readme: This model corresponds to **tapas_masklm_medium_reset** of the [original repository](https://github.com/google-research/tapas).
  
  Here's how you can use it:
  
  ```python
  from transformers import TapasTokenizer, TapasForMaskedLM
  import pandas as pd
  import torch
  
  tokenizer = TapasTokenizer.from_pretrained("google/tapas-medium-masklm")
  model = TapasForMaskedLM.from_pretrained("google/tapas-medium-masklm")
  
  data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
          'Age': ["56", "45", "59"],
          'Number of movies': ["87", "53", "69"]
  }
  table = pd.DataFrame.from_dict(data)
  query = "How many movies has Leonardo [MASK] Caprio played in?"
  
  # prepare inputs
  inputs = tokenizer(table=table, queries=query, padding="max_length", return_tensors="pt")
  
  # forward pass
  outputs = model(**inputs)
  
  # return top 5 values and predictions
  masked_index = torch.nonzero(inputs.input_ids.squeeze() == tokenizer.mask_token_id, as_tuple=False)
  logits = outputs.logits[0, masked_index.item(), :]
  probs = logits.softmax(dim=0)
  values, predictions = probs.topk(5)
  
  for value, pred in zip(values, predictions):
    print(f"{tokenizer.decode([pred])} with confidence {value}")
  ```