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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_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-v2-250m-multi-species_ft_BioS2_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: 0.4669
- F1 Score: 0.8482
- Precision: 0.9254
- Recall: 0.7829
- Accuracy: 0.8514
- Auc: 0.9436
- Prc: 0.9461
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5459 | 0.0840 | 500 | 0.4629 | 0.8082 | 0.7697 | 0.8507 | 0.7858 | 0.8605 | 0.8526 |
| 0.476 | 0.1681 | 1000 | 0.4365 | 0.8113 | 0.8419 | 0.7829 | 0.8068 | 0.8914 | 0.8847 |
| 0.4251 | 0.2521 | 1500 | 0.4039 | 0.8445 | 0.7750 | 0.9277 | 0.8188 | 0.9021 | 0.8959 |
| 0.4355 | 0.3362 | 2000 | 0.4217 | 0.8192 | 0.8716 | 0.7727 | 0.8191 | 0.9104 | 0.9059 |
| 0.4098 | 0.4202 | 2500 | 0.3846 | 0.8278 | 0.8977 | 0.7680 | 0.8305 | 0.9216 | 0.9202 |
| 0.3904 | 0.5043 | 3000 | 0.3587 | 0.8477 | 0.8689 | 0.8276 | 0.8423 | 0.9239 | 0.9266 |
| 0.3785 | 0.5883 | 3500 | 0.3705 | 0.8592 | 0.7885 | 0.9439 | 0.8359 | 0.9306 | 0.9291 |
| 0.37 | 0.6724 | 4000 | 0.3494 | 0.8648 | 0.8831 | 0.8472 | 0.8594 | 0.9353 | 0.9370 |
| 0.3692 | 0.7564 | 4500 | 0.3426 | 0.8667 | 0.8688 | 0.8647 | 0.8589 | 0.9363 | 0.9377 |
| 0.3528 | 0.8405 | 5000 | 0.3328 | 0.8756 | 0.8447 | 0.9087 | 0.8630 | 0.9397 | 0.9405 |
| 0.3494 | 0.9245 | 5500 | 0.3519 | 0.8750 | 0.8640 | 0.8862 | 0.8657 | 0.9403 | 0.9428 |
| 0.3383 | 1.0086 | 6000 | 0.3942 | 0.8734 | 0.8074 | 0.9512 | 0.8537 | 0.9451 | 0.9472 |
| 0.2873 | 1.0926 | 6500 | 0.3763 | 0.8873 | 0.8707 | 0.9046 | 0.8781 | 0.9463 | 0.9481 |
| 0.2825 | 1.1767 | 7000 | 0.3689 | 0.8831 | 0.8293 | 0.9442 | 0.8674 | 0.9455 | 0.9461 |
| 0.2906 | 1.2607 | 7500 | 0.3785 | 0.8851 | 0.8664 | 0.9046 | 0.8754 | 0.9453 | 0.9469 |
| 0.2796 | 1.3448 | 8000 | 0.4669 | 0.8482 | 0.9254 | 0.7829 | 0.8514 | 0.9436 | 0.9461 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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