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