该模型对应原仓库中的tapas_masklm_base_reset。

使用方法如下：

from transformers import TapasTokenizer, TapasForMaskedLM
import pandas as pd
import torch

tokenizer = TapasTokenizer.from_pretrained("google/tapas-base-masklm")
model = TapasForMaskedLM.from_pretrained("google/tapas-base-masklm")

data = {'演员': ["布拉德·皮特", "莱昂纳多·迪卡普里奥", "乔治·克鲁尼"],
        '年龄': ["56", "45", "59"],
        '电影数量': ["87", "53", "69"]
}
table = pd.DataFrame.from_dict(data)
query = "莱昂纳多·[MASK]·迪卡普里奥出演了多少部电影？"

# 准备输入
inputs = tokenizer(table=table, queries=query, padding="max_length", return_tensors="pt")

# 前向传播
outputs = model(**inputs)

# 返回前5个预测值及其置信度
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])} 置信度：{value}")