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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: InstaDeepAI/nucleotide-transformer-v2-100m-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-v2-100m-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-v2-100m-multi-species_ft_BioS74_1kbpHG19_DHSs_H3K27AC |
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This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-100m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-100m-multi-species) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4219 |
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- F1 Score: 0.8409 |
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- Precision: 0.8361 |
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- Recall: 0.8458 |
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- Accuracy: 0.8325 |
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- Auc: 0.9111 |
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- Prc: 0.9049 |
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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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| 0.5544 | 0.1314 | 500 | 0.5244 | 0.7566 | 0.7764 | 0.7378 | 0.7515 | 0.8400 | 0.8313 | |
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| 0.5015 | 0.2629 | 1000 | 0.4997 | 0.7709 | 0.824 | 0.7243 | 0.7747 | 0.8691 | 0.8632 | |
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| 0.4614 | 0.3943 | 1500 | 0.4311 | 0.8219 | 0.8011 | 0.8438 | 0.8086 | 0.8891 | 0.8855 | |
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| 0.4294 | 0.5258 | 2000 | 0.4329 | 0.8290 | 0.7769 | 0.8885 | 0.8080 | 0.8935 | 0.8874 | |
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| 0.4045 | 0.6572 | 2500 | 0.4249 | 0.8331 | 0.7673 | 0.9111 | 0.8088 | 0.8987 | 0.8937 | |
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| 0.432 | 0.7886 | 3000 | 0.4033 | 0.8261 | 0.8244 | 0.8277 | 0.8175 | 0.8993 | 0.8931 | |
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| 0.4148 | 0.9201 | 3500 | 0.4032 | 0.8381 | 0.8156 | 0.8619 | 0.8257 | 0.9057 | 0.8986 | |
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| 0.4127 | 1.0515 | 4000 | 0.4252 | 0.8196 | 0.8417 | 0.7986 | 0.8159 | 0.9026 | 0.8932 | |
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| 0.3807 | 1.1830 | 4500 | 0.3981 | 0.8441 | 0.8146 | 0.8759 | 0.8307 | 0.9046 | 0.8971 | |
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| 0.3711 | 1.3144 | 5000 | 0.4066 | 0.8430 | 0.8130 | 0.8754 | 0.8293 | 0.9011 | 0.8941 | |
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| 0.363 | 1.4458 | 5500 | 0.5295 | 0.8012 | 0.8737 | 0.7398 | 0.8078 | 0.9053 | 0.9037 | |
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| 0.3624 | 1.5773 | 6000 | 0.4219 | 0.8409 | 0.8361 | 0.8458 | 0.8325 | 0.9111 | 0.9049 | |
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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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