How to use the pretraining task of ProphetNet
I want to use the pretraining task of ProphetNet, that recovers the mask span of the input sentence.
I follow the instruction of Figure 1 in the paper.

For example, the input is `But I [MASK][MASK] my life for some lovin\' and some gold` and I only recover the first `[MASK]`. (the sentence is from the pretraining corpus BookCorpus)
I use the following code:
```python
from transformers import ProphetNetTokenizer, ProphetNetForConditionalGeneration
tokenizer = ProphetNetTokenizer.from_pretrained('prophetnet')
model = ProphetNetForConditionalGeneration.from_pretrained('prophetnet')

# the sentence is from the pretraining corpus BookCorpus
input_ids = tokenizer('But I traded all my life for some lovin\' and some gold', return_tensors="pt")['input_ids']
mask_id = input_ids[0][2]
input_ids[0][2:4] = tokenizer.pad_token_id

decoder_input_ids = tokenizer('[MASK][MASK] I', return_tensors="pt")['input_ids']
# the way of MASS: decoder_input_ids = tokenizer('[MASK][MASK][MASK]', return_tensors="pt")['input_ids']

output = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
probs = output.logits[0][2]
# the rank of the target word in the vocabulary
print((probs[mask_id]<probs).sum())
```

However, the rank of `traded` is 15182 among 30522 words.
And I also tried different masked words and masked spans, but the results are all unexpected.

So, I want to ask if my way to recover the mask has some errors? @patrickvonplaten 
cc @qiweizhen<|||||>@StevenTang1998 - could you maybe try to use the forum: https://discuss.huggingface.co/ for such questions. I haven't played around with the model enough to give a qualified answer here sadly :-/ <|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.