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---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-v2-250m-multi-species
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-v2-250m-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-v2-250m-multi-species_ft_BioS74_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-250m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-250m-multi-species) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4377
- F1 Score: 0.8443
- Precision: 0.8375
- Recall: 0.8513
- Accuracy: 0.8357
- Auc: 0.9204
- Prc: 0.9200
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5435 | 0.1314 | 500 | 0.4807 | 0.7852 | 0.7671 | 0.8041 | 0.7697 | 0.8475 | 0.8374 |
| 0.4802 | 0.2629 | 1000 | 0.4676 | 0.7970 | 0.8330 | 0.7639 | 0.7962 | 0.8865 | 0.8785 |
| 0.447 | 0.3943 | 1500 | 0.4175 | 0.8250 | 0.7963 | 0.8559 | 0.8099 | 0.8886 | 0.8861 |
| 0.4234 | 0.5258 | 2000 | 0.4370 | 0.8314 | 0.7957 | 0.8704 | 0.8151 | 0.8937 | 0.8886 |
| 0.3993 | 0.6572 | 2500 | 0.4282 | 0.8396 | 0.7796 | 0.9096 | 0.8180 | 0.9022 | 0.9012 |
| 0.4313 | 0.7886 | 3000 | 0.3782 | 0.8460 | 0.8120 | 0.8830 | 0.8317 | 0.9087 | 0.9067 |
| 0.4072 | 0.9201 | 3500 | 0.4128 | 0.8470 | 0.8187 | 0.8774 | 0.8341 | 0.9120 | 0.9084 |
| 0.3926 | 1.0515 | 4000 | 0.4713 | 0.8271 | 0.8572 | 0.7991 | 0.8251 | 0.9079 | 0.9001 |
| 0.3545 | 1.1830 | 4500 | 0.3876 | 0.8472 | 0.8181 | 0.8785 | 0.8341 | 0.9127 | 0.9099 |
| 0.3484 | 1.3144 | 5000 | 0.3995 | 0.8388 | 0.8323 | 0.8453 | 0.8299 | 0.9133 | 0.9091 |
| 0.3486 | 1.4458 | 5500 | 0.4504 | 0.8275 | 0.8627 | 0.7951 | 0.8265 | 0.9164 | 0.9164 |
| 0.3305 | 1.5773 | 6000 | 0.4377 | 0.8443 | 0.8375 | 0.8513 | 0.8357 | 0.9204 | 0.9200 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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