readme: ---
  license: afl-3.0
  ---
  
  <strong>Example on how to load and use BOW-BERT: <strong>
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  # load model
  model = AutoModelForSequenceClassification.from_pretrained('dmrau/bow-bert')
  # load tokenizer
  tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
  
  # tokenize query and passage and concatenate them
  inp = tokenizer(['this is a query','query a is this'], ['this is a passage', 'passage a is this'], return_tensors='pt')
  # get estimated score
  print('score', model(**inp).logits[:, 1])
  
  ### outputs identical scores for different 
  ### word orders as the model is order invariant:
  # scores: [-2.9463, -2.9463]
  ```
  
  
  
  <strong> Cite us:<strong> 
  
  
  ```
  @article{rau2022role,
    title={The Role of Complex NLP in Transformers for Text Ranking?},
    author={Rau, David and Kamps, Jaap},
    journal={arXiv preprint arXiv:2207.02522},
    year={2022}
  }
  ```
  