0.8800   | 0.9516 | 0.9533 |
| 0.244         | 1.4903 | 2000 | 0.2702          | 0.9001   | 0.8852    | 0.9155 | 0.8915   | 0.9528 | 0.9523 |
| 0.2207        | 1.8629 | 2500 | 0.3141          | 0.8993   | 0.8656    | 0.9358 | 0.8882   | 0.9526 | 0.9517 |
| 0.2753        | 2.2355 | 3000 | 1.4071          | 0.9004   | 0.8611    | 0.9434 | 0.8886   | 0.9400 | 0.9247 |
| 0.4227        | 2.6080 | 3500 | 1.5821          | 0.8953   | 0.9069    | 0.8841 | 0.8897   | 0.9529 | 0.9540 |
| 0.4015        | 2.9806 | 4000 | 1.6699          | 0.8918   | 0.8884    | 0.8953 | 0.8841   | 0.9511 | 0.9516 |
| 0.2124        | 3.3532 | 4500 | 1.7558          | 0.8878   | 0.8944    | 0.8813 | 0.8811   | 0.9434 | 0.9416 |


### Framework versions

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