Delete loading script

imdb.py

@@ -1,111 +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
-"""IMDB movie reviews dataset."""
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DESCRIPTION = """\
-Large Movie Review Dataset.
-This is a dataset for binary sentiment classification containing substantially \
-more data than previous benchmark datasets. We provide a set of 25,000 highly \
-polar movie reviews for training, and 25,000 for testing. There is additional \
-unlabeled data for use as well.\
-"""
-
-_CITATION = """\
-@InProceedings{maas-EtAl:2011:ACL-HLT2011,
-  author    = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},
-  title     = {Learning Word Vectors for Sentiment Analysis},
-  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
-  month     = {June},
-  year      = {2011},
-  address   = {Portland, Oregon, USA},
-  publisher = {Association for Computational Linguistics},
-  pages     = {142--150},
-  url       = {http://www.aclweb.org/anthology/P11-1015}
-}
-"""
-
-_DOWNLOAD_URL = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"
-
-
-class IMDBReviewsConfig(datasets.BuilderConfig):
-    """BuilderConfig for IMDBReviews."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for IMDBReviews.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(IMDBReviewsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-
-
-class Imdb(datasets.GeneratorBasedBuilder):
-    """IMDB movie reviews dataset."""
-
-    BUILDER_CONFIGS = [
-        IMDBReviewsConfig(
-            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=["neg", "pos"])}
-            ),
-            supervised_keys=None,
-            homepage="http://ai.stanford.edu/~amaas/data/sentiment/",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        archive = dl_manager.download(_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "test"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split("unsupervised"),
-                gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train", "labeled": False},
-            ),
-        ]
-
-    def _generate_examples(self, files, split, labeled=True):
-        """Generate aclImdb examples."""
-        # For labeled examples, extract the label from the path.
-        if labeled:
-            label_mapping = {"pos": 1, "neg": 0}
-            for path, f in files:
-                if path.startswith(f"aclImdb/{split}"):
-                    label = label_mapping.get(path.split("/")[2])
-                    if label is not None:
-                        yield path, {"text": f.read().decode("utf-8"), "label": label}
-        else:
-            for path, f in files:
-                if path.startswith(f"aclImdb/{split}"):
-                    if path.split("/")[2] == "unsup":
-                        yield path, {"text": f.read().decode("utf-8"), "label": -1}