Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
License:
# 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 | |
"""Rotten tomatoes movie reviews dataset.""" | |
import os | |
import datasets | |
_DESCRIPTION = """\ | |
Movie Review Dataset. | |
This is a dataset of containing 5,331 positive and 5,331 negative processed | |
sentences from Rotten Tomatoes movie reviews. This data was first used in Bo | |
Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for | |
sentiment categorization with respect to rating scales.'', Proceedings of the | |
ACL, 2005. | |
""" | |
_CITATION = """\ | |
@InProceedings{Pang+Lee:05a, | |
author = {Bo Pang and Lillian Lee}, | |
title = {Seeing stars: Exploiting class relationships for sentiment | |
categorization with respect to rating scales}, | |
booktitle = {Proceedings of the ACL}, | |
year = 2005 | |
} | |
""" | |
_DOWNLOAD_URL = "https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz" | |
class RottenTomatoesMovieReview(datasets.GeneratorBasedBuilder): | |
"""Cornell Rotten Tomatoes movie reviews dataset.""" | |
VERSION = datasets.Version("1.0.0") | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["neg", "pos"])} | |
), | |
supervised_keys=[""], | |
homepage="http://www.cs.cornell.edu/people/pabo/movie-review-data/", | |
citation=_CITATION, | |
) | |
def _vocab_text_gen(self, train_file): | |
for _, ex in self._generate_examples(train_file): | |
yield ex["text"] | |
def _split_generators(self, dl_manager): | |
"""Downloads Rotten Tomatoes sentences.""" | |
extracted_folder_path = dl_manager.download_and_extract(_DOWNLOAD_URL) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={"split_key": "train", "data_dir": extracted_folder_path}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={"split_key": "validation", "data_dir": extracted_folder_path}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={"split_key": "test", "data_dir": extracted_folder_path}, | |
), | |
] | |
def _get_examples_from_split(self, split_key, data_dir): | |
"""Reads Rotten Tomatoes sentences and splits into 80% train, | |
10% validation, and 10% test, as is the practice set out in Jinfeng | |
Li, ``TEXTBUGGER: Generating Adversarial Text Against Real-world | |
Applications.'' | |
""" | |
data_dir = os.path.join(data_dir, "rt-polaritydata") | |
pos_samples = open(os.path.join(data_dir, "rt-polarity.pos"), encoding="latin-1").readlines() | |
pos_samples = list(map(lambda t: t.strip(), pos_samples)) | |
neg_samples = open(os.path.join(data_dir, "rt-polarity.neg"), encoding="latin-1").readlines() | |
neg_samples = list(map(lambda t: t.strip(), neg_samples)) | |
# 80/10/10 split | |
i1 = int(len(pos_samples) * 0.8 + 0.5) | |
i2 = int(len(pos_samples) * 0.9 + 0.5) | |
train_samples = pos_samples[:i1] + neg_samples[:i1] | |
train_labels = (["pos"] * i1) + (["neg"] * i1) | |
validation_samples = pos_samples[i1:i2] + neg_samples[i1:i2] | |
validation_labels = (["pos"] * (i2 - i1)) + (["neg"] * (i2 - i1)) | |
test_samples = pos_samples[i2:] + neg_samples[i2:] | |
test_labels = (["pos"] * (len(pos_samples) - i2)) + (["neg"] * (len(pos_samples) - i2)) | |
if split_key == "train": | |
return (train_samples, train_labels) | |
if split_key == "validation": | |
return (validation_samples, validation_labels) | |
if split_key == "test": | |
return (test_samples, test_labels) | |
else: | |
raise ValueError(f"Invalid split key {split_key}") | |
def _generate_examples(self, split_key, data_dir): | |
"""Yields examples for a given split of MR.""" | |
split_text, split_labels = self._get_examples_from_split(split_key, data_dir) | |
for text, label in zip(split_text, split_labels): | |
data_key = split_key + "_" + text | |
feature_dict = {"text": text, "label": label} | |
yield data_key, feature_dict | |