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Datasets:
MicPie
/
unpredictable_sporcle-com

Tasks:
Multiple Choice
Question Answering
Zero-Shot Classification
Modalities:
Text
Sub-tasks:
multiple-choice-qa
extractive-qa
open-domain-qa
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
Libraries:
Datasets
Croissant
License:
Dataset card Viewer Files Community
unpredictable_sporcle-com
File size: 3,576 Bytes 
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# 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.
"""This loads the AdapTable-sporcle.com dataset."""

import json
import os
import pandas as pd

import datasets


# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {TODO: UPDATE TITLE HERE},
author={TODO: BUT AUTHORS HERE},
year={2020}
}
"""

# TODO: Update description
_DESCRIPTION = """\
The AdapTable dataset consists of tables that naturally occur on the web, that are formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

_LICENSE = "Apache 2.0"

_URL = "https://huggingface.co/datasets/MicPie/adaptable_sporcle.com/resolve/main/data/adaptable_sporcle.com.jsonl"

logger = datasets.logging.get_logger(__name__)


class AdapTableSite(datasets.GeneratorBasedBuilder):
    # TODO: Update docs:
    """
    The AdapTable dataset consists of tables that naturally occur on the web, that are formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card.
    """

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "task": datasets.Value("string"),
                "input": datasets.Value("string"),
                "output": datasets.Value("string"),
                "options": datasets.Sequence([datasets.Value("string")]),
                "pageTitle": datasets.Value("string"),
                "outputColName": datasets.Value("string"),
                "url": datasets.Value("string"),
                "wdcFile": datasets.Value("string")
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        data_dir = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": data_dir},
            ),
        ]

    def _generate_examples(self, filepath):
        """Yields examples."""
        with open(filepath, encoding="utf-8") as f:
            for i, row in enumerate(f):
                data = json.loads(row)
                key = f"{data['task']}_{i}"
                yield key, {
                    "task": data["task"],
                    "input": data["input"],
                    "output": data["output"],
                    "options": data["options"],
                    "pageTitle": data["pageTitle"],
                    "outputColName": data["outputColName"],
                    "url": data["url"],
                    "wdcFile": data["wdcFile"],
                }