Delete loading script

swedish_reviews.py

@@ -1,82 +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
-
-
-import csv
-import os
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DOWNLOAD_URL = "https://raw.githubusercontent.com/timpal0l/swedish-sentiment/main/swedish_sentiment.zip"
-_TRAIN_FILE = "train.csv"
-_VAL_FILE = "dev.csv"
-_TEST_FILE = "test.csv"
-
-_CITATION = ""
-
-_DESCRIPTION = "Swedish reviews scarped from various public available websites"
-
-
-class SwedishReviews(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Plain text import of the Swedish Reviews dataset",
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {"text": datasets.Value("string"), "label": datasets.ClassLabel(names=["negative", "positive"])}
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/timpal0l/swedish-sentiment",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": os.path.join(dl_dir, _TEST_FILE)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": os.path.join(dl_dir, _VAL_FILE)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": os.path.join(dl_dir, _TRAIN_FILE)},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        with open(filepath, encoding="utf-8") as f:
-            reader = csv.DictReader(f)
-            for idx, row in enumerate(reader):
-                yield idx, {
-                    "text": row["text"],
-                    "label": row["sentiment"],
-                }