TopiOCQA.py

# 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
"""TopiOCQA: Open-domain Conversational Question Answering with Topic Switching"""


import json

import datasets
# from datasets.tasks import QuestionAnsweringExtractive


logger = datasets.logging.get_logger(__name__)


# _CITATION = """\
# @article{2016arXiv160605250R,
#        author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
#                  Konstantin and {Liang}, Percy},
#         title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
#       journal = {arXiv e-prints},
#          year = 2016,
#           eid = {arXiv:1606.05250},
#         pages = {arXiv:1606.05250},
# archivePrefix = {arXiv},
#        eprint = {1606.05250},
# }
# """

_DESCRIPTION = """\
TopiOCQA is an information-seeking conversational dataset with challenging topic switching phenomena.
"""

# _URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
_URLS = {
    "train": "data/topiocqa_train.json",
    "valid": "data/topiocqa_valid.json",
}


class TopiOCQAConfig(datasets.BuilderConfig):
    """BuilderConfig for SQUAD."""

    def __init__(self, **kwargs):
        """BuilderConfig for TopiOCQA.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(TopiOCQAConfig, self).__init__(**kwargs)


class Squad(datasets.GeneratorBasedBuilder):
    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""

    BUILDER_CONFIGS = [
        TopiOCQAConfig(
            name="plain_text",
            version=datasets.Version("1.0.0", ""),
            description="Plain text",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "Conversation_no": datasets.Value("int32"),
                    "Turn_no": datasets.Value("int32"),
                    "Question": datasets.Value("string"),
                    "Answer": datasets.Value("string"),
                    "Topic": datasets.Value("string"),
                    "Topic_section": datasets.Value("string"),
                    "Rationale": datasets.Value("string"),
                    "is_nq": datasets.Value("bool"),
                    "Context": datasets.features.Sequence(datasets.Value("string")),
                    "Additional_answers": datasets.features.Sequence(
                        {
                            "Answer": datasets.Value("string"),
                            "Topic": datasets.Value("string"),
                            "Topic_section": datasets.Value("string"),
                            "Rationale": datasets.Value("string"),
                        }
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://mcgill-nlp.github.io/topiocqa/",
            # citation=_CITATION,
            # task_templates=[
            #     QuestionAnsweringExtractive(
            #         question_column="Question", context_column="context", answers_column="answers"
            #     )
            # ],
        )

    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["valid"]}),
        ]

    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="utf-8") as f:
            topiocqa = json.load(f)
            for turn in topiocqa:
                yield key, turn
                key += 1