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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: InstaDeepAI/nucleotide-transformer-2.5b-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-2.5b-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-2.5b-multi-species_ft_BioS74_1kbpHG19_DHSs_H3K27AC |
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This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-2.5b-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-2.5b-multi-species) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6420 |
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- F1 Score: 0.8190 |
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- Precision: 0.8800 |
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- Recall: 0.7659 |
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- Accuracy: 0.8228 |
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- Auc: 0.9177 |
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- Prc: 0.9208 |
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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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| 0.5322 | 0.1314 | 500 | 0.4247 | 0.8242 | 0.8041 | 0.8453 | 0.8112 | 0.8883 | 0.8826 | |
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| 0.4778 | 0.2629 | 1000 | 0.5435 | 0.6778 | 0.9073 | 0.5409 | 0.7307 | 0.8958 | 0.8905 | |
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| 0.4503 | 0.3943 | 1500 | 0.3962 | 0.8223 | 0.8531 | 0.7936 | 0.8204 | 0.9106 | 0.9098 | |
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| 0.4075 | 0.5258 | 2000 | 0.4478 | 0.8457 | 0.7944 | 0.9041 | 0.8272 | 0.9127 | 0.9153 | |
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| 0.3929 | 0.6572 | 2500 | 0.3822 | 0.8488 | 0.7917 | 0.9146 | 0.8293 | 0.9193 | 0.9210 | |
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| 0.4237 | 0.7886 | 3000 | 0.3594 | 0.8401 | 0.8435 | 0.8368 | 0.8333 | 0.9193 | 0.9206 | |
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| 0.3992 | 0.9201 | 3500 | 0.3716 | 0.8437 | 0.8376 | 0.8498 | 0.8351 | 0.9221 | 0.9231 | |
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| 0.3819 | 1.0515 | 4000 | 0.6420 | 0.8190 | 0.8800 | 0.7659 | 0.8228 | 0.9177 | 0.9208 | |
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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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