Update parquet files

README.md

@@ -1,307 +0,0 @@
----
-pretty_name: TyDi QA
-annotations_creators:
-- crowdsourced
-language_creators:
-- crowdsourced
-language:
-- ar
-- bn
-- en
-- fi
-- id
-- ja
-- ko
-- ru
-- sw
-- te
-- th
-license:
-- apache-2.0
-multilinguality:
-- multilingual
-size_categories:
-- unknown
-source_datasets:
-- extended|wikipedia
-task_categories:
-- question-answering
-task_ids:
-- extractive-qa
-paperswithcode_id: tydi-qa
-dataset_info:
-- config_name: primary_task
-  features:
-  - name: passage_answer_candidates
-    sequence:
-    - name: plaintext_start_byte
-      dtype: int32
-    - name: plaintext_end_byte
-      dtype: int32
-  - name: question_text
-    dtype: string
-  - name: document_title
-    dtype: string
-  - name: language
-    dtype: string
-  - name: annotations
-    sequence:
-    - name: passage_answer_candidate_index
-      dtype: int32
-    - name: minimal_answers_start_byte
-      dtype: int32
-    - name: minimal_answers_end_byte
-      dtype: int32
-    - name: yes_no_answer
-      dtype: string
-  - name: document_plaintext
-    dtype: string
-  - name: document_url
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 5550574617
-    num_examples: 166916
-  - name: validation
-    num_bytes: 484380443
-    num_examples: 18670
-  download_size: 1953887429
-  dataset_size: 6034955060
-- config_name: secondary_task
-  features:
-  - name: id
-    dtype: string
-  - name: title
-    dtype: string
-  - name: context
-    dtype: string
-  - name: question
-    dtype: string
-  - name: answers
-    sequence:
-    - name: text
-      dtype: string
-    - name: answer_start
-      dtype: int32
-  splits:
-  - name: train
-    num_bytes: 52948607
-    num_examples: 49881
-  - name: validation
-    num_bytes: 5006461
-    num_examples: 5077
-  download_size: 1953887429
-  dataset_size: 57955068
----
-
-# Dataset Card for "tydiqa"
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://github.com/google-research-datasets/tydiqa](https://github.com/google-research-datasets/tydiqa)
-- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Size of downloaded dataset files:** 3726.74 MB
-- **Size of the generated dataset:** 5812.92 MB
-- **Total amount of disk used:** 9539.67 MB
-
-### Dataset Summary
-
-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).
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Dataset Structure
-
-### Data Instances
-
-#### primary_task
-
-- **Size of downloaded dataset files:** 1863.37 MB
-- **Size of the generated dataset:** 5757.59 MB
-- **Total amount of disk used:** 7620.96 MB
-
-An example of 'validation' looks as follows.
-```
-This example was too long and was cropped:
-
-{
-    "annotations": {
-        "minimal_answers_end_byte": [-1, -1, -1],
-        "minimal_answers_start_byte": [-1, -1, -1],
-        "passage_answer_candidate_index": [-1, -1, -1],
-        "yes_no_answer": ["NONE", "NONE", "NONE"]
-    },
-    "document_plaintext": "\"\\nรองศาสตราจารย์[1] หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร  (22 กันยายน 2495 -) ผู้ว่าราชการกรุงเทพมหานครคนที่ 15 อดีตรองหัวหน้าพรรคปร...",
-    "document_title": "หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร",
-    "document_url": "\"https://th.wikipedia.org/wiki/%E0%B8%AB%E0%B8%A1%E0%B9%88%E0%B8%AD%E0%B8%A1%E0%B8%A3%E0%B8%B2%E0%B8%8A%E0%B8%A7%E0%B8%87%E0%B8%...",
-    "language": "thai",
-    "passage_answer_candidates": "{\"plaintext_end_byte\": [494, 1779, 2931, 3904, 4506, 5588, 6383, 7122, 8224, 9375, 10473, 12563, 15134, 17765, 19863, 21902, 229...",
-    "question_text": "\"หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร เรียนจบจากที่ไหน ?\"..."
-}
-```
-
-#### secondary_task
-
-- **Size of downloaded dataset files:** 1863.37 MB
-- **Size of the generated dataset:** 55.34 MB
-- **Total amount of disk used:** 1918.71 MB
-
-An example of 'validation' looks as follows.
-```
-This example was too long and was cropped:
-
-{
-    "answers": {
-        "answer_start": [394],
-        "text": ["بطولتين"]
-    },
-    "context": "\"أقيمت البطولة 21 مرة، شارك في النهائيات 78 دولة، وعدد الفرق التي فازت بالبطولة حتى الآن 8 فرق، ويعد المنتخب البرازيلي الأكثر تت...",
-    "id": "arabic-2387335860751143628-1",
-    "question": "\"كم عدد مرات فوز الأوروغواي ببطولة كاس العالم لكرو القدم؟\"...",
-    "title": "قائمة نهائيات كأس العالم"
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### primary_task
-- `passage_answer_candidates`: a dictionary feature containing:
-  - `plaintext_start_byte`: a `int32` feature.
-  - `plaintext_end_byte`: a `int32` feature.
-- `question_text`: a `string` feature.
-- `document_title`: a `string` feature.
-- `language`: a `string` feature.
-- `annotations`: a dictionary feature containing:
-  - `passage_answer_candidate_index`: a `int32` feature.
-  - `minimal_answers_start_byte`: a `int32` feature.
-  - `minimal_answers_end_byte`: a `int32` feature.
-  - `yes_no_answer`: a `string` feature.
-- `document_plaintext`: a `string` feature.
-- `document_url`: a `string` feature.
-
-#### secondary_task
-- `id`: a `string` feature.
-- `title`: a `string` feature.
-- `context`: a `string` feature.
-- `question`: a `string` feature.
-- `answers`: a dictionary feature containing:
-  - `text`: a `string` feature.
-  - `answer_start`: a `int32` feature.
-
-### Data Splits
-
-| name           |  train | validation |
-| -------------- | -----: | ---------: |
-| primary_task   | 166916 |      18670 |
-| secondary_task |  49881 |       5077 |
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Citation Information
-
-```
-@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}
-}
-
-```
-
-
-### Contributions
-
-Thanks to [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
-{"primary_task": {"description": "TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.\nThe languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language\nexpresses -- such that we expect models performing well on this set to generalize across a large number of the languages\nin the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic\ninformation-seeking task and avoid priming effects, questions are written by people who want to know the answer, but\ndon\u2019t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without\nthe use of translation (unlike MLQA and XQuAD).\n", "citation": "@article{tydiqa,\ntitle   = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},\nauthor  = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}\nyear    = {2020},\njournal = {Transactions of the Association for Computational Linguistics}\n}\n", "homepage": "https://github.com/google-research-datasets/tydiqa", "license": "", "features": {"passage_answer_candidates": {"feature": {"plaintext_start_byte": {"dtype": "int32", "id": null, "_type": "Value"}, "plaintext_end_byte": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "question_text": {"dtype": "string", "id": null, "_type": "Value"}, "document_title": {"dtype": "string", "id": null, "_type": "Value"}, "language": {"dtype": "string", "id": null, "_type": "Value"}, "annotations": {"feature": {"passage_answer_candidate_index": {"dtype": "int32", "id": null, "_type": "Value"}, "minimal_answers_start_byte": {"dtype": "int32", "id": null, "_type": "Value"}, "minimal_answers_end_byte": {"dtype": "int32", "id": null, "_type": "Value"}, "yes_no_answer": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "document_plaintext": {"dtype": "string", "id": null, "_type": "Value"}, "document_url": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "tydiqa", "config_name": "primary_task", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5550574617, "num_examples": 166916, "dataset_name": "tydiqa"}, "validation": {"name": "validation", "num_bytes": 484380443, "num_examples": 18670, "dataset_name": "tydiqa"}}, "download_checksums": {"https://storage.googleapis.com/tydiqa/v1.0/tydiqa-v1.0-train.jsonl.gz": {"num_bytes": 1729651634, "checksum": "8eeedfee7593db7c3637d65a3d5c67b82486137ac6ac3ea7d08be9a64d71b629"}, "https://storage.googleapis.com/tydiqa/v1.0/tydiqa-v1.0-dev.jsonl.gz": {"num_bytes": 160614310, "checksum": "b52b8d4db1850b1549e960219e6056d8139986f8caf1b5e8b4eecadabed24413"}, "https://storage.googleapis.com/tydiqa/v1.1/tydiqa-goldp-v1.1-train.json": {"num_bytes": 58004076, "checksum": "cefc8e09ff2548d9b10a678d3a6bbbe5bc036be543f92418819ea676c97be23b"}, "https://storage.googleapis.com/tydiqa/v1.1/tydiqa-goldp-v1.1-dev.json": {"num_bytes": 5617409, "checksum": "b286e0f34bc7f52259359989716f369b160565bd12ad8f3a3e311f9b0dbad1c0"}}, "download_size": 1953887429, "post_processing_size": null, "dataset_size": 6034955060, "size_in_bytes": 7988842489}, "secondary_task": {"description": "TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.\nThe languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language\nexpresses -- such that we expect models performing well on this set to generalize across a large number of the languages\nin the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic\ninformation-seeking task and avoid priming effects, questions are written by people who want to know the answer, but\ndon\u2019t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without\nthe use of translation (unlike MLQA and XQuAD).\n", "citation": "@article{tydiqa,\ntitle   = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},\nauthor  = {Jonathan H. 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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,
-                                },
-                            }