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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: InstaDeepAI/nucleotide-transformer-v2-50m-multi-species |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- accuracy |
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model-index: |
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- name: nucleotide-transformer-v2-50m-multi-species_ft_BioS73_1kbpHG19_DHSs_H3K27AC |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# nucleotide-transformer-v2-50m-multi-species_ft_BioS73_1kbpHG19_DHSs_H3K27AC |
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This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-50m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-50m-multi-species) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5982 |
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- F1 Score: 0.8659 |
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- Precision: 0.8638 |
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- Recall: 0.8680 |
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- Accuracy: 0.8565 |
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- Auc: 0.9221 |
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- Prc: 0.9108 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Score | Precision | Recall | Accuracy | Auc | Prc | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:| |
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| 0.4842 | 0.1864 | 500 | 0.4405 | 0.8399 | 0.7690 | 0.9253 | 0.8118 | 0.8896 | 0.8855 | |
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| 0.3895 | 0.3727 | 1000 | 0.3708 | 0.8565 | 0.8186 | 0.8980 | 0.8394 | 0.9145 | 0.9127 | |
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| 0.3812 | 0.5591 | 1500 | 0.3732 | 0.8650 | 0.8468 | 0.8841 | 0.8528 | 0.9200 | 0.9179 | |
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| 0.3785 | 0.7454 | 2000 | 0.3683 | 0.8681 | 0.8566 | 0.8799 | 0.8572 | 0.9226 | 0.9183 | |
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| 0.352 | 0.9318 | 2500 | 0.3957 | 0.8678 | 0.8312 | 0.9078 | 0.8524 | 0.9233 | 0.9208 | |
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| 0.3234 | 1.1182 | 3000 | 0.4323 | 0.8701 | 0.8222 | 0.9239 | 0.8528 | 0.9261 | 0.9220 | |
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| 0.2959 | 1.3045 | 3500 | 0.3964 | 0.8749 | 0.8237 | 0.9330 | 0.8576 | 0.9231 | 0.9151 | |
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| 0.3007 | 1.4909 | 4000 | 0.3848 | 0.8715 | 0.8514 | 0.8925 | 0.8595 | 0.9247 | 0.9188 | |
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| 0.3001 | 1.6772 | 4500 | 0.4423 | 0.8790 | 0.8411 | 0.9204 | 0.8647 | 0.9227 | 0.9129 | |
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| 0.3094 | 1.8636 | 5000 | 0.3997 | 0.8737 | 0.8525 | 0.8959 | 0.8617 | 0.9288 | 0.9235 | |
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| 0.2533 | 2.0499 | 5500 | 0.5849 | 0.8611 | 0.8719 | 0.8506 | 0.8535 | 0.9290 | 0.9247 | |
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| 0.2049 | 2.2363 | 6000 | 0.5982 | 0.8659 | 0.8638 | 0.8680 | 0.8565 | 0.9221 | 0.9108 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.0 |
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