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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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+
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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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+
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+ # marker-associations-snp-binary-base
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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