Delete loading script

sst2.py

@@ -1,105 +0,0 @@
-# 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.
-"""SST-2 (Stanford Sentiment Treebank v2) dataset."""
-
-
-import csv
-import os
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{socher2013recursive,
-  title={Recursive deep models for semantic compositionality over a sentiment treebank},
-  author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},
-  booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},
-  pages={1631--1642},
-  year={2013}
-}
-"""
-
-_DESCRIPTION = """\
-The Stanford Sentiment Treebank consists of sentences from movie reviews and
-human annotations of their sentiment. The task is to predict the sentiment of a
-given sentence. We use the two-way (positive/negative) class split, and use only
-sentence-level labels.
-"""
-
-_HOMEPAGE = "https://nlp.stanford.edu/sentiment/"
-
-_LICENSE = "Unknown"
-
-_URL = "https://dl.fbaipublicfiles.com/glue/data/SST-2.zip"
-
-
-class Sst2(datasets.GeneratorBasedBuilder):
-    """SST-2 dataset."""
-
-    VERSION = datasets.Version("2.0.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "idx": datasets.Value("int32"),
-                "sentence": datasets.Value("string"),
-                "label": datasets.features.ClassLabel(names=["negative", "positive"]),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "file_paths": dl_manager.iter_files(dl_dir),
-                    "data_filename": "train.tsv",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "file_paths": dl_manager.iter_files(dl_dir),
-                    "data_filename": "dev.tsv",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "file_paths": dl_manager.iter_files(dl_dir),
-                    "data_filename": "test.tsv",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, file_paths, data_filename):
-        for file_path in file_paths:
-            filename = os.path.basename(file_path)
-            if filename == data_filename:
-                with open(file_path, encoding="utf8") as f:
-                    reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-                    for idx, row in enumerate(reader):
-                        yield idx, {
-                            "idx": row["index"] if "index" in row else idx,
-                            "sentence": row["sentence"],
-                            "label": int(row["label"]) if "label" in row else -1,
-                        }