---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-v2-250m-multi-species
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-v2-250m-multi-species_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. -->

# nucleotide-transformer-v2-250m-multi-species_ft_BioS45_1kbpHG19_DHSs_H3K27AC

This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-250m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-250m-multi-species) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4434
- F1 Score: 0.8539
- Precision: 0.8522
- Recall: 0.8556
- Accuracy: 0.8473
- Auc: 0.9230
- Prc: 0.9160

## 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    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5396        | 0.2103 | 500  | 0.4921          | 0.7677   | 0.8407    | 0.7065 | 0.7770   | 0.8663 | 0.8653 |
| 0.45          | 0.4207 | 1000 | 0.4548          | 0.8362   | 0.7580    | 0.9323 | 0.8094   | 0.9049 | 0.9023 |
| 0.4027        | 0.6310 | 1500 | 0.4014          | 0.8472   | 0.7861    | 0.9185 | 0.8271   | 0.9120 | 0.9072 |
| 0.3993        | 0.8414 | 2000 | 0.3715          | 0.8561   | 0.8487    | 0.8637 | 0.8485   | 0.9153 | 0.9089 |
| 0.3709        | 1.0517 | 2500 | 0.4005          | 0.8647   | 0.8441    | 0.8863 | 0.8553   | 0.9232 | 0.9173 |
| 0.3206        | 1.2621 | 3000 | 0.4735          | 0.8517   | 0.8355    | 0.8685 | 0.8422   | 0.9172 | 0.9132 |
| 0.3259        | 1.4724 | 3500 | 0.4264          | 0.8612   | 0.8471    | 0.8758 | 0.8528   | 0.9234 | 0.9195 |
| 0.316         | 1.6828 | 4000 | 0.4434          | 0.8539   | 0.8522    | 0.8556 | 0.8473   | 0.9230 | 0.9160 |


### Framework versions

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