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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_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-v2-500m-multi-species_ft_BioS74_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: 2.0901 |
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- F1 Score: 0.8408 |
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- Precision: 0.8543 |
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- Recall: 0.8277 |
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- Accuracy: 0.8359 |
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- Auc: 0.9180 |
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- Prc: 0.9190 |
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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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| 0.5159 | 0.2629 | 500 | 0.4336 | 0.8179 | 0.7917 | 0.8458 | 0.8028 | 0.8805 | 0.8705 | |
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| 0.4166 | 0.5258 | 1000 | 0.4006 | 0.8434 | 0.7908 | 0.9036 | 0.8243 | 0.9079 | 0.9051 | |
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| 0.3854 | 0.7886 | 1500 | 0.3993 | 0.8481 | 0.7691 | 0.9453 | 0.8228 | 0.9127 | 0.9072 | |
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| 0.3755 | 1.0515 | 2000 | 0.3677 | 0.8348 | 0.8600 | 0.8112 | 0.8320 | 0.9252 | 0.9258 | |
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| 0.2996 | 1.3144 | 2500 | 0.3570 | 0.8506 | 0.8478 | 0.8533 | 0.8430 | 0.9268 | 0.9291 | |
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| 0.2827 | 1.5773 | 3000 | 0.3578 | 0.8564 | 0.8504 | 0.8624 | 0.8485 | 0.9305 | 0.9303 | |
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| 0.285 | 1.8402 | 3500 | 0.3630 | 0.8386 | 0.8674 | 0.8117 | 0.8364 | 0.9270 | 0.9280 | |
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| 0.2425 | 2.1030 | 4000 | 0.9870 | 0.8391 | 0.8578 | 0.8212 | 0.8351 | 0.9202 | 0.9203 | |
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| 0.4127 | 2.3659 | 4500 | 2.0901 | 0.8408 | 0.8543 | 0.8277 | 0.8359 | 0.9180 | 0.9190 | |
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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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