---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-v2-50m-multi-species
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-v2-50m-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-v2-50m-multi-species_ft_BioS45_1kbpHG19_DHSs_H3K27AC

This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-50m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-50m-multi-species) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4618
- F1 Score: 0.8488
- Precision: 0.8116
- Recall: 0.8895
- Accuracy: 0.8347
- Auc: 0.9100
- Prc: 0.9051

## 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    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5436        | 0.2103 | 500  | 0.4336          | 0.8161   | 0.8074    | 0.825  | 0.8061   | 0.8826 | 0.8798 |
| 0.4386        | 0.4207 | 1000 | 0.4171          | 0.8423   | 0.7929    | 0.8984 | 0.8246   | 0.9017 | 0.8982 |
| 0.402         | 0.6310 | 1500 | 0.3979          | 0.8486   | 0.7977    | 0.9065 | 0.8313   | 0.9083 | 0.9055 |
| 0.4019        | 0.8414 | 2000 | 0.3865          | 0.8505   | 0.7961    | 0.9129 | 0.8326   | 0.9060 | 0.9000 |
| 0.382         | 1.0517 | 2500 | 0.4091          | 0.8513   | 0.8169    | 0.8887 | 0.8380   | 0.9131 | 0.9049 |
| 0.3297        | 1.2621 | 3000 | 0.4890          | 0.8590   | 0.8143    | 0.9089 | 0.8443   | 0.9105 | 0.8922 |
| 0.3417        | 1.4724 | 3500 | 0.4246          | 0.8517   | 0.8355    | 0.8685 | 0.8422   | 0.9138 | 0.9072 |
| 0.3447        | 1.6828 | 4000 | 0.4299          | 0.8505   | 0.8408    | 0.8605 | 0.8422   | 0.9138 | 0.9052 |
| 0.3384        | 1.8931 | 4500 | 0.4618          | 0.8488   | 0.8116    | 0.8895 | 0.8347   | 0.9100 | 0.9051 |


### Framework versions

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