Delete loading script

xquad_r.py

@@ -1,141 +0,0 @@
-# coding=utf-8
-# 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.
-"""TODO: Add a description here."""
-
-
-import json
-
-import datasets
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@article{roy2020lareqa,
-  title={LAReQA: Language-agnostic answer retrieval from a multilingual pool},
-  author={Roy, Uma and Constant, Noah and Al-Rfou, Rami and Barua, Aditya and Phillips, Aaron and Yang, Yinfei},
-  journal={arXiv preprint arXiv:2004.05484},
-  year={2020}
-}
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages.
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://github.com/google-research-datasets/lareqa"
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace dataset library don't host the datasets but only point to the original files
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URL = "https://github.com/google-research-datasets/lareqa/raw/master/xquad-r/"
-_LANG = ["ar", "de", "zh", "vi", "en", "es", "hi", "el", "th", "tr", "ru"]
-
-
-class XquadRConfig(datasets.BuilderConfig):
-
-    """BuilderConfig for XquadR"""
-
-    def __init__(self, lang, **kwargs):
-        """
-        Args:
-            lang: string, language for the input text
-            **kwargs: keyword arguments forwarded to super.
-        """
-        super(XquadRConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-        self.lang = lang
-
-
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class XquadR(datasets.GeneratorBasedBuilder):
-    """TODO(xquad-r): Short description of my dataset."""
-
-    # TODO(xquad-r): Set up version.
-    VERSION = datasets.Version("1.1.0")
-    BUILDER_CONFIGS = [XquadRConfig(name=f"{lang}", description=_DESCRIPTION, lang=lang) for lang in _LANG]
-
-    def _info(self):
-        # TODO(xquad-r): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        {
-                            "text": datasets.Value("string"),
-                            "answer_start": datasets.Value("int32"),
-                        }
-                    ),
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(xquad-r): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        urls_to_download = {lang: _URL + f"{lang}.json" for lang in _LANG}
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": downloaded_files[self.config.lang]},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(xquad-r): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for article in data["data"]:
-                for paragraph in article["paragraphs"]:
-                    context = paragraph["context"].strip()
-                    for qa in paragraph["qas"]:
-                        question = qa["question"].strip()
-                        id_ = qa["id"]
-
-                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                        answers = [answer["text"].strip() for answer in qa["answers"]]
-
-                        # Features currently used are "context", "question", and "answers".
-                        # Others are extracted here for the ease of future expansions.
-                        yield id_, {
-                            "context": context,
-                            "question": question,
-                            "id": id_,
-                            "answers": {
-                                "answer_start": answer_starts,
-                                "text": answers,
-                            },
-                        }