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model_cards/microsoft/prophetnet-large-uncased/README.md

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+---
+language: en
+---
+
 ## prophetnet-large-uncased
 Pretrained weights for [ProphetNet](https://arxiv.org/abs/2001.04063).  
 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).   
 
 ### Usage
-Please see [the official repository](https://github.com/microsoft/ProphetNet) for details.
+
+This pre-trained model can be fine-tuned on *sequence-to-sequence* tasks. The model could *e.g.* be trained on headline generation as follows:
+
+```python 
+from transformers import ProphetNetForConditionalGeneration, ProphetNetTokenizer
+
+model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased")
+tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")
+
+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, return_dict=True).loss
+```
 
 ### Citation
 ```bibtex