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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_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-2.5b-multi-species_ft_BioS45_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: 0.7950 |
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- F1 Score: 0.8559 |
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- Precision: 0.8234 |
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- Recall: 0.8911 |
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- Accuracy: 0.8435 |
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- Auc: 0.9171 |
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- Prc: 0.9117 |
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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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| 0.5207 | 0.2103 | 500 | 0.4059 | 0.8319 | 0.8038 | 0.8621 | 0.8183 | 0.8975 | 0.8945 | |
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| 0.429 | 0.4207 | 1000 | 0.4149 | 0.8454 | 0.7824 | 0.9194 | 0.8246 | 0.9137 | 0.9091 | |
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| 0.4215 | 0.6310 | 1500 | 0.4626 | 0.8318 | 0.7368 | 0.9548 | 0.7985 | 0.9188 | 0.9187 | |
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| 0.3994 | 0.8414 | 2000 | 0.3757 | 0.8555 | 0.7733 | 0.9573 | 0.8313 | 0.9264 | 0.9226 | |
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| 0.3733 | 1.0517 | 2500 | 0.4529 | 0.8462 | 0.8658 | 0.8274 | 0.8431 | 0.9240 | 0.9215 | |
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| 0.3 | 1.2621 | 3000 | 0.5616 | 0.8585 | 0.8464 | 0.8710 | 0.8502 | 0.9248 | 0.9222 | |
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| 0.3006 | 1.4724 | 3500 | 0.5047 | 0.8498 | 0.8940 | 0.8097 | 0.8507 | 0.9298 | 0.9287 | |
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| 0.3008 | 1.6828 | 4000 | 0.4289 | 0.8614 | 0.8415 | 0.8823 | 0.8519 | 0.9233 | 0.9218 | |
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| 0.2882 | 1.8931 | 4500 | 0.4380 | 0.8634 | 0.8323 | 0.8968 | 0.8519 | 0.9263 | 0.9232 | |
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| 0.2014 | 2.1035 | 5000 | 0.6433 | 0.8624 | 0.8628 | 0.8621 | 0.8565 | 0.9240 | 0.9218 | |
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| 0.1118 | 2.3138 | 5500 | 0.7087 | 0.8670 | 0.8591 | 0.875 | 0.8599 | 0.9272 | 0.9236 | |
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| 0.1406 | 2.5242 | 6000 | 0.9509 | 0.8427 | 0.8757 | 0.8121 | 0.8418 | 0.9252 | 0.9212 | |
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| 0.1304 | 2.7345 | 6500 | 0.7386 | 0.8670 | 0.8398 | 0.8960 | 0.8565 | 0.9265 | 0.9213 | |
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| 0.1515 | 2.9449 | 7000 | 0.7950 | 0.8559 | 0.8234 | 0.8911 | 0.8435 | 0.9171 | 0.9117 | |
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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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