Delete loading script

wikitablequestions.py

@@ -1,184 +0,0 @@
-# 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}