Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
License:
File size: 3,770 Bytes
3c35933 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
# 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,
}
|