Delete loading script

on_the_books.py

@@ -1,81 +0,0 @@
-# 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)