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README.md

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-For details, please refer to the following links.
-
-Github repo: https://github.com/amazon-research/SC2QA-DRIL
-
-Paper: [Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning](https://arxiv.org/pdf/2109.04689.pdf)

val.csv → plain_text/sc2q_commoncrawl_large-test.parquet

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test.csv → plain_text/sc2q_commoncrawl_large-train-00000-of-00004.parquet

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train.csv → plain_text/sc2q_commoncrawl_large-train-00001-of-00004.parquet

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plain_text/sc2q_commoncrawl_large-train-00002-of-00004.parquet

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plain_text/sc2q_commoncrawl_large-train-00003-of-00004.parquet

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plain_text/sc2q_commoncrawl_large-validation.parquet

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sc2q_commoncrawl_large.py

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-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-"""(SC)^2QA: Self-Contained Summary-Centric QA Dataset.
-This dataset (https://huggingface.co/datasets/sc2qa/sc2q_commoncrawl_large) contains 529,039 question and article pairs.
-If you want {Question, Article, Summary, Length Constraint} 4-tuples, please load sc2qa_commoncrawl (https://huggingface.co/datasets/sc2qa/sc2qa_commoncrawl)
-"""
-
-import csv
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@article{zhou2021generating,
-       author = {Li Zhou, Kevin Small, Yong Zhang, Sandeep Atluri},
-       title = "{Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning}",
-       conference = {The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021)},
-       year = 2021,
-}
-"""
-
-_DESCRIPTION = """\
-"""
-
-_URLS = {
-        "train":"https://huggingface.co/datasets/sc2qa/sc2q_commoncrawl_large/resolve/main/train.csv",
-        "val":"https://huggingface.co/datasets/sc2qa/sc2q_commoncrawl_large/resolve/main/val.csv",
-        "test":"https://huggingface.co/datasets/sc2qa/sc2q_commoncrawl_large/resolve/main/test.csv",
-}
-
-class SC2QAConfig(datasets.BuilderConfig):
-    """BuilderConfig for (SC)^2QA."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for (SC)^2QA.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(SC2QAConfig, self).__init__(**kwargs)
-
-
-class SC2QA(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        SC2QAConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Plain text",
-        ),
-    ]
-
-    def _info(self):
-        # Should return a datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "question": datasets.Value("string"),
-                    "article": datasets.Value("string"),
-                    "url": datasets.Value("string"),
-                }
-            ),
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_files = dl_manager.download_and_extract(_URLS)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["val"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-        key = 0
-        with open(filepath, encoding="ascii", errors='ignore') as f:
-            csv_reader = csv.DictReader(f)
-            for i, row in enumerate(csv_reader):
-                yield i, row