wikitablequestions / wikitablequestions.py
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Add wikitablequestions dataset (#3870)
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# 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}