# 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}