Delete loading script

rotten_tomatoes.py

@@ -1,121 +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
-"""Rotten tomatoes movie reviews dataset."""
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DESCRIPTION = """\
-Movie Review Dataset.
-This is a dataset of containing 5,331 positive and 5,331 negative processed
-sentences from Rotten Tomatoes movie reviews. This data was first used in Bo
-Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for
-sentiment categorization with respect to rating scales.'', Proceedings of the
-ACL, 2005.
-"""
-
-_CITATION = """\
-@InProceedings{Pang+Lee:05a,
-  author =       {Bo Pang and Lillian Lee},
-  title =        {Seeing stars: Exploiting class relationships for sentiment
-                  categorization with respect to rating scales},
-  booktitle =    {Proceedings of the ACL},
-  year =         2005
-}
-"""
-
-_DOWNLOAD_URL = "https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz"
-
-
-class RottenTomatoesMovieReview(datasets.GeneratorBasedBuilder):
-    """Cornell Rotten Tomatoes movie reviews dataset."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["neg", "pos"])}
-            ),
-            supervised_keys=[""],
-            homepage="http://www.cs.cornell.edu/people/pabo/movie-review-data/",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Downloads Rotten Tomatoes sentences."""
-        archive = dl_manager.download(_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"split_key": "train", "files": dl_manager.iter_archive(archive)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"split_key": "validation", "files": dl_manager.iter_archive(archive)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"split_key": "test", "files": dl_manager.iter_archive(archive)},
-            ),
-        ]
-
-    def _get_examples_from_split(self, split_key, files):
-        """Reads Rotten Tomatoes sentences and splits into 80% train,
-        10% validation, and 10% test, as is the practice set out in Jinfeng
-        Li, ``TEXTBUGGER: Generating Adversarial Text Against Real-world
-        Applications.''
-        """
-        data_dir = "rt-polaritydata/"
-        pos_samples, neg_samples = None, None
-        for path, f in files:
-            if path == data_dir + "rt-polarity.pos":
-                pos_samples = [line.decode("latin-1").strip() for line in f]
-            elif path == data_dir + "rt-polarity.neg":
-                neg_samples = [line.decode("latin-1").strip() for line in f]
-            if pos_samples is not None and neg_samples is not None:
-                break
-
-        # 80/10/10 split
-        i1 = int(len(pos_samples) * 0.8 + 0.5)
-        i2 = int(len(pos_samples) * 0.9 + 0.5)
-        train_samples = pos_samples[:i1] + neg_samples[:i1]
-        train_labels = (["pos"] * i1) + (["neg"] * i1)
-        validation_samples = pos_samples[i1:i2] + neg_samples[i1:i2]
-        validation_labels = (["pos"] * (i2 - i1)) + (["neg"] * (i2 - i1))
-        test_samples = pos_samples[i2:] + neg_samples[i2:]
-        test_labels = (["pos"] * (len(pos_samples) - i2)) + (["neg"] * (len(pos_samples) - i2))
-
-        if split_key == "train":
-            return (train_samples, train_labels)
-        if split_key == "validation":
-            return (validation_samples, validation_labels)
-        if split_key == "test":
-            return (test_samples, test_labels)
-        else:
-            raise ValueError(f"Invalid split key {split_key}")
-
-    def _generate_examples(self, split_key, files):
-        """Yields examples for a given split of MR."""
-        split_text, split_labels = self._get_examples_from_split(split_key, files)
-        for text, label in zip(split_text, split_labels):
-            data_key = split_key + "_" + text
-            feature_dict = {"text": text, "label": label}
-            yield data_key, feature_dict