readme: ---
  language: en
  datasets:
  - squad
  ---
  
  ## 
  prophetnet-large-uncased-squad-qg
  Fine-tuned weights(converted from [original fairseq version repo](https://github.com/microsoft/ProphetNet)) for [ProphetNet](https://arxiv.org/abs/2001.04063) on question generation 
  SQuAD 1.1.  
  ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction.  
  ProphetNet is able to predict more future tokens with a n-stream decoder. The original implementation is Fairseq version at [github repo](https://github.com/microsoft/ProphetNet).   
  
  ### Usage
  ```
  from transformers import ProphetNetTokenizer, ProphetNetForConditionalGeneration, ProphetNetConfig
  
  model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/prophetnet-large-uncased-squad-qg')
  tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased-squad-qg')
  
  FACT_TO_GENERATE_QUESTION_FROM = ""Bill Gates [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975."
  
  inputs = tokenizer([FACT_TO_GENERATE_QUESTION_FROM], return_tensors='pt')
  
  # Generate Summary
  question_ids = model.generate(inputs['input_ids'], num_beams=5, early_stopping=True)
  tokenizer.batch_decode(question_ids, skip_special_tokens=True)
  
  # should give: 'along with paul allen, who founded microsoft?'
  ```
  ### Citation
  ```bibtex
  @article{yan2020prophetnet,
    title={Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training},
    author={Yan, Yu and Qi, Weizhen and Gong, Yeyun and Liu, Dayiheng and Duan, Nan and Chen, Jiusheng and Zhang, Ruofei and Zhou, Ming},
    journal={arXiv preprint arXiv:2001.04063},
    year={2020}
  }
  ```
  