Delete loading script

labr.py

@@ -1,116 +0,0 @@
-# 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
-"""Arabic Book Reviews."""
-
-
-import csv
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DESCRIPTION = """\
-This dataset contains over 63,000 book reviews in Arabic.\
-It is the largest sentiment analysis dataset for Arabic to-date.\
-The book reviews were harvested from the website Goodreads during the month or March 2013.\
-Each book review comes with the goodreads review id, the user id, the book id, the rating (1 to 5) and the text of the review.
-"""
-
-_CITATION = """\
-@inproceedings{aly2013labr,
-  title={Labr: A large scale arabic book reviews dataset},
-  author={Aly, Mohamed and Atiya, Amir},
-  booktitle={Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
-  pages={494--498},
-  year={2013}
-}
-"""
-
-_URL = "https://raw.githubusercontent.com/mohamedadaly/LABR/master/data/"
-_URLS = {
-    "train": _URL + "5class-balanced-train.txt",
-    "test": _URL + "5class-balanced-test.txt",
-    "reviews": _URL + "reviews.tsv",
-}
-
-
-class LabrConfig(datasets.BuilderConfig):
-    """BuilderConfig for Labr."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for Labr.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(LabrConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-
-
-class Labr(datasets.GeneratorBasedBuilder):
-    """Labr dataset."""
-
-    BUILDER_CONFIGS = [
-        LabrConfig(
-            name="plain_text",
-            description="Plain text",
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "text": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(
-                        names=[
-                            "1",
-                            "2",
-                            "3",
-                            "4",
-                            "5",
-                        ]
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/mohamedadaly/LABR",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download(_URLS)
-        self.reviews_path = data_dir["reviews"]
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"directory": data_dir["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"directory": data_dir["test"]}),
-        ]
-
-    def _generate_examples(self, directory):
-        """Generate examples."""
-        # For labeled examples, extract the label from the path.
-        reviews = []
-        with open(self.reviews_path, encoding="utf-8") as tsvfile:
-            tsvreader = csv.reader(tsvfile, delimiter="\t")
-            for line in tsvreader:
-                reviews.append(line)
-
-        with open(directory, encoding="utf-8") as f:
-            for id_, record in enumerate(f.read().splitlines()):
-                rating, _, _, _, review_text = reviews[int(record)]
-                yield str(id_), {"text": review_text, "label": rating}