demo2.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
"""SQUAD: The Stanford Question Answering Dataset."""
import csv
import json

import datasets


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.
"""

train_url = "https://raw.githubusercontent.com/Sampson2016/test/master/train.csv?token=GHSAT0AAAAAABR4XKTH73T5VNFVZ3KS33FYYVQLQAA"

_URLS = {
    "train": train_url,
    "test": train_url,
}


class Demo2Config(datasets.BuilderConfig):

    def __init__(self, **kwargs):
        super(Demo2Config, self).__init__(**kwargs)


class Demo2(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        Demo2Config(
            name="plain_text",
            version=datasets.Version("1.0.0", ""),
            description="Plain text",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(names=['0', '1'])
                }
            ),
            # 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,
        )

    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.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        logger.info("generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            demo2 = csv.DictReader(f)
            for key, row in enumerate(demo2):
                yield key, {
                    "text": row['text'],
                    "label": row['label'],
                }