readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - marker-associations-snp-binary-base
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: marker-associations-snp-binary-base
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: marker-associations-snp-binary-base
        type: marker-associations-snp-binary-base
      metrics:
      - name: Precision
        type: precision
        value: 0.9384057971014492
      - name: Recall
        type: recall
        value: 0.9055944055944056
      - name: F1
        type: f1
        value: 0.9217081850533808
      - name: Accuracy
        type: accuracy
        value: 0.9107505070993914
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # marker-associations-snp-binary-base
  
  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.
  It achieves the following results on the evaluation set:
  - Loss: 0.4027
  - Precision: 0.9384
  - Recall: 0.9056
  - F1: 0.9217
  - Accuracy: 0.9108
  - Auc: 0.9578
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 1
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy | Auc    |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|
  | No log        | 1.0   | 153  | 0.2776          | 0.9       | 0.9441 | 0.9215 | 0.9067   | 0.9613 |
  | No log        | 2.0   | 306  | 0.4380          | 0.9126    | 0.9126 | 0.9126 | 0.8986   | 0.9510 |
  | No log        | 3.0   | 459  | 0.4027          | 0.9384    | 0.9056 | 0.9217 | 0.9108   | 0.9578 |
  | 0.2215        | 4.0   | 612  | 0.3547          | 0.9449    | 0.8986 | 0.9211 | 0.9108   | 0.9642 |
  | 0.2215        | 5.0   | 765  | 0.4465          | 0.9107    | 0.9266 | 0.9185 | 0.9047   | 0.9636 |
  | 0.2215        | 6.0   | 918  | 0.5770          | 0.8970    | 0.9441 | 0.9199 | 0.9047   | 0.9666 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.0+cu111
  - Tokenizers 0.10.3
  