readme: ## xprophetnet-large-wiki100-cased-xglue-ntg
  Cross-lingual version [ProphetNet](https://arxiv.org/abs/2001.04063), pretrained on [wiki100 xGLUE dataset](https://arxiv.org/abs/2004.01401) and finetuned on xGLUE cross-lingual Question Generation task.  
  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).   
  
  xProphetNet is also served as the baseline model for xGLUE cross-lingual natural language generation tasks.  
  For xGLUE corss-lingual NLG tasks, xProphetNet is finetuned with English data, but inference with both English and other zero-shot language data.  
  ### Usage
  A quick usage is like: 
  ```
  from transformers import ProphetNetTokenizer, ProphetNetForConditionalGeneration, ProphetNetConfig
  
  model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-qg')
  tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-qg')
  
  EN_SENTENCE = "Google left China in 2010"
  ZH_SENTENCE = "Google在2010年离开中国"
  inputs = tokenizer([EN_SENTENCE, ZH_SENTENCE], padding=True, max_length=256, return_tensors='pt')
  
  summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=100, early_stopping=True)
  print([tokenizer.decode(g) for g in summary_ids])  
  ```
  ### 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}
  }
  ```
  