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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_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-v2-250m-multi-species_ft_BioS73_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: 0.5967
- F1 Score: 0.8904
- Precision: 0.8843
- Recall: 0.8966
- Accuracy: 0.8822
- Auc: 0.9398
- Prc: 0.9372
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.4839 | 0.1864 | 500 | 0.4577 | 0.8288 | 0.7331 | 0.9532 | 0.7898 | 0.8989 | 0.8988 |
| 0.3857 | 0.3727 | 1000 | 0.3786 | 0.8651 | 0.8327 | 0.9001 | 0.8502 | 0.9197 | 0.9135 |
| 0.3682 | 0.5591 | 1500 | 0.3529 | 0.8689 | 0.8543 | 0.8841 | 0.8576 | 0.9310 | 0.9303 |
| 0.362 | 0.7454 | 2000 | 0.3569 | 0.8709 | 0.8802 | 0.8617 | 0.8636 | 0.9355 | 0.9330 |
| 0.3336 | 0.9318 | 2500 | 0.4081 | 0.8779 | 0.8023 | 0.9693 | 0.8561 | 0.9397 | 0.9373 |
| 0.2989 | 1.1182 | 3000 | 0.4118 | 0.8799 | 0.8723 | 0.8876 | 0.8707 | 0.9424 | 0.9392 |
| 0.2686 | 1.3045 | 3500 | 0.3579 | 0.8852 | 0.8569 | 0.9155 | 0.8733 | 0.9393 | 0.9343 |
| 0.2696 | 1.4909 | 4000 | 0.3695 | 0.8733 | 0.8957 | 0.8520 | 0.8681 | 0.9439 | 0.9417 |
| 0.2678 | 1.6772 | 4500 | 0.4433 | 0.8944 | 0.8756 | 0.9141 | 0.8848 | 0.9416 | 0.9379 |
| 0.2744 | 1.8636 | 5000 | 0.3976 | 0.8922 | 0.8406 | 0.9504 | 0.8774 | 0.9418 | 0.9394 |
| 0.2166 | 2.0499 | 5500 | 0.5432 | 0.8815 | 0.8802 | 0.8827 | 0.8733 | 0.9426 | 0.9406 |
| 0.1426 | 2.2363 | 6000 | 0.5888 | 0.8988 | 0.8564 | 0.9455 | 0.8863 | 0.9424 | 0.9386 |
| 0.1457 | 2.4227 | 6500 | 0.5963 | 0.8840 | 0.8819 | 0.8862 | 0.8759 | 0.9404 | 0.9370 |
| 0.144 | 2.6090 | 7000 | 0.6159 | 0.8724 | 0.8962 | 0.8499 | 0.8673 | 0.9458 | 0.9454 |
| 0.1279 | 2.7954 | 7500 | 0.5967 | 0.8904 | 0.8843 | 0.8966 | 0.8822 | 0.9398 | 0.9372 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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