README.md

TAPEX model fine-tuned on WTQ.

To load it and run inference, you can do the following:

from transformers import BartTokenizer, BartForConditionalGeneration import pandas as pd

tokenizer = BartTokenizer.from_pretrained("nielsr/tapex-large-finetuned-wtq") model = BartForConditionalGeneration.from_pretrained("nielsr/tapex-large-finetuned-wtq")

create table

data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]} table = pd.DataFrame.from_dict(data)

turn into dict

table_dict = {"header": list(table.columns), "rows": [list(row.values) for i,row in table.iterrows()]}

turn into format TAPEX expects

define the linearizer based on this code: https://github.com/microsoft/Table-Pretraining/blob/main/tapex/processor/table_linearize.py

linearizer = IndexedRowTableLinearize() linear_table = linearizer.process_table(table_dict)

add question

question = "how many movies does George Clooney have?" joint_input = question + " " + linear_table

encode

encoding = tokenizer(joint_input, return_tensors="pt")

forward pass

outputs = model.generate(**encoding)

decode

tokenizer.batch_decode(outputs, skip_special_tokens=True)