Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
License:
Commit
·
283dc80
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Parent(s):
f817a2b
Delete loading script
Browse files
sst2.py
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""SST-2 (Stanford Sentiment Treebank v2) dataset."""
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import csv
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import os
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import datasets
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_CITATION = """\
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@inproceedings{socher2013recursive,
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title={Recursive deep models for semantic compositionality over a sentiment treebank},
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author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},
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booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},
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pages={1631--1642},
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year={2013}
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}
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"""
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_DESCRIPTION = """\
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The Stanford Sentiment Treebank consists of sentences from movie reviews and
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human annotations of their sentiment. The task is to predict the sentiment of a
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given sentence. We use the two-way (positive/negative) class split, and use only
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sentence-level labels.
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"""
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_HOMEPAGE = "https://nlp.stanford.edu/sentiment/"
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_LICENSE = "Unknown"
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_URL = "https://dl.fbaipublicfiles.com/glue/data/SST-2.zip"
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class Sst2(datasets.GeneratorBasedBuilder):
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"""SST-2 dataset."""
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VERSION = datasets.Version("2.0.0")
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def _info(self):
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features = datasets.Features(
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{
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"idx": datasets.Value("int32"),
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"sentence": datasets.Value("string"),
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"label": datasets.features.ClassLabel(names=["negative", "positive"]),
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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dl_dir = dl_manager.download_and_extract(_URL)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"file_paths": dl_manager.iter_files(dl_dir),
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"data_filename": "train.tsv",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"file_paths": dl_manager.iter_files(dl_dir),
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"data_filename": "dev.tsv",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"file_paths": dl_manager.iter_files(dl_dir),
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"data_filename": "test.tsv",
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},
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),
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]
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def _generate_examples(self, file_paths, data_filename):
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for file_path in file_paths:
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filename = os.path.basename(file_path)
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if filename == data_filename:
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with open(file_path, encoding="utf8") as f:
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reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
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for idx, row in enumerate(reader):
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yield idx, {
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"idx": row["index"] if "index" in row else idx,
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"sentence": row["sentence"],
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"label": int(row["label"]) if "label" in row else -1,
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}
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