readme: ---
  license: apache-2.0
  ---
  
  # InformBERT
  
  ## Introduction
  
  InformBERT is pretrained using variable masking strategy, where informative tokens are masked more frequently compared to other tokens. InformBERT outperforms random masking based pretrained models on the factual recall benchmark LAMA and extractive question answering benchmark SQuAD.
  
  More detail: https://arxiv.org/abs/2210.11771
  
  ## How to load
  ```Python
  from transformers import BertTokenizer, AutoModel
  tokenizer = BertTokenizer.from_pretrained("nsadeq/InformBERT")
  model = AutoModel.from_pretrained("nsadeq/InformBERT")
  
  from transformers import pipeline
  unmasker = pipeline('fill-mask', model='nsadeq/InformBERT',tokenizer=tokenizer)
  unmasker("SpeedWeek is an American television program on [MASK].")
  ```
  
  ## Citation
  
  ```bibtex
  @misc{https://doi.org/10.48550/arxiv.2210.11771,
    doi = {10.48550/ARXIV.2210.11771},
    url = {https://arxiv.org/abs/2210.11771},
    author = {Sadeq, Nafis and Xu, Canwen and McAuley, Julian},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, 
    title = {InforMask: Unsupervised Informative Masking for Language Model Pretraining},
    publisher = {arXiv},
    year = {2022},
    copyright = {arXiv.org perpetual, non-exclusive license}
  }
  ```