readme: ### How to use
  Here is how to use this model in PyTorch:
  ```python
  from transformers import PerceiverTokenizer, PerceiverForMaskedLM
  tokenizer = PerceiverTokenizer.from_pretrained("addy88/perceiver_imdb")
  model = PerceiverForMaskedLM.from_pretrained("addy88/perceiver_imdb")
  text = "This is an incomplete sentence where some words are missing."
  # prepare input
  encoding = tokenizer(text, padding="max_length", return_tensors="pt")
  # mask " missing.". Note that the model performs much better if the masked span starts with a space.
  encoding.input_ids[0, 52:61] = tokenizer.mask_token_id
  inputs, input_mask = encoding.input_ids.to(device), encoding.attention_mask.to(device)
  # forward pass
  outputs = model(inputs=inputs, attention_mask=input_mask)
  logits = outputs.logits
  masked_tokens_predictions = logits[0, 51:61].argmax(dim=-1)
  print(tokenizer.decode(masked_tokens_predictions))
  >>> should print " missing."
  ```