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language: multilingual |
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## xprophetnet-large-wiki100-cased |
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Cross-lingual version [ProphetNet](https://arxiv.org/abs/2001.04063), pretrained on [wiki100 xGLUE dataset](https://arxiv.org/abs/2004.01401). |
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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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xProphetNet is also served as the baseline model for xGLUE cross-lingual natural language generation tasks. |
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For xGLUE corss-lingual NLG tasks, xProphetNet is finetuned with English data, but inference with both English and other zero-shot language data. |
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### Usage |
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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: |
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```python |
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from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer |
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model = XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased") |
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tokenizer = XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased") |
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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 ." |
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target_str = "us rejects charges against its ambassador in bolivia" |
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input_ids = tokenizer(input_str, return_tensors="pt").input_ids |
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labels = tokenizer(target_str, return_tensors="pt").input_ids |
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loss = model(input_ids, labels=labels).loss |
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``` |
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Note that since this model is a multi-lingual model it can be fine-tuned on all kinds of other languages. |
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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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