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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: InstaDeepAI/nucleotide-transformer-v2-250m-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-v2-250m-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-v2-250m-multi-species_ft_BioS45_1kbpHG19_DHSs_H3K27AC |
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This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-250m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-250m-multi-species) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4434 |
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- F1 Score: 0.8539 |
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- Precision: 0.8522 |
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- Recall: 0.8556 |
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- Accuracy: 0.8473 |
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- Auc: 0.9230 |
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- Prc: 0.9160 |
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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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| 0.5396 | 0.2103 | 500 | 0.4921 | 0.7677 | 0.8407 | 0.7065 | 0.7770 | 0.8663 | 0.8653 | |
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| 0.45 | 0.4207 | 1000 | 0.4548 | 0.8362 | 0.7580 | 0.9323 | 0.8094 | 0.9049 | 0.9023 | |
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| 0.4027 | 0.6310 | 1500 | 0.4014 | 0.8472 | 0.7861 | 0.9185 | 0.8271 | 0.9120 | 0.9072 | |
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| 0.3993 | 0.8414 | 2000 | 0.3715 | 0.8561 | 0.8487 | 0.8637 | 0.8485 | 0.9153 | 0.9089 | |
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| 0.3709 | 1.0517 | 2500 | 0.4005 | 0.8647 | 0.8441 | 0.8863 | 0.8553 | 0.9232 | 0.9173 | |
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| 0.3206 | 1.2621 | 3000 | 0.4735 | 0.8517 | 0.8355 | 0.8685 | 0.8422 | 0.9172 | 0.9132 | |
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| 0.3259 | 1.4724 | 3500 | 0.4264 | 0.8612 | 0.8471 | 0.8758 | 0.8528 | 0.9234 | 0.9195 | |
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| 0.316 | 1.6828 | 4000 | 0.4434 | 0.8539 | 0.8522 | 0.8556 | 0.8473 | 0.9230 | 0.9160 | |
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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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