Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
License:
Convert dataset to Parquet
#4
by
davzoku
- opened
- README.md +14 -5
- data/test-00000-of-00001.parquet +3 -0
- data/train-00000-of-00001.parquet +3 -0
- data/validation-00000-of-00001.parquet +3 -0
- dataset_infos.json +0 -1
- rotten_tomatoes.py +0 -121
README.md
CHANGED
@@ -31,16 +31,25 @@ dataset_info:
|
|
31 |
'1': pos
|
32 |
splits:
|
33 |
- name: train
|
34 |
-
num_bytes:
|
35 |
num_examples: 8530
|
36 |
- name: validation
|
37 |
-
num_bytes:
|
38 |
num_examples: 1066
|
39 |
- name: test
|
40 |
-
num_bytes:
|
41 |
num_examples: 1066
|
42 |
-
download_size:
|
43 |
-
dataset_size:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
44 |
train-eval-index:
|
45 |
- config: default
|
46 |
task: text-classification
|
|
|
31 |
'1': pos
|
32 |
splits:
|
33 |
- name: train
|
34 |
+
num_bytes: 1074806
|
35 |
num_examples: 8530
|
36 |
- name: validation
|
37 |
+
num_bytes: 134675
|
38 |
num_examples: 1066
|
39 |
- name: test
|
40 |
+
num_bytes: 135968
|
41 |
num_examples: 1066
|
42 |
+
download_size: 881052
|
43 |
+
dataset_size: 1345449
|
44 |
+
configs:
|
45 |
+
- config_name: default
|
46 |
+
data_files:
|
47 |
+
- split: train
|
48 |
+
path: data/train-*
|
49 |
+
- split: validation
|
50 |
+
path: data/validation-*
|
51 |
+
- split: test
|
52 |
+
path: data/test-*
|
53 |
train-eval-index:
|
54 |
- config: default
|
55 |
task: text-classification
|
data/test-00000-of-00001.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:960f73534d561dcac3f2606a331c05b95635ea5dd6cd7585fe0a866e58d16546
|
3 |
+
size 92206
|
data/train-00000-of-00001.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:88ed3800f8db5e7ae1404e86f20469e92fb57cf2d7519813b8a1cec599ea1a5e
|
3 |
+
size 698845
|
data/validation-00000-of-00001.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:53d17c4b866ded1e76951f386169a0881120a4edda9d4bb5bd94bc4ed4cdd5bc
|
3 |
+
size 90001
|
dataset_infos.json
DELETED
@@ -1 +0,0 @@
|
|
1 |
-
{"default": {"description": "Movie Review Dataset.\nThis is a dataset of containing 5,331 positive and 5,331 negative processed\nsentences from Rotten Tomatoes movie reviews. This data was first used in Bo\nPang and Lillian Lee, ``Seeing stars: Exploiting class relationships for\nsentiment categorization with respect to rating scales.'', Proceedings of the\nACL, 2005.\n", "citation": "@InProceedings{Pang+Lee:05a,\n author = {Bo Pang and Lillian Lee},\n title = {Seeing stars: Exploiting class relationships for sentiment\n categorization with respect to rating scales},\n booktitle = {Proceedings of the ACL},\n year = 2005\n}\n", "homepage": "http://www.cs.cornell.edu/people/pabo/movie-review-data/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["neg", "pos"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "", "output": ""}, "task_templates": [{"task": "text-classification", "text_column": "text", "label_column": "label", "labels": ["neg", "pos"]}], "builder_name": "rotten_tomatoes_movie_review", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1074810, "num_examples": 8530, "dataset_name": "rotten_tomatoes_movie_review"}, "validation": {"name": "validation", "num_bytes": 134679, "num_examples": 1066, "dataset_name": "rotten_tomatoes_movie_review"}, "test": {"name": "test", "num_bytes": 135972, "num_examples": 1066, "dataset_name": "rotten_tomatoes_movie_review"}}, "download_checksums": {"https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz": {"num_bytes": 487770, "checksum": "a05befe52aafda71d458d188a1c54506a998b1308613ba76bbda2e5029409ce9"}}, "download_size": 487770, "post_processing_size": null, "dataset_size": 1345461, "size_in_bytes": 1833231}}
|
|
|
|
rotten_tomatoes.py
DELETED
@@ -1,121 +0,0 @@
|
|
1 |
-
# coding=utf-8
|
2 |
-
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
|
3 |
-
#
|
4 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
-
# you may not use this file except in compliance with the License.
|
6 |
-
# You may obtain a copy of the License at
|
7 |
-
#
|
8 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
-
#
|
10 |
-
# Unless required by applicable law or agreed to in writing, software
|
11 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
-
# See the License for the specific language governing permissions and
|
14 |
-
# limitations under the License.
|
15 |
-
|
16 |
-
# Lint as: python3
|
17 |
-
"""Rotten tomatoes movie reviews dataset."""
|
18 |
-
|
19 |
-
import datasets
|
20 |
-
from datasets.tasks import TextClassification
|
21 |
-
|
22 |
-
|
23 |
-
_DESCRIPTION = """\
|
24 |
-
Movie Review Dataset.
|
25 |
-
This is a dataset of containing 5,331 positive and 5,331 negative processed
|
26 |
-
sentences from Rotten Tomatoes movie reviews. This data was first used in Bo
|
27 |
-
Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for
|
28 |
-
sentiment categorization with respect to rating scales.'', Proceedings of the
|
29 |
-
ACL, 2005.
|
30 |
-
"""
|
31 |
-
|
32 |
-
_CITATION = """\
|
33 |
-
@InProceedings{Pang+Lee:05a,
|
34 |
-
author = {Bo Pang and Lillian Lee},
|
35 |
-
title = {Seeing stars: Exploiting class relationships for sentiment
|
36 |
-
categorization with respect to rating scales},
|
37 |
-
booktitle = {Proceedings of the ACL},
|
38 |
-
year = 2005
|
39 |
-
}
|
40 |
-
"""
|
41 |
-
|
42 |
-
_DOWNLOAD_URL = "https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz"
|
43 |
-
|
44 |
-
|
45 |
-
class RottenTomatoesMovieReview(datasets.GeneratorBasedBuilder):
|
46 |
-
"""Cornell Rotten Tomatoes movie reviews dataset."""
|
47 |
-
|
48 |
-
VERSION = datasets.Version("1.0.0")
|
49 |
-
|
50 |
-
def _info(self):
|
51 |
-
return datasets.DatasetInfo(
|
52 |
-
description=_DESCRIPTION,
|
53 |
-
features=datasets.Features(
|
54 |
-
{"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["neg", "pos"])}
|
55 |
-
),
|
56 |
-
supervised_keys=[""],
|
57 |
-
homepage="http://www.cs.cornell.edu/people/pabo/movie-review-data/",
|
58 |
-
citation=_CITATION,
|
59 |
-
task_templates=[TextClassification(text_column="text", label_column="label")],
|
60 |
-
)
|
61 |
-
|
62 |
-
def _split_generators(self, dl_manager):
|
63 |
-
"""Downloads Rotten Tomatoes sentences."""
|
64 |
-
archive = dl_manager.download(_DOWNLOAD_URL)
|
65 |
-
return [
|
66 |
-
datasets.SplitGenerator(
|
67 |
-
name=datasets.Split.TRAIN,
|
68 |
-
gen_kwargs={"split_key": "train", "files": dl_manager.iter_archive(archive)},
|
69 |
-
),
|
70 |
-
datasets.SplitGenerator(
|
71 |
-
name=datasets.Split.VALIDATION,
|
72 |
-
gen_kwargs={"split_key": "validation", "files": dl_manager.iter_archive(archive)},
|
73 |
-
),
|
74 |
-
datasets.SplitGenerator(
|
75 |
-
name=datasets.Split.TEST,
|
76 |
-
gen_kwargs={"split_key": "test", "files": dl_manager.iter_archive(archive)},
|
77 |
-
),
|
78 |
-
]
|
79 |
-
|
80 |
-
def _get_examples_from_split(self, split_key, files):
|
81 |
-
"""Reads Rotten Tomatoes sentences and splits into 80% train,
|
82 |
-
10% validation, and 10% test, as is the practice set out in Jinfeng
|
83 |
-
Li, ``TEXTBUGGER: Generating Adversarial Text Against Real-world
|
84 |
-
Applications.''
|
85 |
-
"""
|
86 |
-
data_dir = "rt-polaritydata/"
|
87 |
-
pos_samples, neg_samples = None, None
|
88 |
-
for path, f in files:
|
89 |
-
if path == data_dir + "rt-polarity.pos":
|
90 |
-
pos_samples = [line.decode("latin-1").strip() for line in f]
|
91 |
-
elif path == data_dir + "rt-polarity.neg":
|
92 |
-
neg_samples = [line.decode("latin-1").strip() for line in f]
|
93 |
-
if pos_samples is not None and neg_samples is not None:
|
94 |
-
break
|
95 |
-
|
96 |
-
# 80/10/10 split
|
97 |
-
i1 = int(len(pos_samples) * 0.8 + 0.5)
|
98 |
-
i2 = int(len(pos_samples) * 0.9 + 0.5)
|
99 |
-
train_samples = pos_samples[:i1] + neg_samples[:i1]
|
100 |
-
train_labels = (["pos"] * i1) + (["neg"] * i1)
|
101 |
-
validation_samples = pos_samples[i1:i2] + neg_samples[i1:i2]
|
102 |
-
validation_labels = (["pos"] * (i2 - i1)) + (["neg"] * (i2 - i1))
|
103 |
-
test_samples = pos_samples[i2:] + neg_samples[i2:]
|
104 |
-
test_labels = (["pos"] * (len(pos_samples) - i2)) + (["neg"] * (len(pos_samples) - i2))
|
105 |
-
|
106 |
-
if split_key == "train":
|
107 |
-
return (train_samples, train_labels)
|
108 |
-
if split_key == "validation":
|
109 |
-
return (validation_samples, validation_labels)
|
110 |
-
if split_key == "test":
|
111 |
-
return (test_samples, test_labels)
|
112 |
-
else:
|
113 |
-
raise ValueError(f"Invalid split key {split_key}")
|
114 |
-
|
115 |
-
def _generate_examples(self, split_key, files):
|
116 |
-
"""Yields examples for a given split of MR."""
|
117 |
-
split_text, split_labels = self._get_examples_from_split(split_key, files)
|
118 |
-
for text, label in zip(split_text, split_labels):
|
119 |
-
data_key = split_key + "_" + text
|
120 |
-
feature_dict = {"text": text, "label": label}
|
121 |
-
yield data_key, feature_dict
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|