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albertvillanova HF staff commited on
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Delete loading script

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  1. squad.py +0 -142
squad.py DELETED
@@ -1,142 +0,0 @@
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- # coding=utf-8
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- # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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- #
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- # Licensed under the Apache License, Version 2.0 (the "License");
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- # you may not use this file except in compliance with the License.
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- # You may obtain a copy of the License at
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- #
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- # http://www.apache.org/licenses/LICENSE-2.0
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- #
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- # Unless required by applicable law or agreed to in writing, software
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- # distributed under the License is distributed on an "AS IS" BASIS,
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- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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- # See the License for the specific language governing permissions and
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- # limitations under the License.
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-
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- # Lint as: python3
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- """SQUAD: The Stanford Question Answering Dataset."""
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-
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-
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- import json
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-
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- import datasets
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- from datasets.tasks import QuestionAnsweringExtractive
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-
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-
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- logger = datasets.logging.get_logger(__name__)
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-
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-
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- _CITATION = """\
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- @article{2016arXiv160605250R,
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- author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
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- Konstantin and {Liang}, Percy},
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- title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
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- journal = {arXiv e-prints},
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- year = 2016,
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- eid = {arXiv:1606.05250},
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- pages = {arXiv:1606.05250},
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- archivePrefix = {arXiv},
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- eprint = {1606.05250},
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- }
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- """
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-
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- _DESCRIPTION = """\
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- Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
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- dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
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- articles, where the answer to every question is a segment of text, or span, \
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- from the corresponding reading passage, or the question might be unanswerable.
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- """
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-
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- _URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
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- _URLS = {
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- "train": _URL + "train-v1.1.json",
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- "dev": _URL + "dev-v1.1.json",
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- }
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-
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-
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- class SquadConfig(datasets.BuilderConfig):
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- """BuilderConfig for SQUAD."""
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-
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- def __init__(self, **kwargs):
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- """BuilderConfig for SQUAD.
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-
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- Args:
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- **kwargs: keyword arguments forwarded to super.
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- """
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- super(SquadConfig, self).__init__(**kwargs)
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-
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-
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- class Squad(datasets.GeneratorBasedBuilder):
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- """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
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-
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- BUILDER_CONFIGS = [
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- SquadConfig(
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- name="plain_text",
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- version=datasets.Version("1.0.0", ""),
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- description="Plain text",
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- ),
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- ]
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-
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- def _info(self):
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- return datasets.DatasetInfo(
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- description=_DESCRIPTION,
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- features=datasets.Features(
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- {
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- "id": datasets.Value("string"),
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- "title": datasets.Value("string"),
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- "context": datasets.Value("string"),
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- "question": datasets.Value("string"),
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- "answers": datasets.features.Sequence(
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- {
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- "text": datasets.Value("string"),
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- "answer_start": datasets.Value("int32"),
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- }
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- ),
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- }
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- ),
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- # No default supervised_keys (as we have to pass both question
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- # and context as input).
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- supervised_keys=None,
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- homepage="https://rajpurkar.github.io/SQuAD-explorer/",
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- citation=_CITATION,
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- task_templates=[
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- QuestionAnsweringExtractive(
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- question_column="question", context_column="context", answers_column="answers"
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- )
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- ],
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- )
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-
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- def _split_generators(self, dl_manager):
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- downloaded_files = dl_manager.download_and_extract(_URLS)
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-
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- return [
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- datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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- datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
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- ]
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-
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- def _generate_examples(self, filepath):
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- """This function returns the examples in the raw (text) form."""
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- logger.info("generating examples from = %s", filepath)
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- key = 0
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- with open(filepath, encoding="utf-8") as f:
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- squad = json.load(f)
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- for article in squad["data"]:
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- title = article.get("title", "")
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- for paragraph in article["paragraphs"]:
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- context = paragraph["context"] # do not strip leading blank spaces GH-2585
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- for qa in paragraph["qas"]:
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- answer_starts = [answer["answer_start"] for answer in qa["answers"]]
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- answers = [answer["text"] for answer in qa["answers"]]
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- # Features currently used are "context", "question", and "answers".
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- # Others are extracted here for the ease of future expansions.
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- yield key, {
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- "title": title,
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- "context": context,
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- "question": qa["question"],
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- "id": qa["id"],
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- "answers": {
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- "answer_start": answer_starts,
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- "text": answers,
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- },
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- }
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- key += 1