---
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: 0.4539
- F1 Score: 0.8085
- Precision: 0.7851
- Recall: 0.8332
- Accuracy: 0.7933
- Auc: 0.8657
- Prc: 0.8552

## 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    |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5457        | 0.1314 | 500   | 0.5130          | 0.7789   | 0.7681    | 0.7901 | 0.7652   | 0.8248 | 0.7983 |
| 0.5285        | 0.2629 | 1000  | 0.5064          | 0.7851   | 0.7822    | 0.7880 | 0.7741   | 0.8339 | 0.8177 |
| 0.5192        | 0.3943 | 1500  | 0.5260          | 0.7687   | 0.7852    | 0.7529 | 0.7628   | 0.8313 | 0.8174 |
| 0.4935        | 0.5258 | 2000  | 0.5067          | 0.7964   | 0.7532    | 0.8448 | 0.7739   | 0.8342 | 0.8097 |
| 0.4776        | 0.6572 | 2500  | 0.4999          | 0.8050   | 0.7410    | 0.8810 | 0.7765   | 0.8392 | 0.8221 |
| 0.5066        | 0.7886 | 3000  | 0.4872          | 0.7978   | 0.7484    | 0.8543 | 0.7733   | 0.8429 | 0.8282 |
| 0.4927        | 0.9201 | 3500  | 0.5143          | 0.8023   | 0.7509    | 0.8614 | 0.7778   | 0.8327 | 0.8071 |
| 0.5076        | 1.0515 | 4000  | 0.5345          | 0.7476   | 0.8154    | 0.6901 | 0.7560   | 0.8443 | 0.8294 |
| 0.4967        | 1.1830 | 4500  | 0.5013          | 0.7991   | 0.7147    | 0.9061 | 0.7615   | 0.8367 | 0.8181 |
| 0.4905        | 1.3144 | 5000  | 0.4846          | 0.8055   | 0.7496    | 0.8704 | 0.7799   | 0.8461 | 0.8328 |
| 0.4872        | 1.4458 | 5500  | 0.4887          | 0.7939   | 0.7888    | 0.7991 | 0.7828   | 0.8523 | 0.8405 |
| 0.4734        | 1.5773 | 6000  | 0.5150          | 0.7924   | 0.7739    | 0.8117 | 0.7773   | 0.8460 | 0.8268 |
| 0.4699        | 1.7087 | 6500  | 0.4917          | 0.7764   | 0.8120    | 0.7438 | 0.7757   | 0.8520 | 0.8421 |
| 0.4632        | 1.8402 | 7000  | 0.5059          | 0.7860   | 0.8026    | 0.7700 | 0.7804   | 0.8508 | 0.8396 |
| 0.4774        | 1.9716 | 7500  | 0.4994          | 0.8069   | 0.7269    | 0.9066 | 0.7728   | 0.8515 | 0.8323 |
| 0.4709        | 2.1030 | 8000  | 0.4750          | 0.7944   | 0.7932    | 0.7956 | 0.7844   | 0.8555 | 0.8470 |
| 0.4608        | 2.2345 | 8500  | 0.4694          | 0.7985   | 0.7901    | 0.8071 | 0.7867   | 0.8572 | 0.8476 |
| 0.4626        | 2.3659 | 9000  | 0.4691          | 0.7997   | 0.7963    | 0.8031 | 0.7894   | 0.8592 | 0.8507 |
| 0.4476        | 2.4974 | 9500  | 0.4661          | 0.8109   | 0.7818    | 0.8423 | 0.7944   | 0.8584 | 0.8398 |
| 0.4618        | 2.6288 | 10000 | 0.4752          | 0.7986   | 0.8068    | 0.7906 | 0.7912   | 0.8634 | 0.8559 |
| 0.453         | 2.7603 | 10500 | 0.4660          | 0.7966   | 0.7966    | 0.7966 | 0.7870   | 0.8590 | 0.8488 |
| 0.4482        | 2.8917 | 11000 | 0.4639          | 0.8018   | 0.7970    | 0.8066 | 0.7912   | 0.8615 | 0.8514 |
| 0.4421        | 3.0231 | 11500 | 0.4728          | 0.8128   | 0.7755    | 0.8538 | 0.7941   | 0.8611 | 0.8490 |
| 0.4359        | 3.1546 | 12000 | 0.4647          | 0.8094   | 0.7707    | 0.8523 | 0.7899   | 0.8604 | 0.8509 |
| 0.4339        | 3.2860 | 12500 | 0.4843          | 0.8012   | 0.8018    | 0.8006 | 0.7920   | 0.8671 | 0.8615 |
| 0.4415        | 3.4175 | 13000 | 0.4764          | 0.8080   | 0.7279    | 0.9081 | 0.7741   | 0.8621 | 0.8557 |
| 0.4336        | 3.5489 | 13500 | 0.4627          | 0.8056   | 0.7823    | 0.8302 | 0.7902   | 0.8637 | 0.8560 |
| 0.4446        | 3.6803 | 14000 | 0.4539          | 0.8085   | 0.7851    | 0.8332 | 0.7933   | 0.8657 | 0.8552 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0