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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_BioS45_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_BioS45_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: 0.4618 |
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- F1 Score: 0.8488 |
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- Precision: 0.8116 |
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- Recall: 0.8895 |
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- Accuracy: 0.8347 |
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- Auc: 0.9100 |
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- Prc: 0.9051 |
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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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| 0.5436 | 0.2103 | 500 | 0.4336 | 0.8161 | 0.8074 | 0.825 | 0.8061 | 0.8826 | 0.8798 | |
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| 0.4386 | 0.4207 | 1000 | 0.4171 | 0.8423 | 0.7929 | 0.8984 | 0.8246 | 0.9017 | 0.8982 | |
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| 0.402 | 0.6310 | 1500 | 0.3979 | 0.8486 | 0.7977 | 0.9065 | 0.8313 | 0.9083 | 0.9055 | |
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| 0.4019 | 0.8414 | 2000 | 0.3865 | 0.8505 | 0.7961 | 0.9129 | 0.8326 | 0.9060 | 0.9000 | |
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| 0.382 | 1.0517 | 2500 | 0.4091 | 0.8513 | 0.8169 | 0.8887 | 0.8380 | 0.9131 | 0.9049 | |
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| 0.3297 | 1.2621 | 3000 | 0.4890 | 0.8590 | 0.8143 | 0.9089 | 0.8443 | 0.9105 | 0.8922 | |
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| 0.3417 | 1.4724 | 3500 | 0.4246 | 0.8517 | 0.8355 | 0.8685 | 0.8422 | 0.9138 | 0.9072 | |
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| 0.3447 | 1.6828 | 4000 | 0.4299 | 0.8505 | 0.8408 | 0.8605 | 0.8422 | 0.9138 | 0.9052 | |
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| 0.3384 | 1.8931 | 4500 | 0.4618 | 0.8488 | 0.8116 | 0.8895 | 0.8347 | 0.9100 | 0.9051 | |
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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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