readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - marker-associations-binary-base
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: marker-associations-binary-base
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: marker-associations-binary-base
        type: marker-associations-binary-base
      metrics:
      - name: Precision
        type: precision
        value: 0.7981651376146789
      - name: Recall
        type: recall
        value: 0.9560439560439561
      - name: F1
        type: f1
        value: 0.87
      - name: Accuracy
        type: accuracy
        value: 0.8884120171673819
  ---
  
  <!-- 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-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-binary-base dataset.
  It achieves the following results on the evaluation set:
  
  ### Gene Results
  - Precision = 0.808
  - Recall = 0.940
  - F1 = 0.869
  - Accuracy = 0.862
  - AUC = 0.944
  
  ### Chemical Results
  - Precision = 0.774
  - Recall = 1.0
  - F1 = 0.873
  - Accuracy = 0.926
  - AUC = 0.964
  
  ## 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   | 88   | 0.3266          | 0.8191    | 0.8462 | 0.8324 | 0.8670   | 0.9313 |
  | No log        | 2.0   | 176  | 0.3335          | 0.7870    | 0.9341 | 0.8543 | 0.8755   | 0.9465 |
  | No log        | 3.0   | 264  | 0.4243          | 0.7982    | 0.9560 | 0.87   | 0.8884   | 0.9516 |
  | No log        | 4.0   | 352  | 0.5388          | 0.825     | 0.7253 | 0.7719 | 0.8326   | 0.9384 |
  | No log        | 5.0   | 440  | 0.7101          | 0.8537    | 0.7692 | 0.8092 | 0.8584   | 0.9416 |
  | 0.1824        | 6.0   | 528  | 0.6175          | 0.8242    | 0.8242 | 0.8242 | 0.8627   | 0.9478 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.0+cu111
  - Tokenizers 0.10.3
  