readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: CORD19_BERT
    results: []
  ---
  
  # CORD19-BERT
  
  ## How to use
  
  ```python
  from transformers import BertTokenizer, BertModel
  tokenizer = BertTokenizer.from_pretrained('CovRelex-SE/CORD19-BERT')
  model = BertModel.from_pretrained("CovRelex-SE/CORD19-BERT")
  text = "The virus can spread from an infected person’s mouth or nose."
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_input)
  ```
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  