---
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_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-v2-500m-multi-species_ft_BioS45_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: 1.2234
- F1 Score: 0.8240
- Precision: 0.8645
- Recall: 0.7871
- Accuracy: 0.8246
- Auc: 0.9119
- Prc: 0.9103

## 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    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.4976        | 0.4205 | 500  | 0.4129          | 0.8285   | 0.7809    | 0.8823 | 0.8094   | 0.8977 | 0.8961 |
| 0.384         | 0.8410 | 1000 | 0.3673          | 0.8526   | 0.8023    | 0.9097 | 0.8359   | 0.9206 | 0.9183 |
| 0.3235        | 1.2616 | 1500 | 0.3902          | 0.8505   | 0.8643    | 0.8371 | 0.8464   | 0.9269 | 0.9284 |
| 0.2866        | 1.6821 | 2000 | 0.3665          | 0.8623   | 0.8514    | 0.8734 | 0.8544   | 0.9286 | 0.9270 |
| 0.2547        | 2.1026 | 2500 | 0.7526          | 0.8592   | 0.8003    | 0.9274 | 0.8414   | 0.9245 | 0.9232 |
| 0.316         | 2.5231 | 3000 | 1.5948          | 0.8466   | 0.8614    | 0.8323 | 0.8427   | 0.9224 | 0.9239 |
| 0.4261        | 2.9437 | 3500 | 1.2234          | 0.8240   | 0.8645    | 0.7871 | 0.8246   | 0.9119 | 0.9103 |


### Framework versions

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