---
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_BioS2_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_BioS2_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: 0.3735
- F1 Score: 0.8587
- Precision: 0.8425
- Recall: 0.8754
- Accuracy: 0.8487
- Auc: 0.9238
- Prc: 0.9216

## 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    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5667        | 0.0840 | 500  | 0.5015          | 0.7975   | 0.7265    | 0.8838 | 0.7643   | 0.8328 | 0.8048 |
| 0.4953        | 0.1681 | 1000 | 0.4806          | 0.8157   | 0.7288    | 0.9260 | 0.7803   | 0.8607 | 0.8304 |
| 0.4666        | 0.2521 | 1500 | 0.4297          | 0.8321   | 0.7856    | 0.8844 | 0.8126   | 0.8818 | 0.8659 |
| 0.4404        | 0.3362 | 2000 | 0.4224          | 0.8267   | 0.8060    | 0.8485 | 0.8132   | 0.8860 | 0.8693 |
| 0.4366        | 0.4202 | 2500 | 0.4054          | 0.8393   | 0.7599    | 0.9372 | 0.8116   | 0.9012 | 0.8936 |
| 0.4145        | 0.5043 | 3000 | 0.3880          | 0.8276   | 0.8491    | 0.8072 | 0.8235   | 0.9060 | 0.8967 |
| 0.408         | 0.5883 | 3500 | 0.3836          | 0.8360   | 0.8481    | 0.8242 | 0.8302   | 0.9105 | 0.9063 |
| 0.3924        | 0.6724 | 4000 | 0.4161          | 0.8444   | 0.7698    | 0.9350 | 0.8191   | 0.9053 | 0.8972 |
| 0.3858        | 0.7564 | 4500 | 0.4237          | 0.8432   | 0.7774    | 0.9212 | 0.8201   | 0.9114 | 0.9041 |
| 0.3781        | 0.8405 | 5000 | 0.3678          | 0.8446   | 0.8435    | 0.8457 | 0.8366   | 0.9169 | 0.9082 |
| 0.3915        | 0.9245 | 5500 | 0.4415          | 0.8158   | 0.8908    | 0.7525 | 0.8217   | 0.9192 | 0.9156 |
| 0.3862        | 1.0086 | 6000 | 0.4456          | 0.8584   | 0.8201    | 0.9004 | 0.8440   | 0.9106 | 0.8937 |
| 0.3313        | 1.0926 | 6500 | 0.3869          | 0.8593   | 0.8335    | 0.8866 | 0.8475   | 0.9234 | 0.9158 |
| 0.3144        | 1.1767 | 7000 | 0.4080          | 0.8581   | 0.8527    | 0.8636 | 0.8501   | 0.9266 | 0.9210 |
| 0.3239        | 1.2607 | 7500 | 0.3974          | 0.8515   | 0.8587    | 0.8444 | 0.8454   | 0.9239 | 0.9188 |
| 0.3446        | 1.3448 | 8000 | 0.3735          | 0.8587   | 0.8425    | 0.8754 | 0.8487   | 0.9238 | 0.9216 |


### Framework versions

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