readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - pritamdeka/cord-19-abstract
  metrics:
  - accuracy
  model-index:
  - name: pubmedbert-abstract-cord19
    results:
    - task:
        name: Masked Language Modeling
        type: fill-mask
      dataset:
        name: pritamdeka/cord-19-abstract
        type: pritamdeka/cord-19-abstract
        args: fulltext
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7246798699728464
  ---
  
  <!-- 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-abstract-cord19-v2
  
  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-abstract](https://huggingface.co/datasets/pritamdeka/cord-19-abstract) dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2371
  - Accuracy: 0.7247
  
  ## 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: 42
  - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 10000
  - num_epochs: 4.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.27          | 0.53  | 5000  | 1.2425          | 0.7236   |
  | 1.2634        | 1.06  | 10000 | 1.3123          | 0.7141   |
  | 1.3041        | 1.59  | 15000 | 1.3583          | 0.7072   |
  | 1.3829        | 2.12  | 20000 | 1.3590          | 0.7121   |
  | 1.3069        | 2.65  | 25000 | 1.3506          | 0.7154   |
  | 1.2921        | 3.18  | 30000 | 1.3448          | 0.7160   |
  | 1.2731        | 3.7   | 35000 | 1.3375          | 0.7178   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  