Delete loading script

senti_ws.py

@@ -1,132 +0,0 @@
-# coding=utf-8
-# Copyright 2020 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.
-"""SentiWS: German-language resource for sentiment analysis, pos-tagging"""
-
-
-import os
-
-import datasets
-
-
-_CITATION = """\
-@INPROCEEDINGS{remquahey2010,
-title = {SentiWS -- a Publicly Available German-language Resource for Sentiment Analysis},
-booktitle = {Proceedings of the 7th International Language Resources and Evaluation (LREC'10)},
-author = {Remus, R. and Quasthoff, U. and Heyer, G.},
-year = {2010}
-}
-"""
-
-_DESCRIPTION = """\
-SentimentWortschatz, or SentiWS for short, is a publicly available German-language resource for sentiment analysis, and pos-tagging. The POS tags are ["NN", "VVINF", "ADJX", "ADV"] -> ["noun", "verb", "adjective", "adverb"], and positive and negative polarity bearing words are weighted within the interval of [-1, 1].
-"""
-
-_HOMEPAGE = ""
-
-_LICENSE = "Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License"
-
-_URLs = ["https://downloads.wortschatz-leipzig.de/etc/SentiWS/SentiWS_v2.0.zip"]
-
-
-class SentiWS(datasets.GeneratorBasedBuilder):
-    """SentiWS: German-language resource for sentiment analysis, pos-tagging"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="pos-tagging", version=VERSION, description="This covers pos-tagging task"),
-        datasets.BuilderConfig(
-            name="sentiment-scoring",
-            version=VERSION,
-            description="This covers the sentiment-scoring in [-1, 1] corresponding to (negative, positive) sentiment",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "pos-tagging"
-
-    def _info(self):
-
-        if (
-            self.config.name == "pos-tagging"
-        ):  # the pos-tags are ["NN", "VVINF", "ADJX", "ADV"] -> ["noun", "verb", "adjective", "adverb"]
-            features = datasets.Features(
-                {
-                    "word": datasets.Value("string"),
-                    "pos-tag": datasets.ClassLabel(names=["NN", "VVINF", "ADJX", "ADV"]),
-                }
-            )
-        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
-            features = datasets.Features(
-                {
-                    "word": datasets.Value("string"),
-                    "sentiment-score": datasets.Value("float32"),
-                }
-            )
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        my_urls = _URLs
-        data_dir = dl_manager.download_and_extract(my_urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "sourcefiles": [
-                        os.path.join(data_dir[0], f)
-                        for f in ["SentiWS_v2.0_Positive.txt", "SentiWS_v2.0_Negative.txt"]
-                    ],
-                    "split": "train",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, sourcefiles, split):
-        """Yields examples."""
-        # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
-        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
-        # The key is not important, it's more here for legacy reason (legacy from tfds)
-        for file_idx, filepath in enumerate(sourcefiles):
-            with open(filepath, encoding="utf-8") as f:
-                for id_, row in enumerate(f):
-                    word = row.split("|")[0]
-                    if self.config.name == "pos-tagging":
-                        tag = row.split("|")[1].split("\t")[0]
-                        yield f"{file_idx}_{id_}", {"word": word, "pos-tag": tag}
-                    else:
-                        sentiscore = row.split("|")[1].split("\t")[1]
-                        yield f"{file_idx}_{id_}", {"word": word, "sentiment-score": float(sentiscore)}