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dnabert2_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot
tanoManzo/dnabert2_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot
dnabert2_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot 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
- Nov 19, 2024
- Updated
- Nov 19, 2024
- SHA
- 583ad5a1
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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_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot
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: 1.3587
- F1 Score: 0.7246
- Precision: 0.6944
- Recall: 0.7576
- Accuracy: 0.6780
- Auc: 0.7110
- Prc: 0.7491
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 |
|---|---|---|---|---|---|---|---|---|---|
| 0.5413 | 8.3333 | 500 | 0.8825 | 0.7324 | 0.6842 | 0.7879 | 0.6780 | 0.7279 | 0.7724 |
| 0.3411 | 16.6667 | 1000 | 1.3587 | 0.7246 | 0.6944 | 0.7576 | 0.6780 | 0.7110 | 0.7491 |
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
- Nov 19, 2024
- Updated
- Nov 19, 2024