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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_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-2.5b-multi-species_ft_BioS2_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: 0.3464 |
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- F1 Score: 0.8617 |
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- Precision: 0.8829 |
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- Recall: 0.8414 |
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- Accuracy: 0.8570 |
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- Auc: 0.9374 |
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- Prc: 0.9355 |
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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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| 0.5131 | 0.0841 | 500 | 0.4263 | 0.8248 | 0.7933 | 0.8589 | 0.8069 | 0.8829 | 0.8773 | |
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| 0.453 | 0.1682 | 1000 | 0.4288 | 0.8085 | 0.8500 | 0.7708 | 0.8068 | 0.8988 | 0.8974 | |
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| 0.4295 | 0.2522 | 1500 | 0.4352 | 0.8478 | 0.7642 | 0.9520 | 0.8192 | 0.9110 | 0.9080 | |
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| 0.4114 | 0.3363 | 2000 | 0.3765 | 0.8308 | 0.8743 | 0.7915 | 0.8295 | 0.9198 | 0.9167 | |
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| 0.3881 | 0.4204 | 2500 | 0.4381 | 0.8181 | 0.8932 | 0.7546 | 0.8224 | 0.9225 | 0.9235 | |
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| 0.3833 | 0.5045 | 3000 | 0.3577 | 0.8631 | 0.8023 | 0.9339 | 0.8433 | 0.9255 | 0.9247 | |
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| 0.3849 | 0.5885 | 3500 | 0.3715 | 0.8548 | 0.8392 | 0.8709 | 0.8434 | 0.9223 | 0.9209 | |
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| 0.3506 | 0.6726 | 4000 | 0.4040 | 0.8657 | 0.8452 | 0.8872 | 0.8544 | 0.9288 | 0.9261 | |
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| 0.3708 | 0.7567 | 4500 | 0.3428 | 0.8619 | 0.8731 | 0.8509 | 0.8557 | 0.9329 | 0.9309 | |
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| 0.3581 | 0.8408 | 5000 | 0.4926 | 0.8216 | 0.9180 | 0.7435 | 0.8291 | 0.9347 | 0.9331 | |
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| 0.3629 | 0.9248 | 5500 | 0.3464 | 0.8617 | 0.8829 | 0.8414 | 0.8570 | 0.9374 | 0.9355 | |
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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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