| ## 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} | |
| } | |
| ``` | |