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license: bsd-3-clause
base_model: LongSafari/hyenadna-medium-450k-seqlen-hf
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: hyenadna-medium-450k-seqlen-hf_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. -->
# hyenadna-medium-450k-seqlen-hf_ft_BioS2_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [LongSafari/hyenadna-medium-450k-seqlen-hf](https://huggingface.co/LongSafari/hyenadna-medium-450k-seqlen-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4664
- F1 Score: 0.8050
- Precision: 0.7960
- Recall: 0.8141
- Accuracy: 0.7932
- Auc: 0.8726
- Prc: 0.8695
## 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.561 | 0.1682 | 500 | 0.5369 | 0.7760 | 0.7043 | 0.8639 | 0.7385 | 0.7986 | 0.7757 |
| 0.5175 | 0.3365 | 1000 | 0.5174 | 0.7666 | 0.7529 | 0.7807 | 0.7508 | 0.8132 | 0.7964 |
| 0.5015 | 0.5047 | 1500 | 0.4905 | 0.7942 | 0.7265 | 0.8758 | 0.7621 | 0.8354 | 0.8196 |
| 0.4923 | 0.6729 | 2000 | 0.4868 | 0.7845 | 0.7653 | 0.8048 | 0.7683 | 0.8351 | 0.8193 |
| 0.4786 | 0.8412 | 2500 | 0.4833 | 0.7929 | 0.7716 | 0.8154 | 0.7767 | 0.8468 | 0.8335 |
| 0.4767 | 1.0094 | 3000 | 0.4572 | 0.8062 | 0.7699 | 0.8462 | 0.7868 | 0.8595 | 0.8507 |
| 0.4431 | 1.1777 | 3500 | 0.4899 | 0.7679 | 0.8116 | 0.7287 | 0.7691 | 0.8486 | 0.8384 |
| 0.4272 | 1.3459 | 4000 | 0.4569 | 0.8054 | 0.7801 | 0.8324 | 0.7892 | 0.8628 | 0.8535 |
| 0.4394 | 1.5141 | 4500 | 0.4544 | 0.7961 | 0.8073 | 0.7852 | 0.7892 | 0.8696 | 0.8630 |
| 0.434 | 1.6824 | 5000 | 0.4483 | 0.8045 | 0.7936 | 0.8157 | 0.7922 | 0.8688 | 0.8644 |
| 0.4411 | 1.8506 | 5500 | 0.4454 | 0.8199 | 0.7694 | 0.8774 | 0.7979 | 0.8721 | 0.8649 |
| 0.4238 | 2.0188 | 6000 | 0.4617 | 0.7927 | 0.8057 | 0.7801 | 0.7861 | 0.8675 | 0.8643 |
| 0.3887 | 2.1871 | 6500 | 0.4518 | 0.7971 | 0.8163 | 0.7788 | 0.7922 | 0.8724 | 0.8714 |
| 0.3922 | 2.3553 | 7000 | 0.4510 | 0.8050 | 0.8070 | 0.8029 | 0.7961 | 0.8742 | 0.8709 |
| 0.3943 | 2.5236 | 7500 | 0.4423 | 0.8207 | 0.7711 | 0.8770 | 0.7991 | 0.8769 | 0.8724 |
| 0.3899 | 2.6918 | 8000 | 0.4576 | 0.8007 | 0.8050 | 0.7965 | 0.7922 | 0.8725 | 0.8687 |
| 0.3901 | 2.8600 | 8500 | 0.4486 | 0.8218 | 0.7582 | 0.8970 | 0.7961 | 0.8737 | 0.8687 |
| 0.386 | 3.0283 | 9000 | 0.4721 | 0.8037 | 0.7910 | 0.8167 | 0.7908 | 0.8668 | 0.8611 |
| 0.3453 | 3.1965 | 9500 | 0.5186 | 0.7673 | 0.8195 | 0.7213 | 0.7707 | 0.8586 | 0.8532 |
| 0.3522 | 3.3647 | 10000 | 0.4681 | 0.8064 | 0.7772 | 0.8379 | 0.7892 | 0.8693 | 0.8689 |
| 0.3473 | 3.5330 | 10500 | 0.4696 | 0.8048 | 0.8042 | 0.8055 | 0.7952 | 0.8702 | 0.8676 |
| 0.3377 | 3.7012 | 11000 | 0.4664 | 0.8050 | 0.7960 | 0.8141 | 0.7932 | 0.8726 | 0.8695 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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