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---
license: bsd-3-clause
base_model: LongSafari/hyenadna-large-1m-seqlen-hf
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: hyenadna-large-1m-seqlen-hf_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. -->
# hyenadna-large-1m-seqlen-hf_ft_BioS74_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [LongSafari/hyenadna-large-1m-seqlen-hf](https://huggingface.co/LongSafari/hyenadna-large-1m-seqlen-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4843
- F1 Score: 0.7964
- Precision: 0.7556
- Recall: 0.8418
- Accuracy: 0.7747
- Auc: 0.8453
- Prc: 0.8347
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5422 | 0.1314 | 500 | 0.5443 | 0.7378 | 0.7886 | 0.6931 | 0.7420 | 0.8185 | 0.8068 |
| 0.5205 | 0.2629 | 1000 | 0.5236 | 0.7618 | 0.7807 | 0.7438 | 0.7565 | 0.8309 | 0.8226 |
| 0.5233 | 0.3943 | 1500 | 0.5168 | 0.7576 | 0.7893 | 0.7283 | 0.7560 | 0.8319 | 0.8199 |
| 0.5023 | 0.5258 | 2000 | 0.5105 | 0.7933 | 0.7591 | 0.8307 | 0.7733 | 0.8369 | 0.8254 |
| 0.4879 | 0.6572 | 2500 | 0.5088 | 0.7988 | 0.7263 | 0.8875 | 0.7660 | 0.8382 | 0.8229 |
| 0.5064 | 0.7886 | 3000 | 0.4937 | 0.7919 | 0.7569 | 0.8302 | 0.7715 | 0.8373 | 0.8267 |
| 0.4934 | 0.9201 | 3500 | 0.4923 | 0.7971 | 0.7661 | 0.8307 | 0.7786 | 0.8452 | 0.8289 |
| 0.4952 | 1.0515 | 4000 | 0.4834 | 0.7809 | 0.7915 | 0.7705 | 0.7736 | 0.8480 | 0.8352 |
| 0.488 | 1.1830 | 4500 | 0.4898 | 0.7899 | 0.7575 | 0.8252 | 0.7702 | 0.8404 | 0.8293 |
| 0.494 | 1.3144 | 5000 | 0.4843 | 0.7964 | 0.7556 | 0.8418 | 0.7747 | 0.8453 | 0.8347 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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