squad_modified_for_t5_qg.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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""SQUAD: The Stanford Question Answering Dataset."""
"""Modified version for fine tuning T5 on Question Generation """

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 = """\
Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
articles, where the answer to every question is a segment of text, or span, \
from the corresponding reading passage, or the question might be unanswerable.
"""

_URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
_URLS = {
    "train": _URL + "train-v1.1.json",
    "dev": _URL + "dev-v1.1.json",
    }


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

    def __init__(self, **kwargs):
        """BuilderConfig for SQUAD.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(SquadConfig, self).__init__(**kwargs)


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

    CONTEXT_PREFIX = 'gq: '
    QUESTIONS_SEP = ' Question: '
    BUILDER_CONFIGS = [
        SquadConfig(
                name="plain_text",
                version=datasets.Version("2.9.0", ""),
                description="Plain text",
                ),
        ]

    def _info(self):
        return datasets.DatasetInfo(
                description=_DESCRIPTION,
                features=datasets.Features(
                        {
                            "context": datasets.Value("string"),
                            "questions": datasets.Value("string"),
                            }
                        ),
                # No default supervised_keys (as we have to pass both question
                # and context as input).
                supervised_keys=None,
                homepage="https://rajpurkar.github.io/SQuAD-explorer/",
                citation=_CITATION,
                task_templates=[

                    ],
                )

    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["dev"]}),
            ]


    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:
            squad = json.load(f)
            for article in squad["data"]:
                for paragraph in article["paragraphs"]:
                    source_text = self.CONTEXT_PREFIX + paragraph['context'].strip()

                    # Get questions in order
                    qas = []
                    for qa in paragraph['qas']:
                        earliest_answer_start = min([answer['answer_start'] for answer in qa['answers']])
                        question = qa['question'].strip()
                        qas.append((earliest_answer_start, question))
                    sorted_qas = sorted(qas, key=lambda x: x[0])
                    only_qs = [qa[1] for qa in sorted_qas]
                    target_text = self.QUESTIONS_SEP + self.QUESTIONS_SEP.join(only_qs)
                    target_text = target_text.strip()
                    yield key, {
                        "context": source_text,
                        "questions": target_text}
                    key += 1