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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_BioS2_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_BioS2_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: 2.0128 |
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- F1 Score: 0.8753 |
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- Precision: 0.8186 |
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- Recall: 0.9403 |
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- Accuracy: 0.8603 |
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- Auc: 0.9383 |
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- Prc: 0.9326 |
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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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| 0.534 | 0.1681 | 500 | 0.4570 | 0.7800 | 0.8228 | 0.7414 | 0.7820 | 0.8740 | 0.8666 | |
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| 0.3972 | 0.3361 | 1000 | 0.3836 | 0.8574 | 0.8086 | 0.9126 | 0.8418 | 0.9181 | 0.9115 | |
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| 0.3583 | 0.5042 | 1500 | 0.3394 | 0.8617 | 0.8135 | 0.9158 | 0.8467 | 0.9321 | 0.9322 | |
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| 0.3405 | 0.6723 | 2000 | 0.3418 | 0.8664 | 0.8789 | 0.8542 | 0.8627 | 0.9379 | 0.9360 | |
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| 0.323 | 0.8403 | 2500 | 0.3204 | 0.8565 | 0.9085 | 0.8101 | 0.8585 | 0.9453 | 0.9453 | |
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| 0.3109 | 1.0084 | 3000 | 0.3163 | 0.8774 | 0.8875 | 0.8675 | 0.8736 | 0.9471 | 0.9477 | |
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| 0.2315 | 1.1765 | 3500 | 0.3899 | 0.8803 | 0.8255 | 0.9429 | 0.8664 | 0.9473 | 0.9447 | |
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| 0.2265 | 1.3445 | 4000 | 0.3476 | 0.8816 | 0.8476 | 0.9184 | 0.8714 | 0.9483 | 0.9489 | |
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| 0.2286 | 1.5126 | 4500 | 0.3797 | 0.8587 | 0.9147 | 0.8091 | 0.8612 | 0.9445 | 0.9471 | |
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| 0.23 | 1.6807 | 5000 | 0.3251 | 0.8845 | 0.8714 | 0.8981 | 0.8778 | 0.9486 | 0.9497 | |
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| 0.2271 | 1.8487 | 5500 | 0.3160 | 0.8836 | 0.8579 | 0.9110 | 0.8750 | 0.9489 | 0.9489 | |
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| 0.2199 | 2.0168 | 6000 | 0.4896 | 0.8836 | 0.8300 | 0.9445 | 0.8703 | 0.9477 | 0.9459 | |
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| 0.2465 | 2.1849 | 6500 | 2.0128 | 0.8753 | 0.8186 | 0.9403 | 0.8603 | 0.9383 | 0.9326 | |
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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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