Datasets:

Stanford
/

wikitablequestions

	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	"""The WikiTableQuestions dataset is a large-scale dataset for the task of question answering on semi-structured tables."""

	import os

	import datasets


	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{pasupat-liang-2015-compositional,
	title = "Compositional Semantic Parsing on Semi-Structured Tables",
	author = "Pasupat, Panupong and Liang, Percy",
	booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
	month = jul,
	year = "2015",
	address = "Beijing, China",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/P15-1142",
	doi = "10.3115/v1/P15-1142",
	pages = "1470--1480",
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	This WikiTableQuestions dataset is a large-scale dataset for the task of question answering on semi-structured tables.
	"""

	_HOMEPAGE = "https://nlp.stanford.edu/software/sempre/wikitable"

	_LICENSE = "Creative Commons Attribution Share Alike 4.0 International"

	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_DATA_URL = (
	"https://github.com/ppasupat/WikiTableQuestions/releases/download/v1.0.2/WikiTableQuestions-1.0.2-compact.zip"
	)


	class WikiTableQuestions(datasets.GeneratorBasedBuilder):
	"""WikiTableQuestions: a large-scale dataset for the task of question answering on semi-structured tables."""

	VERSION = datasets.Version("1.0.2")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="random-split-1",
	version=VERSION,
	description="The random-split-1-train/dev.tsv and pristine-unseen-tables.tsv",
	),
	datasets.BuilderConfig(
	name="random-split-2",
	version=VERSION,
	description="The random-split-2-train/dev.tsv and pristine-unseen-tables.tsv",
	),
	datasets.BuilderConfig(
	name="random-split-3",
	version=VERSION,
	description="The random-split-3-train/dev.tsv and pristine-unseen-tables.tsv",
	),
	datasets.BuilderConfig(
	name="random-split-4",
	version=VERSION,
	description="The random-split-4-train/dev.tsv and pristine-unseen-tables.tsv",
	),
	datasets.BuilderConfig(
	name="random-split-5",
	version=VERSION,
	description="The random-split-5-train/dev.tsv and pristine-unseen-tables.tsv",
	),
	]

	DEFAULT_CONFIG_NAME = (
	"random-split-1" # It's not mandatory to have a default configuration. Just use one if it make sense.
	)

	def _info(self):
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answers": datasets.features.Sequence(datasets.Value("string")),
	"table": {
	"header": datasets.features.Sequence(datasets.Value("string")),
	"rows": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
	"name": datasets.Value("string"),
	},
	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	train_file = "{}-train.tsv".format(self.config.name)
	dev_file = "{}-dev.tsv".format(self.config.name)
	test_file = "pristine-unseen-tables.tsv"
	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
	urls = _DATA_URL
	root_dir = os.path.join(dl_manager.download_and_extract(urls), "WikiTableQuestions")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"main_filepath": os.path.join(root_dir, "data", train_file), "root_dir": root_dir},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"main_filepath": os.path.join(root_dir, "data", test_file), "root_dir": root_dir},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"main_filepath": os.path.join(root_dir, "data", dev_file), "root_dir": root_dir},
	),
	]

	def _read_table_from_file(self, table_name: str, root_dir: str):
	def _extract_table_content(_line: str):
	_vals = [_.replace("\n", " ").strip() for _ in _line.strip("\n").split("\t")]
	return _vals

	rows = []
	# assert ".csv" in _wtq_table_name
	# use the normalized table file
	table_name = table_name.replace(".csv", ".tsv")
	with open(os.path.join(root_dir, table_name), "r", encoding="utf8") as table_f:
	table_lines = table_f.readlines()
	# the first line is header
	header = _extract_table_content(table_lines[0])
	for line in table_lines[1:]:
	rows.append(_extract_table_content(line))
	return {"header": header, "rows": rows, "name": table_name}

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, main_filepath, root_dir):
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	with open(main_filepath, encoding="utf-8") as f:
	# skip the first line since it is the tsv header
	next(f)
	for idx, line in enumerate(f):
	example_id, question, table_name, answer = line.strip("\n").split("\t")
	answer = answer.split("\|")
	# must contain rows and header keys
	table_content = self._read_table_from_file(table_name, root_dir)

	yield idx, {"id": example_id, "question": question, "answers": answer, "table": table_content}