Migrate model card from transformers-repo

Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/microsoft/xprophetnet-large-wiki100-cased/README.md

README.md

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+---
+language: multilingual
+---
+
+## 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}
+}
+```