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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_BioS74_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_BioS74_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: 0.4277 |
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- F1 Score: 0.8355 |
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- Precision: 0.8318 |
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- Recall: 0.8393 |
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- Accuracy: 0.8270 |
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- Auc: 0.9066 |
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- Prc: 0.9000 |
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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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| 0.5314 | 0.1314 | 500 | 0.4688 | 0.8060 | 0.7652 | 0.8513 | 0.7854 | 0.8552 | 0.8400 | |
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| 0.4807 | 0.2629 | 1000 | 0.4967 | 0.7824 | 0.8433 | 0.7298 | 0.7875 | 0.8783 | 0.8671 | |
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| 0.4541 | 0.3943 | 1500 | 0.4272 | 0.8177 | 0.8166 | 0.8187 | 0.8088 | 0.8900 | 0.8819 | |
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| 0.4213 | 0.5258 | 2000 | 0.4602 | 0.8361 | 0.7841 | 0.8955 | 0.8162 | 0.8916 | 0.8819 | |
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| 0.4085 | 0.6572 | 2500 | 0.4336 | 0.8363 | 0.7528 | 0.9407 | 0.8073 | 0.8959 | 0.8890 | |
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| 0.4383 | 0.7886 | 3000 | 0.4106 | 0.8240 | 0.8238 | 0.8242 | 0.8157 | 0.8978 | 0.8913 | |
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| 0.4237 | 0.9201 | 3500 | 0.4270 | 0.8372 | 0.8043 | 0.8729 | 0.8222 | 0.9017 | 0.8957 | |
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| 0.4121 | 1.0515 | 4000 | 0.4787 | 0.7913 | 0.8662 | 0.7283 | 0.7988 | 0.9028 | 0.8948 | |
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| 0.3789 | 1.1830 | 4500 | 0.4081 | 0.8379 | 0.8139 | 0.8634 | 0.8251 | 0.8999 | 0.8889 | |
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| 0.3736 | 1.3144 | 5000 | 0.4348 | 0.8344 | 0.8167 | 0.8528 | 0.8228 | 0.9020 | 0.8951 | |
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| 0.3655 | 1.4458 | 5500 | 0.4388 | 0.8153 | 0.8509 | 0.7825 | 0.8144 | 0.9056 | 0.8995 | |
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| 0.3597 | 1.5773 | 6000 | 0.4277 | 0.8355 | 0.8318 | 0.8393 | 0.8270 | 0.9066 | 0.9000 | |
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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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