prophetnet-large-uncased-squad-qgFine-tuned weights(converted from original fairseq version repo ) for ProphetNet on question generation SQuAD 1.1. ProphetNet是一种新的预训练语言模型，用于序列到序列学习，它具有一种称为future n-gram prediction的新颖自监督目标。 ProphetNet能够使用n-stream解码器预测更多未来的标记。原始实现是在 github repo 的Fairseq版本。

使用方法

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?'

引用

@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}
}