---
base_model: zhihan1996/DNABERT-2-117M
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: DNABERT-2-117M_ft_BioS2_1kbpHG19_DHSs_H3K27AC
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# DNABERT-2-117M_ft_BioS2_1kbpHG19_DHSs_H3K27AC

This model is a fine-tuned version of [zhihan1996/DNABERT-2-117M](https://huggingface.co/zhihan1996/DNABERT-2-117M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4620
- F1 Score: 0.7974
- Precision: 0.8134
- Recall: 0.7819
- Accuracy: 0.7933
- Auc: 0.8741
- Prc: 0.8601

## 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    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.706         | 0.1680 | 500  | 0.6216          | 0.6107   | 0.7600    | 0.5105 | 0.6616   | 0.7690 | 0.7525 |
| 0.614         | 0.3360 | 1000 | 0.5784          | 0.7600   | 0.6685    | 0.8805 | 0.7108   | 0.7836 | 0.7638 |
| 0.5795        | 0.5040 | 1500 | 0.5409          | 0.7576   | 0.6964    | 0.8307 | 0.7236   | 0.7938 | 0.7723 |
| 0.557         | 0.6720 | 2000 | 0.5898          | 0.7622   | 0.6293    | 0.9661 | 0.6864   | 0.8063 | 0.7838 |
| 0.561         | 0.8401 | 2500 | 0.5143          | 0.7770   | 0.7298    | 0.8307 | 0.7520   | 0.8183 | 0.7953 |
| 0.5313        | 1.0081 | 3000 | 0.4928          | 0.7958   | 0.7300    | 0.8746 | 0.7666   | 0.8334 | 0.8096 |
| 0.5167        | 1.1761 | 3500 | 0.5086          | 0.7590   | 0.7963    | 0.7250 | 0.7605   | 0.8471 | 0.8266 |
| 0.5052        | 1.3441 | 4000 | 0.4732          | 0.8021   | 0.7578    | 0.8520 | 0.7814   | 0.8533 | 0.8327 |
| 0.4923        | 1.5121 | 4500 | 0.4741          | 0.7898   | 0.7864    | 0.7932 | 0.7804   | 0.8572 | 0.8386 |
| 0.493         | 1.6801 | 5000 | 0.4683          | 0.7821   | 0.7995    | 0.7654 | 0.7782   | 0.8618 | 0.8431 |
| 0.4834        | 1.8481 | 5500 | 0.4522          | 0.8100   | 0.7702    | 0.8543 | 0.7916   | 0.8638 | 0.8465 |
| 0.4871        | 2.0161 | 6000 | 0.4527          | 0.8160   | 0.7475    | 0.8982 | 0.7893   | 0.8665 | 0.8500 |
| 0.457         | 2.1841 | 6500 | 0.6625          | 0.7653   | 0.6231    | 0.9916 | 0.6838   | 0.8679 | 0.8530 |
| 0.4685        | 2.3522 | 7000 | 0.5230          | 0.7281   | 0.8393    | 0.6430 | 0.7503   | 0.8681 | 0.8521 |
| 0.4567        | 2.5202 | 7500 | 0.4433          | 0.8198   | 0.7557    | 0.8956 | 0.7952   | 0.8702 | 0.8533 |
| 0.4506        | 2.6882 | 8000 | 0.4520          | 0.8208   | 0.7520    | 0.9034 | 0.7948   | 0.8721 | 0.8580 |
| 0.4443        | 2.8562 | 8500 | 0.4590          | 0.8029   | 0.7963    | 0.8097 | 0.7933   | 0.8730 | 0.8581 |
| 0.4433        | 3.0242 | 9000 | 0.5848          | 0.7530   | 0.8380    | 0.6837 | 0.7668   | 0.8738 | 0.8592 |
| 0.4355        | 3.1922 | 9500 | 0.4620          | 0.7974   | 0.8134    | 0.7819 | 0.7933   | 0.8741 | 0.8601 |


### Framework versions

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