|  Weak supervision for aggregation  | input_ids, attention_mask, token_type_ids, labels, numeric_values, numeric_values_scale, float_answer |
| Strong supervision for aggregation | input ids, attention mask, token type ids, labels, aggregation_labels                                     |
[TapasTokenizer] creates the labels, numeric_values and numeric_values_scale based on the answer_coordinates and answer_text columns of the TSV file. The float_answer and aggregation_labels are already in the TSV file of step 2. Here's an example:
```py
from transformers import TapasTokenizer
import pandas as pd
model_name = "google/tapas-base"
tokenizer = TapasTokenizer.from_pretrained(model_name)
data = {"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], "Number of movies": ["87", "53", "69"]}
