# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import (
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
AutoModelForTableQuestionAnswering,
AutoTokenizer,
TableQuestionAnsweringPipeline,
TFAutoModelForTableQuestionAnswering,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
require_pandas,
require_tensorflow_probability,
require_tf,
require_torch,
slow,
)
@is_pipeline_test
class TQAPipelineTests(unittest.TestCase):
# Putting it there for consistency, but TQA do not have fast tokenizer
# which are needed to generate automatic tests
model_mapping = MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING
@require_tensorflow_probability
@require_pandas
@require_tf
@require_torch
def test_small_model_tf(self):
model_id = "lysandre/tiny-tapas-random-wtq"
model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_id, from_pt=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
self.assertIsInstance(model.config.aggregation_labels, dict)
self.assertIsInstance(model.config.no_aggregation_label_index, int)
table_querier = TableQuestionAnsweringPipeline(model=model, tokenizer=tokenizer)
outputs = table_querier(
table={
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["56", "45", "59"],
"number of movies": ["87", "53", "69"],
"date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
},
query="how many movies has george clooney played in?",
)
self.assertEqual(
outputs,
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
)
outputs = table_querier(
table={
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["56", "45", "59"],
"number of movies": ["87", "53", "69"],
"date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
},
query=["how many movies has george clooney played in?", "how old is he?", "what's his date of birth?"],
)
self.assertEqual(
outputs,
[
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
],
)
outputs = table_querier(
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
},
query=[
"What repository has the largest number of stars?",
"Given that the numbers of stars defines if a repository is active, what repository is the most"
" active?",
"What is the number of repositories?",
"What is the average number of stars?",
"What is the total amount of stars?",
],
)
self.assertEqual(
outputs,
[
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
],
)
with self.assertRaises(ValueError):
table_querier(query="What does it do with empty context ?", table=None)
with self.assertRaises(ValueError):
table_querier(query="What does it do with empty context ?", table="")
with self.assertRaises(ValueError):
table_querier(query="What does it do with empty context ?", table={})
with self.assertRaises(ValueError):
table_querier(
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
}
)
with self.assertRaises(ValueError):
table_querier(
query="",
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
},
)
with self.assertRaises(ValueError):
table_querier(
query=None,
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
},
)
@require_torch
def test_small_model_pt(self):
model_id = "lysandre/tiny-tapas-random-wtq"
model = AutoModelForTableQuestionAnswering.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
self.assertIsInstance(model.config.aggregation_labels, dict)
self.assertIsInstance(model.config.no_aggregation_label_index, int)
table_querier = TableQuestionAnsweringPipeline(model=model, tokenizer=tokenizer)
outputs = table_querier(
table={
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["56", "45", "59"],
"number of movies": ["87", "53", "69"],
"date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
},
query="how many movies has george clooney played in?",
)
self.assertEqual(
outputs,
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
)
outputs = table_querier(
table={
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["56", "45", "59"],
"number of movies": ["87", "53", "69"],
"date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
},
query=["how many movies has george clooney played in?", "how old is he?", "what's his date of birth?"],
)
self.assertEqual(
outputs,
[
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
],
)
outputs = table_querier(
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
},
query=[
"What repository has the largest number of stars?",
"Given that the numbers of stars defines if a repository is active, what repository is the most"
" active?",
"What is the number of repositories?",
"What is the average number of stars?",
"What is the total amount of stars?",
],
)
self.assertEqual(
outputs,
[
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
{"answer": "AVERAGE > ", "coordinates": [], "cells": [], "aggregator": "AVERAGE"},
],
)
with self.assertRaises(ValueError):
table_querier(query="What does it do with empty context ?", table=None)
with self.assertRaises(ValueError):
table_querier(query="What does it do with empty context ?", table="")
with self.assertRaises(ValueError):
table_querier(query="What does it do with empty context ?", table={})
with self.assertRaises(ValueError):
table_querier(
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
}
)
with self.assertRaises(ValueError):
table_querier(
query="",
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
},
)
with self.assertRaises(ValueError):
table_querier(
query=None,
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
},
)
@require_torch
def test_slow_tokenizer_sqa_pt(self):
model_id = "lysandre/tiny-tapas-random-sqa"
model = AutoModelForTableQuestionAnswering.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
table_querier = TableQuestionAnsweringPipeline(model=model, tokenizer=tokenizer)
inputs = {
"table": {
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["56", "45", "59"],
"number of movies": ["87", "53", "69"],
"date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
},
"query": ["how many movies has george clooney played in?", "how old is he?", "what's his date of birth?"],
}
sequential_outputs = table_querier(**inputs, sequential=True)
batch_outputs = table_querier(**inputs, sequential=False)
self.assertEqual(len(sequential_outputs), 3)
self.assertEqual(len(batch_outputs), 3)
self.assertEqual(sequential_outputs[0], batch_outputs[0])
self.assertNotEqual(sequential_outputs[1], batch_outputs[1])
# self.assertNotEqual(sequential_outputs[2], batch_outputs[2])
table_querier = TableQuestionAnsweringPipeline(model=model, tokenizer=tokenizer)
outputs = table_querier(
table={
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["56", "45", "59"],
"number of movies": ["87", "53", "69"],
"date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
},
query="how many movies has george clooney played in?",
)
self.assertEqual(
outputs,
{"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
)
outputs = table_querier(
table={
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["56", "45", "59"],
"number of movies": ["87", "53", "69"],
"date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
},
query=["how many movies has george clooney played in?", "how old is he?", "what's his date of birth?"],
)
self.assertEqual(
outputs,
[
{"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
{"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
{"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
],
)
outputs = table_querier(
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
},
query=[
"What repository has the largest number of stars?",
"Given that the numbers of stars defines if a repository is active, what repository is the most"
" active?",
"What is the number of repositories?",
"What is the average number of stars?",
"What is the total amount of stars?",
],
)
self.assertEqual(
outputs,
[
{"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
{"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
{"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
{"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
{"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
],
)
with self.assertRaises(ValueError):
table_querier(query="What does it do with empty context ?", table=None)
with self.assertRaises(ValueError):
table_querier(query="What does it do with empty context ?", table="")
with self.assertRaises(ValueError):
table_querier(query="What does it do with empty context ?", table={})
with self.assertRaises(ValueError):
table_querier(
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
}
)
with self.assertRaises(ValueError):
table_querier(
query="",
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
},
)
with self.assertRaises(ValueError):
table_querier(
query=None,
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
},
)
@require_tf
@require_tensorflow_probability
@require_pandas
@require_torch
def test_slow_tokenizer_sqa_tf(self):
model_id = "lysandre/tiny-tapas-random-sqa"
model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_id, from_pt=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
table_querier = TableQuestionAnsweringPipeline(model=model, tokenizer=tokenizer)
inputs = {
"table": {
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["56", "45", "59"],
"number of movies": ["87", "53", "69"],
"date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
},
"query": ["how many movies has george clooney played in?", "how old is he?", "what's his date of birth?"],
}
sequential_outputs = table_querier(**inputs, sequential=True)
batch_outputs = table_querier(**inputs, sequential=False)
self.assertEqual(len(sequential_outputs), 3)
self.assertEqual(len(batch_outputs), 3)
self.assertEqual(sequential_outputs[0], batch_outputs[0])
self.assertNotEqual(sequential_outputs[1], batch_outputs[1])
# self.assertNotEqual(sequential_outputs[2], batch_outputs[2])
table_querier = TableQuestionAnsweringPipeline(model=model, tokenizer=tokenizer)
outputs = table_querier(
table={
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["56", "45", "59"],
"number of movies": ["87", "53", "69"],
"date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
},
query="how many movies has george clooney played in?",
)
self.assertEqual(
outputs,
{"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
)
outputs = table_querier(
table={
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["56", "45", "59"],
"number of movies": ["87", "53", "69"],
"date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
},
query=["how many movies has george clooney played in?", "how old is he?", "what's his date of birth?"],
)
self.assertEqual(
outputs,
[
{"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
{"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
{"answer": "7 february 1967", "coordinates": [(0, 3)], "cells": ["7 february 1967"]},
],
)
outputs = table_querier(
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
},
query=[
"What repository has the largest number of stars?",
"Given that the numbers of stars defines if a repository is active, what repository is the most"
" active?",
"What is the number of repositories?",
"What is the average number of stars?",
"What is the total amount of stars?",
],
)
self.assertEqual(
outputs,
[
{"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
{"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
{"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
{"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
{"answer": "Python, Python", "coordinates": [(0, 3), (1, 3)], "cells": ["Python", "Python"]},
],
)
with self.assertRaises(ValueError):
table_querier(query="What does it do with empty context ?", table=None)
with self.assertRaises(ValueError):
table_querier(query="What does it do with empty context ?", table="")
with self.assertRaises(ValueError):
table_querier(query="What does it do with empty context ?", table={})
with self.assertRaises(ValueError):
table_querier(
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
}
)
with self.assertRaises(ValueError):
table_querier(
query="",
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
},
)
with self.assertRaises(ValueError):
table_querier(
query=None,
table={
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
},
)
@slow
@require_torch
def test_integration_wtq_pt(self):
table_querier = pipeline("table-question-answering")
data = {
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
}
queries = [
"What repository has the largest number of stars?",
"Given that the numbers of stars defines if a repository is active, what repository is the most active?",
"What is the number of repositories?",
"What is the average number of stars?",
"What is the total amount of stars?",
]
results = table_querier(data, queries)
expected_results = [
{"answer": "Transformers", "coordinates": [(0, 0)], "cells": ["Transformers"], "aggregator": "NONE"},
{"answer": "Transformers", "coordinates": [(0, 0)], "cells": ["Transformers"], "aggregator": "NONE"},
{
"answer": "COUNT > Transformers, Datasets, Tokenizers",
"coordinates": [(0, 0), (1, 0), (2, 0)],
"cells": ["Transformers", "Datasets", "Tokenizers"],
"aggregator": "COUNT",
},
{
"answer": "AVERAGE > 36542, 4512, 3934",
"coordinates": [(0, 1), (1, 1), (2, 1)],
"cells": ["36542", "4512", "3934"],
"aggregator": "AVERAGE",
},
{
"answer": "SUM > 36542, 4512, 3934",
"coordinates": [(0, 1), (1, 1), (2, 1)],
"cells": ["36542", "4512", "3934"],
"aggregator": "SUM",
},
]
self.assertListEqual(results, expected_results)
@slow
@require_tensorflow_probability
@require_pandas
def test_integration_wtq_tf(self):
model_id = "google/tapas-base-finetuned-wtq"
model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
table_querier = pipeline("table-question-answering", model=model, tokenizer=tokenizer)
data = {
"Repository": ["Transformers", "Datasets", "Tokenizers"],
"Stars": ["36542", "4512", "3934"],
"Contributors": ["651", "77", "34"],
"Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
}
queries = [
"What repository has the largest number of stars?",
"Given that the numbers of stars defines if a repository is active, what repository is the most active?",
"What is the number of repositories?",
"What is the average number of stars?",
"What is the total amount of stars?",
]
results = table_querier(data, queries)
expected_results = [
{"answer": "Transformers", "coordinates": [(0, 0)], "cells": ["Transformers"], "aggregator": "NONE"},
{"answer": "Transformers", "coordinates": [(0, 0)], "cells": ["Transformers"], "aggregator": "NONE"},
{
"answer": "COUNT > Transformers, Datasets, Tokenizers",
"coordinates": [(0, 0), (1, 0), (2, 0)],
"cells": ["Transformers", "Datasets", "Tokenizers"],
"aggregator": "COUNT",
},
{
"answer": "AVERAGE > 36542, 4512, 3934",
"coordinates": [(0, 1), (1, 1), (2, 1)],
"cells": ["36542", "4512", "3934"],
"aggregator": "AVERAGE",
},
{
"answer": "SUM > 36542, 4512, 3934",
"coordinates": [(0, 1), (1, 1), (2, 1)],
"cells": ["36542", "4512", "3934"],
"aggregator": "SUM",
},
]
self.assertListEqual(results, expected_results)
@slow
@require_torch
def test_integration_sqa_pt(self):
table_querier = pipeline(
"table-question-answering",
model="google/tapas-base-finetuned-sqa",
tokenizer="google/tapas-base-finetuned-sqa",
)
data = {
"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
"Age": ["56", "45", "59"],
"Number of movies": ["87", "53", "69"],
"Date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
}
queries = ["How many movies has George Clooney played in?", "How old is he?", "What's his date of birth?"]
results = table_querier(data, queries, sequential=True)
expected_results = [
{"answer": "69", "coordinates": [(2, 2)], "cells": ["69"]},
{"answer": "59", "coordinates": [(2, 1)], "cells": ["59"]},
{"answer": "28 november 1967", "coordinates": [(2, 3)], "cells": ["28 november 1967"]},
]
self.assertListEqual(results, expected_results)
@slow
@require_tensorflow_probability
@require_pandas
def test_integration_sqa_tf(self):
model_id = "google/tapas-base-finetuned-sqa"
model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
table_querier = pipeline(
"table-question-answering",
model=model,
tokenizer=tokenizer,
)
data = {
"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
"Age": ["56", "45", "59"],
"Number of movies": ["87", "53", "69"],
"Date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
}
queries = ["How many movies has George Clooney played in?", "How old is he?", "What's his date of birth?"]
results = table_querier(data, queries, sequential=True)
expected_results = [
{"answer": "69", "coordinates": [(2, 2)], "cells": ["69"]},
{"answer": "59", "coordinates": [(2, 1)], "cells": ["59"]},
{"answer": "28 november 1967", "coordinates": [(2, 3)], "cells": ["28 november 1967"]},
]
self.assertListEqual(results, expected_results)
@slow
@require_torch
def test_large_model_pt_tapex(self):
model_id = "microsoft/tapex-large-finetuned-wtq"
table_querier = pipeline(
"table-question-answering",
model=model_id,
)
data = {
"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
"Age": ["56", "45", "59"],
"Number of movies": ["87", "53", "69"],
"Date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
}
queries = [
"How many movies has George Clooney played in?",
"How old is Mr Clooney ?",
"What's the date of birth of Leonardo ?",
]
results = table_querier(data, queries, sequential=True)
expected_results = [
{"answer": " 69"},
{"answer": " 59"},
{"answer": " 10 june 1996"},
]
self.assertListEqual(results, expected_results)