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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- marker-associations-snp-binary-base |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: marker-associations-snp-binary-base |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: marker-associations-snp-binary-base |
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type: marker-associations-snp-binary-base |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9384057971014492 |
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- name: Recall |
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type: recall |
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value: 0.9055944055944056 |
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- name: F1 |
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type: f1 |
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value: 0.9217081850533808 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9107505070993914 |
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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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# marker-associations-snp-binary-base |
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This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the marker-associations-snp-binary-base dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4027 |
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- Precision: 0.9384 |
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- Recall: 0.9056 |
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- F1: 0.9217 |
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- Accuracy: 0.9108 |
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- Auc: 0.9578 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 1 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:| |
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| No log | 1.0 | 153 | 0.2776 | 0.9 | 0.9441 | 0.9215 | 0.9067 | 0.9613 | |
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| No log | 2.0 | 306 | 0.4380 | 0.9126 | 0.9126 | 0.9126 | 0.8986 | 0.9510 | |
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| No log | 3.0 | 459 | 0.4027 | 0.9384 | 0.9056 | 0.9217 | 0.9108 | 0.9578 | |
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| 0.2215 | 4.0 | 612 | 0.3547 | 0.9449 | 0.8986 | 0.9211 | 0.9108 | 0.9642 | |
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| 0.2215 | 5.0 | 765 | 0.4465 | 0.9107 | 0.9266 | 0.9185 | 0.9047 | 0.9636 | |
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| 0.2215 | 6.0 | 918 | 0.5770 | 0.8970 | 0.9441 | 0.9199 | 0.9047 | 0.9666 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.9.0+cu111 |
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- Tokenizers 0.10.3 |
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