readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - pritamdeka/cord-19-fulltext
  metrics:
  - accuracy
  model-index:
  - name: pubmedbert-fulltext-cord19
    results:
    - task:
        name: Masked Language Modeling
        type: fill-mask
      dataset:
        name: pritamdeka/cord-19-fulltext
        type: pritamdeka/cord-19-fulltext
        args: fulltext
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7175316733550737
  ---
  
  <!-- 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. -->
  
  # pubmedbert-fulltext-cord19
  
  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 pritamdeka/cord-19-fulltext dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2667
  - Accuracy: 0.7175
  
  ## Model description
  
  The model has been trained using a maximum train sample size of 300K and evaluation size of 25K due to GPU limitations
  
  ## 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: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 10000
  - num_epochs: 3.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.7985        | 0.27  | 5000  | 1.2710          | 0.7176   |
  | 1.7542        | 0.53  | 10000 | 1.3359          | 0.7070   |
  | 1.7462        | 0.8   | 15000 | 1.3489          | 0.7034   |
  | 1.8371        | 1.07  | 20000 | 1.4361          | 0.6891   |
  | 1.7102        | 1.33  | 25000 | 1.3502          | 0.7039   |
  | 1.6596        | 1.6   | 30000 | 1.3341          | 0.7065   |
  | 1.6265        | 1.87  | 35000 | 1.3228          | 0.7087   |
  | 1.605         | 2.13  | 40000 | 1.3079          | 0.7099   |
  | 1.5731        | 2.4   | 45000 | 1.2986          | 0.7121   |
  | 1.5602        | 2.67  | 50000 | 1.2929          | 0.7136   |
  | 1.5447        | 2.93  | 55000 | 1.2875          | 0.7143   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  