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DNABERT-2-117M_ft_BioS45_1kbpHG19_DHSs_H3K27AC
tanoManzo/DNABERT-2-117M_ft_BioS45_1kbpHG19_DHSs_H3K27AC
DNABERT-2-117M_ft_BioS45_1kbpHG19_DHSs_H3K27AC is a machine learning model from tanoManzo. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Model type
- bert
- Access
- Public
- Created
- Sep 23, 2024
- Updated
- Sep 23, 2024
- SHA
- 6026303c
Base models
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. -->
DNABERT-2-117M_ft_BioS45_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of zhihan1996/DNABERT-2-117M on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7663
- F1 Score: 0.6154
- Precision: 0.8763
- Recall: 0.4742
- Accuracy: 0.6908
- Auc: 0.8534
- Prc: 0.8406
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 |
|---|---|---|---|---|---|---|---|---|---|
| 0.7033 | 0.4205 | 500 | 0.5780 | 0.7163 | 0.7317 | 0.7016 | 0.7101 | 0.7860 | 0.7818 |
| 0.5929 | 0.8410 | 1000 | 0.5370 | 0.7339 | 0.7525 | 0.7161 | 0.7291 | 0.8066 | 0.7979 |
| 0.5608 | 1.2616 | 1500 | 0.5352 | 0.7532 | 0.7508 | 0.7556 | 0.7417 | 0.8204 | 0.8097 |
| 0.5471 | 1.6821 | 2000 | 0.5455 | 0.6955 | 0.8211 | 0.6032 | 0.7244 | 0.8317 | 0.8200 |
| 0.5252 | 2.1026 | 2500 | 0.5145 | 0.7897 | 0.7367 | 0.8508 | 0.7636 | 0.8384 | 0.8261 |
| 0.4833 | 2.5231 | 3000 | 0.5229 | 0.7976 | 0.7146 | 0.9024 | 0.7610 | 0.8469 | 0.8351 |
| 0.5161 | 2.9437 | 3500 | 0.4988 | 0.7593 | 0.8049 | 0.7185 | 0.7623 | 0.8506 | 0.8393 |
| 0.4921 | 3.3642 | 4000 | 0.4784 | 0.7967 | 0.7447 | 0.8565 | 0.7720 | 0.8541 | 0.8426 |
| 0.4779 | 3.7847 | 4500 | 0.7663 | 0.6154 | 0.8763 | 0.4742 | 0.6908 | 0.8534 | 0.8406 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
- Type
- bert
- Created
- Sep 23, 2024
- Updated
- Sep 23, 2024