on_the_books.py

# coding=utf-8
# Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""On the Books Dataset"""

import csv

import datasets


_CITATION = """TODO"""

_DESCRIPTION = """\
This file is the training set that was used to train an algorithm to identify Jim Crow laws.
It contains laws that are labeled as "Jim Crow" (jim_crow=1) or "Not Jim Crow" (jim_crow=0).
The source of the determination is also provided.

"""

_HOMEPAGE = "https://onthebooks.lib.unc.edu/"

_LICENSE = "CC BY 3.0"

_URL = "https://cdr.lib.unc.edu/downloads/76537b20b?locale=en"


class OnTheBooks(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.1.0")

    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "source": datasets.Value("string"),
                "jim_crow": datasets.ClassLabel(names=["no_jim_crow", "jim_crow"]),
                "type": datasets.Value("string"),
                "chapter_num": datasets.Value("int32"),
                "section_num": datasets.Value("int32"),
                "chapter_text": datasets.Value("string"),
                "section_text": datasets.Value("string"),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""

        data_file = dl_manager.download(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": data_file,
                },
            ),
        ]

    def _generate_examples(self, filepath):
        """Yields examples as (key, example) tuples."""
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f)
            yield from enumerate(reader)