---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-v2-100m-multi-species
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-v2-100m-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-v2-100m-multi-species_ft_BioS74_1kbpHG19_DHSs_H3K27AC

This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-100m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-100m-multi-species) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4219
- F1 Score: 0.8409
- Precision: 0.8361
- Recall: 0.8458
- Accuracy: 0.8325
- Auc: 0.9111
- Prc: 0.9049

## 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    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5544        | 0.1314 | 500  | 0.5244          | 0.7566   | 0.7764    | 0.7378 | 0.7515   | 0.8400 | 0.8313 |
| 0.5015        | 0.2629 | 1000 | 0.4997          | 0.7709   | 0.824     | 0.7243 | 0.7747   | 0.8691 | 0.8632 |
| 0.4614        | 0.3943 | 1500 | 0.4311          | 0.8219   | 0.8011    | 0.8438 | 0.8086   | 0.8891 | 0.8855 |
| 0.4294        | 0.5258 | 2000 | 0.4329          | 0.8290   | 0.7769    | 0.8885 | 0.8080   | 0.8935 | 0.8874 |
| 0.4045        | 0.6572 | 2500 | 0.4249          | 0.8331   | 0.7673    | 0.9111 | 0.8088   | 0.8987 | 0.8937 |
| 0.432         | 0.7886 | 3000 | 0.4033          | 0.8261   | 0.8244    | 0.8277 | 0.8175   | 0.8993 | 0.8931 |
| 0.4148        | 0.9201 | 3500 | 0.4032          | 0.8381   | 0.8156    | 0.8619 | 0.8257   | 0.9057 | 0.8986 |
| 0.4127        | 1.0515 | 4000 | 0.4252          | 0.8196   | 0.8417    | 0.7986 | 0.8159   | 0.9026 | 0.8932 |
| 0.3807        | 1.1830 | 4500 | 0.3981          | 0.8441   | 0.8146    | 0.8759 | 0.8307   | 0.9046 | 0.8971 |
| 0.3711        | 1.3144 | 5000 | 0.4066          | 0.8430   | 0.8130    | 0.8754 | 0.8293   | 0.9011 | 0.8941 |
| 0.363         | 1.4458 | 5500 | 0.5295          | 0.8012   | 0.8737    | 0.7398 | 0.8078   | 0.9053 | 0.9037 |
| 0.3624        | 1.5773 | 6000 | 0.4219          | 0.8409   | 0.8361    | 0.8458 | 0.8325   | 0.9111 | 0.9049 |


### Framework versions

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