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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: InstaDeepAI/nucleotide-transformer-v2-500m-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-v2-500m-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-v2-500m-multi-species_ft_BioS73_1kbpHG19_DHSs_H3K27AC |
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This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-500m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-500m-multi-species) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7558 |
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- F1 Score: 0.8878 |
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- Precision: 0.8944 |
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- Recall: 0.8813 |
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- Accuracy: 0.8811 |
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- Auc: 0.9434 |
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- Prc: 0.9416 |
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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: 16 |
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- eval_batch_size: 16 |
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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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| 0.4249 | 0.3726 | 500 | 0.3524 | 0.8663 | 0.8348 | 0.9001 | 0.8517 | 0.9271 | 0.9269 | |
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| 0.3396 | 0.7452 | 1000 | 0.3162 | 0.8815 | 0.8701 | 0.8932 | 0.8718 | 0.9411 | 0.9405 | |
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| 0.2838 | 1.1177 | 1500 | 0.2938 | 0.8907 | 0.8666 | 0.9162 | 0.8800 | 0.9516 | 0.9533 | |
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| 0.244 | 1.4903 | 2000 | 0.2702 | 0.9001 | 0.8852 | 0.9155 | 0.8915 | 0.9528 | 0.9523 | |
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| 0.2207 | 1.8629 | 2500 | 0.3141 | 0.8993 | 0.8656 | 0.9358 | 0.8882 | 0.9526 | 0.9517 | |
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| 0.2753 | 2.2355 | 3000 | 1.4071 | 0.9004 | 0.8611 | 0.9434 | 0.8886 | 0.9400 | 0.9247 | |
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| 0.4227 | 2.6080 | 3500 | 1.5821 | 0.8953 | 0.9069 | 0.8841 | 0.8897 | 0.9529 | 0.9540 | |
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| 0.4015 | 2.9806 | 4000 | 1.6699 | 0.8918 | 0.8884 | 0.8953 | 0.8841 | 0.9511 | 0.9516 | |
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| 0.2124 | 3.3532 | 4500 | 1.7558 | 0.8878 | 0.8944 | 0.8813 | 0.8811 | 0.9434 | 0.9416 | |
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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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