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| language: multilingual | |
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| ## xprophetnet-large-wiki100-cased | |
| Cross-lingual version [ProphetNet](https://arxiv.org/abs/2001.04063), pretrained on [wiki100 xGLUE dataset](https://arxiv.org/abs/2004.01401). | |
| 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 | |
| This pre-trained model can be fine-tuned on *sequence-to-sequence* tasks. The model could *e.g.* be trained on English headline generation as follows: | |
| ```python | |
| from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer | |
| model = XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased") | |
| tokenizer = XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased") | |
| input_str = "the us state department said wednesday it had received no formal word from bolivia that it was expelling the us ambassador there but said the charges made against him are `` baseless ." | |
| target_str = "us rejects charges against its ambassador in bolivia" | |
| input_ids = tokenizer(input_str, return_tensors="pt").input_ids | |
| labels = tokenizer(target_str, return_tensors="pt").input_ids | |
| loss = model(input_ids, labels=labels).loss | |
| ``` | |
| Note that since this model is a multi-lingual model it can be fine-tuned on all kinds of other languages. | |
| ### 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} | |
| } | |
| ``` | |