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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_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-v2-500m-multi-species_ft_BioS45_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: 1.2234 |
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- F1 Score: 0.8240 |
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- Precision: 0.8645 |
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- Recall: 0.7871 |
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- Accuracy: 0.8246 |
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- Auc: 0.9119 |
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- Prc: 0.9103 |
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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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| 0.4976 | 0.4205 | 500 | 0.4129 | 0.8285 | 0.7809 | 0.8823 | 0.8094 | 0.8977 | 0.8961 | |
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| 0.384 | 0.8410 | 1000 | 0.3673 | 0.8526 | 0.8023 | 0.9097 | 0.8359 | 0.9206 | 0.9183 | |
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| 0.3235 | 1.2616 | 1500 | 0.3902 | 0.8505 | 0.8643 | 0.8371 | 0.8464 | 0.9269 | 0.9284 | |
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| 0.2866 | 1.6821 | 2000 | 0.3665 | 0.8623 | 0.8514 | 0.8734 | 0.8544 | 0.9286 | 0.9270 | |
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| 0.2547 | 2.1026 | 2500 | 0.7526 | 0.8592 | 0.8003 | 0.9274 | 0.8414 | 0.9245 | 0.9232 | |
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| 0.316 | 2.5231 | 3000 | 1.5948 | 0.8466 | 0.8614 | 0.8323 | 0.8427 | 0.9224 | 0.9239 | |
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| 0.4261 | 2.9437 | 3500 | 1.2234 | 0.8240 | 0.8645 | 0.7871 | 0.8246 | 0.9119 | 0.9103 | |
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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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