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.
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# 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__)


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

_URLS = {
    "train": "data/topiocqa_train.jsonl",
    "valid": "data/topiocqa_valid.jsonl",
}


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

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

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


class TopiOCQA(datasets.GeneratorBasedBuilder):
    """TopiOCQA: Open-domain Conversational Question Answering with Topic Switching"""

    BUILDER_CONFIGS = [
        TopiOCQAConfig(
            name="plain_text",
            version=datasets.Version("1.0.1", ""),
            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"),
                        }
                    ),
                    "Gold_passage": {
                        "id": datasets.Value("string"),
                        "title": datasets.Value("string"),
                        "text": datasets.Value("string"),
                    }
                }
            ),
            supervised_keys=None,
            homepage="https://mcgill-nlp.github.io/topiocqa/",
        )

    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:
            for line in f:
                data = json.loads(line)
                yield key, {
                    "Conversation_no": data["Conversation_no"],
                    "Turn_no": data["Turn_no"],
                    "Question": data["Question"],
                    "Answer": data["Answer"],
                    "Topic": data["Topic"],
                    "Topic_section": data["Topic_section"],
                    "Rationale": data["Rationale"],
                    "is_nq": data["is_nq"],
                    "Context": data["Context"],
                    "Additional_answers": data["Additional_answers"],
                    "Gold_passage": data["Gold_passage"],
                }
                key += 1