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dnabert2_ft_BioS73_1kbpHG19_DHSs_H3K27AC
tanoManzo/dnabert2_ft_BioS73_1kbpHG19_DHSs_H3K27AC
dnabert2_ft_BioS73_1kbpHG19_DHSs_H3K27AC is a text classification model from tanoManzo. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Sep 29, 2024
- Updated
- Sep 29, 2024
- SHA
- 47f331c0
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Model card
From the Hugging Face model README
<!-- 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. -->
dnabert2_ft_BioS73_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of vivym/DNABERT-2-117M on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4759
- F1 Score: 0.8097
- Precision: 0.7215
- Recall: 0.9225
- Accuracy: 0.7685
- Auc: 0.8751
- Prc: 0.8761
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 |
|---|---|---|---|---|---|---|---|---|---|
| 0.5511 | 0.1864 | 500 | 0.5573 | 0.7765 | 0.6445 | 0.9763 | 0.7000 | 0.8604 | 0.8624 |
| 0.5087 | 0.3727 | 1000 | 0.5165 | 0.7894 | 0.8206 | 0.7605 | 0.7835 | 0.8637 | 0.8656 |
| 0.5156 | 0.5591 | 1500 | 0.5062 | 0.8117 | 0.7655 | 0.8638 | 0.7861 | 0.8675 | 0.8683 |
| 0.5127 | 0.7454 | 2000 | 0.4772 | 0.8160 | 0.7645 | 0.875 | 0.7894 | 0.8679 | 0.8694 |
| 0.4991 | 0.9318 | 2500 | 0.5063 | 0.8103 | 0.7203 | 0.9260 | 0.7685 | 0.8712 | 0.8719 |
| 0.478 | 1.1182 | 3000 | 0.4569 | 0.8113 | 0.7422 | 0.8946 | 0.7779 | 0.8713 | 0.8723 |
| 0.4808 | 1.3045 | 3500 | 0.4585 | 0.8041 | 0.8086 | 0.7996 | 0.7920 | 0.8723 | 0.8738 |
| 0.4873 | 1.4909 | 4000 | 0.4672 | 0.8115 | 0.7297 | 0.9141 | 0.7734 | 0.8744 | 0.8761 |
| 0.4649 | 1.6772 | 4500 | 0.4759 | 0.8097 | 0.7215 | 0.9225 | 0.7685 | 0.8751 | 0.8761 |
Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 2.18.0
- Tokenizers 0.20.0
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Sep 29, 2024
- Updated
- Sep 29, 2024