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README.md

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----
-annotations_creators:
-- machine-generated
-language:
-- en
-language_creators:
-- found
-license:
-- unknown
-multilinguality:
-- monolingual
-pretty_name: Quizbowl
-size_categories:
-- 100K<n<1M
-source_datasets:
-- original
-task_categories:
-- question-answering
-task_ids: []
-paperswithcode_id: quizbowl
-tags:
-- quizbowl
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: qanta_id
-    dtype: int32
-  - name: proto_id
-    dtype: string
-  - name: qdb_id
-    dtype: int32
-  - name: dataset
-    dtype: string
-  - name: text
-    dtype: string
-  - name: full_question
-    dtype: string
-  - name: first_sentence
-    dtype: string
-  - name: char_idx
-    dtype: int32
-  - name: sentence_idx
-    dtype: int32
-  - name: tokenizations
-    sequence:
-      sequence: int32
-      length: 2
-  - name: answer
-    dtype: string
-  - name: page
-    dtype: string
-  - name: raw_answer
-    dtype: string
-  - name: fold
-    dtype: string
-  - name: gameplay
-    dtype: bool
-  - name: category
-    dtype: string
-  - name: subcategory
-    dtype: string
-  - name: tournament
-    dtype: string
-  - name: difficulty
-    dtype: string
-  - name: year
-    dtype: int32
-  config_name: mode=first,char_skip=25
-  splits:
-  - name: adversarial
-    num_bytes: 1258844
-    num_examples: 1145
-  - name: buzzdev
-    num_bytes: 1553636
-    num_examples: 1161
-  - name: buzztest
-    num_bytes: 2653425
-    num_examples: 1953
-  - name: buzztrain
-    num_bytes: 19699736
-    num_examples: 16706
-  - name: guessdev
-    num_bytes: 1414882
-    num_examples: 1055
-  - name: guesstest
-    num_bytes: 2997123
-    num_examples: 2151
-  - name: guesstrain
-    num_bytes: 117599750
-    num_examples: 96221
-  download_size: 170754918
-  dataset_size: 147177396
----
-
-# Dataset Card for "qanta"
-
-## 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:** [http://www.qanta.org/](http://www.qanta.org/)
-- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792)
-- **Point of Contact:** [Jordan Boyd-Graber](mailto:jbg@umiacs.umd.edu)
-- **Size of downloaded dataset files:** 162.84 MB
-- **Size of the generated dataset:** 140.36 MB
-- **Total amount of disk used:** 303.20 MB
-
-### Dataset Summary
-
-The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl.
-
-### 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
-
-#### mode=first,char_skip=25
-
-- **Size of downloaded dataset files:** 162.84 MB
-- **Size of the generated dataset:** 140.36 MB
-- **Total amount of disk used:** 303.20 MB
-
-An example of 'guessdev' looks as follows.
-```
-This example was too long and was cropped:
-
-{
-    "answer": "Apollo_program",
-    "category": "History",
-    "char_idx": -1,
-    "dataset": "quizdb.org",
-    "difficulty": "easy_college",
-    "first_sentence": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"",
-    "fold": "guessdev",
-    "full_question": "\"As part of this program, William Anders took a photo that Galen Rowell called \\\"the most influential environmental photograph e...",
-    "gameplay": false,
-    "id": "127028-first",
-    "page": "Apollo_program",
-    "proto_id": "",
-    "qanta_id": 127028,
-    "qdb_id": 126689,
-    "raw_answer": "Apollo program [or Project Apollo; accept Apollo 8; accept Apollo 1; accept Apollo 11; prompt on landing on the moon]",
-    "sentence_idx": -1,
-    "subcategory": "American",
-    "text": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"",
-    "tokenizations": [[0, 137], [138, 281], [282, 412], [413, 592], [593, 675]],
-    "tournament": "ACF Fall",
-    "year": 2016
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### mode=first,char_skip=25
-- `id`: a `string` feature.
-- `qanta_id`: a `int32` feature.
-- `proto_id`: a `string` feature.
-- `qdb_id`: a `int32` feature.
-- `dataset`: a `string` feature.
-- `text`: a `string` feature.
-- `full_question`: a `string` feature.
-- `first_sentence`: a `string` feature.
-- `char_idx`: a `int32` feature.
-- `sentence_idx`: a `int32` feature.
-- `tokenizations`: a dictionary feature containing:
-  - `feature`: a `int32` feature.
-- `answer`: a `string` feature.
-- `page`: a `string` feature.
-- `raw_answer`: a `string` feature.
-- `fold`: a `string` feature.
-- `gameplay`: a `bool` feature.
-- `category`: a `string` feature.
-- `subcategory`: a `string` feature.
-- `tournament`: a `string` feature.
-- `difficulty`: a `string` feature.
-- `year`: a `int32` feature.
-
-### Data Splits
-
-|         name          |adversarial|buzzdev|buzztrain|guessdev|guesstrain|buzztest|guesstest|
-|-----------------------|----------:|------:|--------:|-------:|---------:|-------:|--------:|
-|mode=first,char_skip=25|       1145|   1161|    16706|    1055|     96221|    1953|     2151|
-
-## 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{Rodriguez2019QuizbowlTC,
-  title={Quizbowl: The Case for Incremental Question Answering},
-  author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber},
-  journal={ArXiv},
-  year={2019},
-  volume={abs/1904.04792}
-}
-```
-
-
-### Contributions
-
-Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

dataset_infos.json

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qanta.py

@@ -1,289 +0,0 @@
-"""qanta dataset."""
-
-
-import json
-from typing import List, Tuple
-
-import datasets
-
-
-_CITATION = """
-@article{Rodriguez2019QuizbowlTC,
-  title={Quizbowl: The Case for Incremental Question Answering},
-  author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber},
-  journal={ArXiv},
-  year={2019},
-  volume={abs/1904.04792}
-}
-"""
-
-_DESCRIPTION = """
-The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl.
-"""
-
-
-_QANTA_URL = "https://s3-us-west-2.amazonaws.com/pinafore-us-west-2/qanta-jmlr-datasets/qanta.mapped.2018.04.18.json"
-_TRICK_URL = "https://s3-us-west-2.amazonaws.com/pinafore-us-west-2/trick-tacl-datasets/qanta.tacl-trick.json"
-_VERSION = datasets.Version("2018.04.18")
-_FIRST = "first"
-_FULL = "full"
-_SENTENCES = "sentences"
-_RUNS = "runs"
-# Order matters, the first one is default
-_MODES = [_FULL, _FIRST, _SENTENCES, _RUNS]
-_DEFAULT_CHAR_SKIP = 25
-
-
-class QantaConfig(datasets.BuilderConfig):
-    """BuilderConfig for Qanta."""
-
-    def __init__(self, mode: str, char_skip: int, **kwargs):
-        super(QantaConfig, self).__init__(version=_VERSION, **kwargs)
-        self.mode = mode
-        self.char_skip = char_skip
-
-
-def create_char_runs(text: str, char_skip: int) -> List[Tuple[str, int]]:
-    """
-    Returns runs of the question based on skipping char_skip characters at a time. Also returns the indices used
-    q: name this first united states president.
-    runs with char_skip=10:
-    ['name this ',
-        'name this first unit',
-        'name this first united state p',
-        'name this first united state president.']
-    :param char_skip: Number of characters to skip each time
-    """
-    char_indices = list(range(char_skip, len(text) + char_skip, char_skip))
-    return [(text[:idx], idx) for idx in char_indices]
-
-
-def with_default(key, lookup, default):
-    if key in lookup:
-        value = lookup[key]
-        if value is None:
-            return default
-        else:
-            return value
-    else:
-        return default
-
-
-def question_to_examples(question, mode: str, char_skip: int):
-    features = {
-        "qanta_id": question["qanta_id"],
-        "proto_id": with_default("proto_id", question, ""),
-        "qdb_id": with_default("qdb_id", question, -1),
-        # We refer to the actual answer as page, but this
-        # may be misleading externally, so rename here to
-        # be clearer
-        "page": question["page"],
-        "answer": question["page"],
-        "raw_answer": question["answer"],
-        "dataset": with_default("dataset", question, ""),
-        "full_question": question["text"],
-        "first_sentence": question["first_sentence"],
-        "tokenizations": question["tokenizations"],
-        "fold": question["fold"],
-        "gameplay": question["gameplay"],
-        "category": with_default("category", question, ""),
-        "subcategory": with_default("subcategory", question, ""),
-        "tournament": question["tournament"],
-        "difficulty": with_default("difficulty", question, ""),
-        "year": question["year"],
-        "char_idx": -1,
-        "sentence_idx": -1,
-    }
-    if mode == _FULL:
-        yield {
-            "text": question["text"],
-            "id": str(question["qanta_id"]) + "-full",
-            **features,
-        }
-    elif mode == _FIRST:
-        yield {
-            "text": question["first_sentence"],
-            "id": str(question["qanta_id"]) + "-first",
-            **features,
-        }
-    elif mode == _RUNS:
-        text = question["text"]
-        for text_run, char_idx in create_char_runs(text, char_skip):
-            yield {
-                "text": text_run,
-                "char_idx": char_idx,
-                "id": str(question["qanta_id"]) + "-char-" + str(char_idx),
-                **features,
-            }
-    elif mode == _SENTENCES:
-        for sentence_idx, (start, end) in enumerate(question["tokenizations"]):
-            sentence = question["text"][start:end]
-            yield {
-                "text": sentence,
-                "sentence_idx": sentence_idx,
-                "id": str(question["qanta_id"]) + "-sentence-" + str(sentence_idx),
-                **features,
-            }
-    else:
-        raise ValueError(f"Invalid mode: {mode}")
-
-
-_FEATURES = {
-    # Generated ID based modes set, unique
-    "id": datasets.Value("string"),
-    # Dataset defined IDs
-    "qanta_id": datasets.Value("int32"),
-    "proto_id": datasets.Value("string"),
-    "qdb_id": datasets.Value("int32"),
-    "dataset": datasets.Value("string"),
-    # Inputs
-    "text": datasets.Value("string"),
-    "full_question": datasets.Value("string"),
-    "first_sentence": datasets.Value("string"),
-    "char_idx": datasets.Value("int32"),
-    "sentence_idx": datasets.Value("int32"),
-    # Character indices of sentences: List[Tuple[int, int]]
-    "tokenizations": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("int32"), length=2)),
-    # Labels: Number is equal to number of unique pages across all folds
-    "answer": datasets.Value("string"),
-    "page": datasets.Value("string"),
-    "raw_answer": datasets.Value("string"),
-    # Meta Information
-    "fold": datasets.Value("string"),
-    "gameplay": datasets.Value("bool"),
-    "category": datasets.Value("string"),
-    "subcategory": datasets.Value("string"),
-    "tournament": datasets.Value("string"),
-    "difficulty": datasets.Value("string"),
-    "year": datasets.Value("int32"),
-}
-
-
-class Qanta(datasets.GeneratorBasedBuilder):
-    """The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl."""
-
-    VERSION = _VERSION
-    BUILDER_CONFIGS = [
-        QantaConfig(
-            name=f"mode={mode},char_skip={_DEFAULT_CHAR_SKIP}",
-            description=f"Question format: {mode}, char_skip: {_DEFAULT_CHAR_SKIP}",
-            mode=mode,
-            char_skip=_DEFAULT_CHAR_SKIP,
-        )
-        for mode in _MODES
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(_FEATURES),
-            # Number of classes is a function of the dataset, ClassLabel doesn't support dynamic
-            # definition, so have to defer conversion to classes to later, so can't define
-            # supervied keys
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="http://www.qanta.org/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        qanta_path = dl_manager.download_and_extract(_QANTA_URL)
-        trick_path = dl_manager.download_and_extract(_TRICK_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split("guesstrain"),
-                gen_kwargs={
-                    "qanta_filepath": qanta_path,
-                    "trick_filepath": trick_path,
-                    "fold": "guesstrain",
-                    "mode": self.config.mode,
-                    "char_skip": self.config.char_skip,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split("buzztrain"),
-                gen_kwargs={
-                    "qanta_filepath": qanta_path,
-                    "trick_filepath": trick_path,
-                    "fold": "buzztrain",
-                    "mode": self.config.mode,
-                    "char_skip": self.config.char_skip,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split("guessdev"),
-                gen_kwargs={
-                    "qanta_filepath": qanta_path,
-                    "trick_filepath": trick_path,
-                    "fold": "guessdev",
-                    "mode": self.config.mode,
-                    "char_skip": self.config.char_skip,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split("buzzdev"),
-                gen_kwargs={
-                    "qanta_filepath": qanta_path,
-                    "trick_filepath": trick_path,
-                    "fold": "buzzdev",
-                    "mode": self.config.mode,
-                    "char_skip": self.config.char_skip,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split("guesstest"),
-                gen_kwargs={
-                    "qanta_filepath": qanta_path,
-                    "trick_filepath": trick_path,
-                    "fold": "guesstest",
-                    "mode": self.config.mode,
-                    "char_skip": self.config.char_skip,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split("buzztest"),
-                gen_kwargs={
-                    "qanta_filepath": qanta_path,
-                    "trick_filepath": trick_path,
-                    "fold": "buzztest",
-                    "mode": self.config.mode,
-                    "char_skip": self.config.char_skip,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split("adversarial"),
-                gen_kwargs={
-                    "qanta_filepath": qanta_path,
-                    "trick_filepath": trick_path,
-                    "fold": "adversarial",
-                    "mode": self.config.mode,
-                    "char_skip": self.config.char_skip,
-                },
-            ),
-        ]
-
-    def _generate_examples(
-        self,
-        qanta_filepath: str,
-        trick_filepath: str,
-        fold: str,
-        mode: str,
-        char_skip: int,
-    ):
-        """Yields examples."""
-        if mode not in _MODES:
-            raise ValueError(f"Invalid mode: {mode}")
-
-        if fold == "adversarial":
-            path = trick_filepath
-        else:
-            path = qanta_filepath
-        with open(path, encoding="utf-8") as f:
-            questions = json.load(f)["questions"]
-            for q in questions:
-                if q["page"] is not None and q["fold"] == fold:
-                    for example in question_to_examples(q, mode, char_skip):
-                        yield example["id"], example