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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: InstaDeepAI/nucleotide-transformer-2.5b-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-2.5b-multi-species_ft_Hepg2_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-2.5b-multi-species_ft_Hepg2_1kbpHG19_DHSs_H3K27AC |
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This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-2.5b-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-2.5b-multi-species) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.2344 |
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- F1 Score: 0.8878 |
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- Precision: 0.8690 |
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- Recall: 0.9074 |
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- Accuracy: 0.8836 |
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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: 24 |
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- eval_batch_size: 24 |
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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: 10 |
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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 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
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| 0.3737 | 0.5593 | 500 | 0.3067 | 0.8745 | 0.8377 | 0.9147 | 0.8668 | |
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| 0.292 | 1.1186 | 1000 | 0.3043 | 0.8852 | 0.8332 | 0.9441 | 0.8757 | |
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| 0.1865 | 1.6779 | 1500 | 0.3219 | 0.8854 | 0.8324 | 0.9456 | 0.8757 | |
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| 0.1744 | 2.2371 | 2000 | 2.0896 | 0.8683 | 0.8712 | 0.8654 | 0.8668 | |
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| 0.2341 | 2.7964 | 2500 | 1.9718 | 0.8887 | 0.8383 | 0.9456 | 0.8799 | |
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| 0.1357 | 3.3557 | 3000 | 2.2402 | 0.8787 | 0.8268 | 0.9375 | 0.8687 | |
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| 0.0779 | 3.9150 | 3500 | 2.0910 | 0.8857 | 0.8664 | 0.9059 | 0.8813 | |
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| 0.0426 | 4.4743 | 4000 | 2.2457 | 0.8724 | 0.8911 | 0.8544 | 0.8731 | |
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| 0.0549 | 5.0336 | 4500 | 1.8462 | 0.8952 | 0.8718 | 0.9199 | 0.8907 | |
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| 0.0331 | 5.5928 | 5000 | 1.9918 | 0.8803 | 0.8790 | 0.8816 | 0.8784 | |
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| 0.0206 | 6.1521 | 5500 | 2.0727 | 0.8847 | 0.8811 | 0.8882 | 0.8825 | |
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| 0.0101 | 6.7114 | 6000 | 2.0343 | 0.8882 | 0.8853 | 0.8912 | 0.8862 | |
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| 0.0099 | 7.2707 | 6500 | 2.1056 | 0.8831 | 0.8914 | 0.875 | 0.8825 | |
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| 0.0058 | 7.8300 | 7000 | 2.3964 | 0.8673 | 0.9036 | 0.8338 | 0.8705 | |
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| 0.0 | 8.3893 | 7500 | 2.2680 | 0.8808 | 0.8814 | 0.8801 | 0.8791 | |
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| 0.0047 | 8.9485 | 8000 | 2.1908 | 0.8863 | 0.8730 | 0.9 | 0.8828 | |
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| 0.0 | 9.5078 | 8500 | 2.2344 | 0.8878 | 0.8690 | 0.9074 | 0.8836 | |
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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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