This model corresponds to **tapas_masklm_small_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-small-masklm") model = TapasForMaskedLM.from_pretrained("google/tapas-small-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}") ```