Delete loading script

tydiqa.py

@@ -1,268 +0,0 @@
-"""TODO(tydiqa): Add a description here."""
-
-
-import json
-import textwrap
-
-import datasets
-from datasets.tasks import QuestionAnsweringExtractive
-
-
-# TODO(tydiqa): BibTeX citation
-_CITATION = """\
-@article{tydiqa,
-title   = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
-author  = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
-year    = {2020},
-journal = {Transactions of the Association for Computational Linguistics}
-}
-"""
-
-# TODO(tydiqa):
-_DESCRIPTION = """\
-TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
-The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
-expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
-in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic
-information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but
-don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without
-the use of translation (unlike MLQA and XQuAD).
-"""
-
-_URL = "https://storage.googleapis.com/tydiqa/"
-_PRIMARY_URLS = {
-    "train": _URL + "v1.0/tydiqa-v1.0-train.jsonl.gz",
-    "dev": _URL + "v1.0/tydiqa-v1.0-dev.jsonl.gz",
-}
-_SECONDARY_URLS = {
-    "train": _URL + "v1.1/tydiqa-goldp-v1.1-train.json",
-    "dev": _URL + "v1.1/tydiqa-goldp-v1.1-dev.json",
-}
-
-
-class TydiqaConfig(datasets.BuilderConfig):
-
-    """BuilderConfig for Tydiqa"""
-
-    def __init__(self, **kwargs):
-        """
-
-        Args:
-            **kwargs: keyword arguments forwarded to super.
-        """
-        super(TydiqaConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-
-
-class Tydiqa(datasets.GeneratorBasedBuilder):
-    """TODO(tydiqa): Short description of my dataset."""
-
-    # TODO(tydiqa): Set up version.
-    VERSION = datasets.Version("0.1.0")
-    BUILDER_CONFIGS = [
-        TydiqaConfig(
-            name="primary_task",
-            description=textwrap.dedent(
-                """\
-          Passage selection task (SelectP): Given a list of the passages in the article, return either (a) the index of
-          the passage that answers the question or (b) NULL if no such passage exists.
-          Minimal answer span task (MinSpan): Given the full text of an article, return one of (a) the start and end
-          byte indices of the minimal span that completely answers the question; (b) YES or NO if the question requires
-          a yes/no answer and we can draw a conclusion from the passage; (c) NULL if it is not possible to produce a
-          minimal answer for this question."""
-            ),
-        ),
-        TydiqaConfig(
-            name="secondary_task",
-            description=textwrap.dedent(
-                """Gold passage task (GoldP): Given a passage that is guaranteed to contain the
-          answer, predict the single contiguous span of characters that answers the question. This is more similar to
-          existing reading comprehension datasets (as opposed to the information-seeking task outlined above).
-          This task is constructed with two goals in mind: (1) more directly comparing with prior work and (2) providing
-          a simplified way for researchers to use TyDi QA by providing compatibility with existing code for SQuAD 1.1,
-          XQuAD, and MLQA. Toward these goals, the gold passage task differs from the primary task in several ways:
-          only the gold answer passage is provided rather than the entire Wikipedia article;
-          unanswerable questions have been discarded, similar to MLQA and XQuAD;
-          we evaluate with the SQuAD 1.1 metrics like XQuAD; and
-         Thai and Japanese are removed since the lack of whitespace breaks some tools.
-          """
-            ),
-        ),
-    ]
-
-    def _info(self):
-        # TODO(tydiqa): Specifies the datasets.DatasetInfo object
-        if self.config.name == "primary_task":
-            return datasets.DatasetInfo(
-                # This is the description that will appear on the datasets page.
-                description=_DESCRIPTION,
-                # datasets.features.FeatureConnectors
-                features=datasets.Features(
-                    {
-                        "passage_answer_candidates": datasets.features.Sequence(
-                            {
-                                "plaintext_start_byte": datasets.Value("int32"),
-                                "plaintext_end_byte": datasets.Value("int32"),
-                            }
-                        ),
-                        "question_text": datasets.Value("string"),
-                        "document_title": datasets.Value("string"),
-                        "language": datasets.Value("string"),
-                        "annotations": datasets.features.Sequence(
-                            {
-                                # 'annotation_id': datasets.Value('variant'),
-                                "passage_answer_candidate_index": datasets.Value("int32"),
-                                "minimal_answers_start_byte": datasets.Value("int32"),
-                                "minimal_answers_end_byte": datasets.Value("int32"),
-                                "yes_no_answer": datasets.Value("string"),
-                            }
-                        ),
-                        "document_plaintext": datasets.Value("string"),
-                        # 'example_id': datasets.Value('variant'),
-                        "document_url": datasets.Value("string")
-                        # These are the features of your dataset like images, labels ...
-                    }
-                ),
-                # 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="https://github.com/google-research-datasets/tydiqa",
-                citation=_CITATION,
-            )
-        elif self.config.name == "secondary_task":
-            return datasets.DatasetInfo(
-                description=_DESCRIPTION,
-                features=datasets.Features(
-                    {
-                        "id": datasets.Value("string"),
-                        "title": datasets.Value("string"),
-                        "context": datasets.Value("string"),
-                        "question": datasets.Value("string"),
-                        "answers": datasets.features.Sequence(
-                            {
-                                "text": datasets.Value("string"),
-                                "answer_start": datasets.Value("int32"),
-                            }
-                        ),
-                    }
-                ),
-                # No default supervised_keys (as we have to pass both question
-                # and context as input).
-                supervised_keys=None,
-                homepage="https://github.com/google-research-datasets/tydiqa",
-                citation=_CITATION,
-                task_templates=[
-                    QuestionAnsweringExtractive(
-                        question_column="question", context_column="context", answers_column="answers"
-                    )
-                ],
-            )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(tydiqa): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        primary_downloaded = dl_manager.download_and_extract(_PRIMARY_URLS)
-        secondary_downloaded = dl_manager.download_and_extract(_SECONDARY_URLS)
-        if self.config.name == "primary_task":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": primary_downloaded["train"]},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": primary_downloaded["dev"]},
-                ),
-            ]
-        elif self.config.name == "secondary_task":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": secondary_downloaded["train"]},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": secondary_downloaded["dev"]},
-                ),
-            ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(tydiqa): Yields (key, example) tuples from the dataset
-        if self.config.name == "primary_task":
-            with open(filepath, encoding="utf-8") as f:
-                for id_, row in enumerate(f):
-                    data = json.loads(row)
-                    passages = data["passage_answer_candidates"]
-                    end_byte = [passage["plaintext_end_byte"] for passage in passages]
-                    start_byte = [passage["plaintext_start_byte"] for passage in passages]
-                    title = data["document_title"]
-                    lang = data["language"]
-                    question = data["question_text"]
-                    annotations = data["annotations"]
-                    # annot_ids = [annotation["annotation_id"] for annotation in annotations]
-                    yes_no_answers = [annotation["yes_no_answer"] for annotation in annotations]
-                    min_answers_end_byte = [
-                        annotation["minimal_answer"]["plaintext_end_byte"] for annotation in annotations
-                    ]
-                    min_answers_start_byte = [
-                        annotation["minimal_answer"]["plaintext_start_byte"] for annotation in annotations
-                    ]
-                    passage_cand_answers = [
-                        annotation["passage_answer"]["candidate_index"] for annotation in annotations
-                    ]
-                    doc = data["document_plaintext"]
-                    # example_id = data["example_id"]
-                    url = data["document_url"]
-                    yield id_, {
-                        "passage_answer_candidates": {
-                            "plaintext_start_byte": start_byte,
-                            "plaintext_end_byte": end_byte,
-                        },
-                        "question_text": question,
-                        "document_title": title,
-                        "language": lang,
-                        "annotations": {
-                            # 'annotation_id': annot_ids,
-                            "passage_answer_candidate_index": passage_cand_answers,
-                            "minimal_answers_start_byte": min_answers_start_byte,
-                            "minimal_answers_end_byte": min_answers_end_byte,
-                            "yes_no_answer": yes_no_answers,
-                        },
-                        "document_plaintext": doc,
-                        # 'example_id': example_id,
-                        "document_url": url,
-                    }
-        elif self.config.name == "secondary_task":
-            with open(filepath, encoding="utf-8") as f:
-                data = json.load(f)
-                for article in data["data"]:
-                    title = article.get("title", "").strip()
-                    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_, {
-                                "title": title,
-                                "context": context,
-                                "question": question,
-                                "id": id_,
-                                "answers": {
-                                    "answer_start": answer_starts,
-                                    "text": answers,
-                                },
-                            }