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---
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_BioS73_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_BioS73_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: 1.7558
- F1 Score: 0.8878
- Precision: 0.8944
- Recall: 0.8813
- Accuracy: 0.8811
- Auc: 0.9434
- Prc: 0.9416
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.4249 | 0.3726 | 500 | 0.3524 | 0.8663 | 0.8348 | 0.9001 | 0.8517 | 0.9271 | 0.9269 |
| 0.3396 | 0.7452 | 1000 | 0.3162 | 0.8815 | 0.8701 | 0.8932 | 0.8718 | 0.9411 | 0.9405 |
| 0.2838 | 1.1177 | 1500 | 0.2938 | 0.8907 | 0.8666 | 0.9162 | 0.8800 | 0.9516 | 0.9533 |
| 0.244 | 1.4903 | 2000 | 0.2702 | 0.9001 | 0.8852 | 0.9155 | 0.8915 | 0.9528 | 0.9523 |
| 0.2207 | 1.8629 | 2500 | 0.3141 | 0.8993 | 0.8656 | 0.9358 | 0.8882 | 0.9526 | 0.9517 |
| 0.2753 | 2.2355 | 3000 | 1.4071 | 0.9004 | 0.8611 | 0.9434 | 0.8886 | 0.9400 | 0.9247 |
| 0.4227 | 2.6080 | 3500 | 1.5821 | 0.8953 | 0.9069 | 0.8841 | 0.8897 | 0.9529 | 0.9540 |
| 0.4015 | 2.9806 | 4000 | 1.6699 | 0.8918 | 0.8884 | 0.8953 | 0.8841 | 0.9511 | 0.9516 |
| 0.2124 | 3.3532 | 4500 | 1.7558 | 0.8878 | 0.8944 | 0.8813 | 0.8811 | 0.9434 | 0.9416 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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