'numeric_values': tensor([[ ... ]]), 'numeric_values_scale: tensor([[ ... ]]), labels: tensor([[ ... ]])}
```
Note that [TapasTokenizer] expects the data of the table to be text-only. You can use .astype(str) on a dataframe to turn it into text-only data.
Of course, this only shows how to encode a single training example. It is advised to create a dataloader to iterate over batches:
```py
import torch
import pandas as pd
tsv_path = "your_path_to_the_tsv_file"
table_csv_path = "your_path_to_a_directory_containing_all_csv_files"
class TableDataset(torch.utils.data.Dataset):
...     def init(self, data, tokenizer):
...         self.data = data
...         self.tokenizer = tokenizer
...     def getitem(self, idx):
...         item = data.iloc[idx]
...         table = pd.read_csv(table_csv_path + item.table_file).astype(
