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This dataset is a quick-and-dirty benchmark for predicting ratings across
different domains and on different rating scales based on text. It pulls in a
bunch of rating datasets, takes at most 1000 instances from each and combines
them into a big dataset.

Files changed (5) hide show
  1. .gitignore +1 -0
  2. README.md +8 -3
  3. cross_domain_review_benchmark.py +228 -0
  4. poetry.lock +1058 -0
  5. pyproject.toml +17 -0
.gitignore ADDED
@@ -0,0 +1 @@
 
 
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+ /__pycache__
README.md CHANGED
@@ -1,3 +1,8 @@
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- ---
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- license: unknown
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- ---
 
 
 
 
 
 
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+ ----
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+ -license: unknown
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+ ----
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+
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+ This dataset is a quick-and-dirty benchmark for predicting ratings across
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+ different domains and on different rating scales based on text. It pulls in a
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+ bunch of rating datasets, takes at most 1000 instances from each and combines
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+ them into a big dataset.
cross_domain_review_benchmark.py ADDED
@@ -0,0 +1,228 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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");
4
+ # you may not use this file except in compliance with the License.
5
+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
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+
15
+ """
16
+ This dataset is a quick-and-dirty benchmark for predicting ratings across
17
+ different domains and on different rating scales based on text. It pulls in a
18
+ bunch of rating datasets, takes at most 1000 instances from each and combines
19
+ them into a big dataset.
20
+ """
21
+
22
+ import datasets
23
+ from dataclasses import dataclass
24
+ from typing import Callable, Any
25
+ import operator
26
+ import shutil
27
+ import os
28
+ import sys
29
+ from os.path import join as pjoin
30
+
31
+ from datasets import concatenate_datasets, load_dataset
32
+ from more_itertools.recipes import take
33
+
34
+
35
+ @dataclass
36
+ class SubDataset:
37
+ source: Any
38
+ nick: str
39
+ out_of: int
40
+ get_review: Callable[[Any], Any]
41
+ get_rating: Callable[[Any], Any]
42
+
43
+
44
+ gi = operator.itemgetter
45
+
46
+
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+ def warn(msg):
48
+ print(file=sys.stderr)
49
+ print(f" ** Warning: {msg} **", file=sys.stderr)
50
+ print(file=sys.stderr)
51
+
52
+
53
+ def round_near(x, eps=0.001):
54
+ x_rnd = int(x + 0.5)
55
+ if abs(x_rnd - x) > eps:
56
+ warn("got {x_rnd} when rounding {x}")
57
+ return x_rnd
58
+
59
+
60
+ @dataclass
61
+ class SplitHFSrc:
62
+ name: str
63
+
64
+ def load(self):
65
+ return load_dataset(self.name, streaming=True)
66
+
67
+
68
+ @dataclass
69
+ class TrainOnlyHFSrc:
70
+ name: str
71
+
72
+ def load(self):
73
+ if hasattr(self, "_cached"):
74
+ return self._cached
75
+ self._cached = load_dataset(self.name)["train"].train_test_split(test_size=0.5, seed=42)
76
+ return self._cached
77
+
78
+
79
+ _kaggle_api = None
80
+
81
+
82
+ def get_kaggle_api():
83
+ global _kaggle_api
84
+ if _kaggle_api is not None:
85
+ return _kaggle_api
86
+ from kaggle.api.kaggle_api_extended import KaggleApi
87
+ _kaggle_api = KaggleApi()
88
+ _kaggle_api.authenticate()
89
+ return _kaggle_api
90
+
91
+
92
+ @dataclass
93
+ class KaggleSrc:
94
+ name: str
95
+ file: str
96
+
97
+ def load(self):
98
+ if hasattr(self, "_cached"):
99
+ return self._cached
100
+ kaggle_api = get_kaggle_api()
101
+ dir_name = self.name.replace("/", "__")
102
+ if os.path.exists(dir_name):
103
+ shutil.rmtree(dir_name)
104
+ os.mkdir(dir_name)
105
+ try:
106
+ file_path = pjoin(dir_name, self.file.rsplit("/", 1)[-1])
107
+ kaggle_api.dataset_download_file(self.name, self.file, path=dir_name)
108
+ dataset = load_dataset("csv", data_files=file_path + ".zip")
109
+ return dataset["train"].train_test_split(test_size=0.5, seed=42)
110
+ finally:
111
+ shutil.rmtree(dir_name)
112
+
113
+
114
+ def int_or_drop(col):
115
+ def inner(row):
116
+ try:
117
+ n = float(row[col])
118
+ except ValueError:
119
+ return None
120
+ return round_near(n)
121
+ return inner
122
+
123
+
124
+ DATASETS = [
125
+ SubDataset(SplitHFSrc("juliensimon/amazon-shoe-reviews"), "amazon-shoes", 5, gi("text"), lambda row: row["labels"] + 1),
126
+ # TODO: Appears to be corrupt
127
+ #SubDataset("florentgbelidji/edmunds-car-ratings", "car-ratings", 40, lambda row: row["Review"].strip(), lambda row: round_near(row["Rating"] * 8) - 7),
128
+ SubDataset(TrainOnlyHFSrc("florentgbelidji/car-reviews"), "car-ratings", 5, gi("Review"), gi("Rating")),
129
+ SubDataset(SplitHFSrc("codyburker/yelp_review_sampled"), "yelp", 5, gi("text"), gi("stars")),
130
+ SubDataset(SplitHFSrc("kkotkar1/course-reviews"), "course-reviews", 5, gi("review"), gi("label")),
131
+ SubDataset(TrainOnlyHFSrc("app_reviews"), "app-reviews", 5, gi("review"), gi("star")),
132
+ SubDataset(TrainOnlyHFSrc("LoganKells/amazon_product_reviews_video_games"), "amazon-games", 5, gi("reviewText"), lambda row: round_near(row["overall"]) + 1),
133
+ SubDataset(KaggleSrc("zynicide/wine-reviews", "winemag-data-130k-v2.csv"), "wine-reviews", 100, gi("description"), gi("points")),
134
+ SubDataset(KaggleSrc("sadmadlad/imdb-user-reviews", "Pulp Fiction/movieReviews.csv"), "imdb-user-reviews", 10, gi("Review"), int_or_drop("User's Rating out of 10")),
135
+ # TODO: Unicode decoding error
136
+ #SubDataset(KaggleSrc("arushchillar/disneyland-reviews", "DisneylandReviews.csv"), "disneyland-reviews", 5, gi("Review_Text"), gi("Rating")),
137
+ ]
138
+
139
+ _DESCRIPTION = __doc__
140
+
141
+ _HOMEPAGE = ""
142
+
143
+ _LICENSE = "Mixed"
144
+
145
+
146
+ class CrossDomainReviewBenchmarkDataset(datasets.GeneratorBasedBuilder):
147
+ _DESCRIPTION
148
+
149
+ VERSION = datasets.Version("1.0.0")
150
+
151
+ def _info(self):
152
+ features = datasets.Features(
153
+ {
154
+ "text": datasets.Value("string"),
155
+ "rating": datasets.Value("uint8"),
156
+ "out_of": datasets.Value("uint8"),
157
+ "dataset": datasets.Value("string")
158
+ }
159
+ )
160
+ return datasets.DatasetInfo(
161
+ # This is the description that will appear on the datasets page.
162
+ description=_DESCRIPTION,
163
+ # This defines the different columns of the dataset and their types
164
+ features=features, # Here we define them above because they are different between the two configurations
165
+ # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
166
+ # specify them. They'll be used if as_supervised=True in builder.as_dataset.
167
+ supervised_keys=("text", "rating"),
168
+ # Homepage of the dataset for documentation
169
+ homepage=_HOMEPAGE,
170
+ # License for the dataset if available
171
+ license=_LICENSE,
172
+ # Citation for the dataset
173
+ citation="",
174
+ )
175
+
176
+ def _split_generators(self, dl_manager):
177
+ return [
178
+ datasets.SplitGenerator(
179
+ name=datasets.Split.TRAIN,
180
+ gen_kwargs={
181
+ "split": "train",
182
+ },
183
+ ),
184
+ datasets.SplitGenerator(
185
+ name=datasets.Split.TEST,
186
+ gen_kwargs={
187
+ "split": "test"
188
+ },
189
+ ),
190
+ ]
191
+
192
+ def _generate_examples(self, split):
193
+ key = 0
194
+ for ds_info in DATASETS:
195
+ subdataset = ds_info.source.load()
196
+ lowest = float("inf")
197
+ highest = float("-inf")
198
+ got = 0
199
+ for row in subdataset[split]:
200
+ review = ds_info.get_review(row)
201
+ if review is None:
202
+ continue
203
+ rating = ds_info.get_rating(row)
204
+ if rating is None:
205
+ continue
206
+ assert 1 <= rating <= ds_info.out_of, f"Expected {rating} (inclusively) between 1 and {ds_info.out_of}"
207
+ lowest = min(lowest, rating)
208
+ highest = max(highest, rating)
209
+ yield key, {
210
+ "text": review,
211
+ "rating": rating,
212
+ "out_of": ds_info.out_of,
213
+ "dataset": ds_info.nick
214
+ }
215
+ key += 1
216
+ got += 1
217
+ if got >= 1000:
218
+ break
219
+ if lowest != 1:
220
+ warn(
221
+ f"Lowest rating in {ds_info.nick} was {lowest}, "
222
+ "would suppose it would be 1"
223
+ )
224
+ if highest != ds_info.out_of:
225
+ warn(
226
+ f"Highest rating in {ds_info.nick} was {highest}, "
227
+ f"would suppose it would be {ds_info.out_of}"
228
+ )
poetry.lock ADDED
@@ -0,0 +1,1058 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ [[package]]
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+ description = "Async http client/server framework (asyncio)"
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+ category = "main"
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+ python-versions = ">=3.6"
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+ [package.dependencies]
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+ attrs = ">=17.3.0"
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+ frozenlist = ">=1.1.1"
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+ multidict = ">=4.5,<7.0"
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+
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+ [package.extras]
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+ speedups = ["Brotli", "aiodns", "cchardet"]
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+
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+ [[package]]
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+ name = "aiosignal"
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+ version = "1.2.0"
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+ description = "aiosignal: a list of registered asynchronous callbacks"
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+ category = "main"
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+ optional = false
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+ python-versions = ">=3.6"
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+
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+ [package.dependencies]
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+ frozenlist = ">=1.1.0"
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+
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+ [[package]]
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+ name = "async-timeout"
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+ version = "4.0.2"
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+ description = "Timeout context manager for asyncio programs"
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+ category = "main"
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+ optional = false
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+ python-versions = ">=3.6"
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+
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+ [[package]]
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+ name = "attrs"
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+ version = "22.1.0"
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+ description = "Classes Without Boilerplate"
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+ category = "main"
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+ optional = false
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+ python-versions = ">=3.5"
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+
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+ [package.extras]
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+ dev = ["cloudpickle", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "mypy (>=0.900,!=0.940)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "sphinx", "sphinx-notfound-page", "zope.interface"]
50
+ docs = ["furo", "sphinx", "sphinx-notfound-page", "zope.interface"]
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+ tests = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "zope.interface"]
52
+ tests_no_zope = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins"]
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+
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+ [[package]]
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+ name = "certifi"
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+ version = "2022.9.24"
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+ description = "Python package for providing Mozilla's CA Bundle."
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+ category = "main"
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+ python-versions = ">=3.6"
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+
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+ [[package]]
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+
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+ [package.extras]
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+
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+ [[package]]
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+ name = "colorama"
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+ version = "0.4.5"
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+ category = "main"
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+ optional = false
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+ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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+
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+ [[package]]
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+ name = "datasets"
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+ version = "2.5.2"
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+ description = "HuggingFace community-driven open-source library of datasets"
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+ category = "main"
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+ optional = false
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+ python-versions = ">=3.7.0"
88
+
89
+ [package.dependencies]
90
+ aiohttp = "*"
91
+ dill = "<0.3.6"
92
+ fsspec = {version = ">=2021.11.1", extras = ["http"]}
93
+ huggingface-hub = ">=0.2.0,<1.0.0"
94
+ multiprocess = "*"
95
+ numpy = ">=1.17"
96
+ packaging = "*"
97
+ pandas = "*"
98
+ pyarrow = ">=6.0.0"
99
+ requests = ">=2.19.0"
100
+ responses = "<0.19"
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+ tqdm = ">=4.62.1"
102
+ xxhash = "*"
103
+
104
+ [package.extras]
105
+ apache-beam = ["apache-beam (>=2.26.0)"]
106
+ audio = ["librosa"]
107
+ benchmarks = ["numpy (==1.18.5)", "tensorflow (==2.3.0)", "torch (==1.7.1)", "transformers (==3.0.2)"]
108
+ dev = ["Pillow (>=6.2.1)", "Werkzeug (>=1.0.1)", "absl-py", "aiobotocore (>=2.0.1)", "apache-beam (>=2.26.0)", "bert-score (>=0.3.6)", "black (>=22.0,<23.0)", "boto3 (>=1.19.8)", "botocore (>=1.22.8)", "bs4", "conllu", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "flake8 (>=3.8.3)", "fsspec[s3]", "h5py", "isort (>=5.0.0)", "jiwer", "langdetect", "librosa", "lxml", "lz4", "mauve-text", "moto[s3,server] (==2.0.4)", "mwparserfromhell", "nltk", "openpyxl", "py7zr", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "rarfile (>=4.0)", "requests-file (>=1.5.1)", "rouge-score", "s3fs (>=2021.11.1)", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "soundfile", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "torchaudio (<0.12.0)", "transformers", "zstandard"]
109
+ docs = ["s3fs"]
110
+ quality = ["black (>=22.0,<23.0)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "pyyaml (>=5.3.1)"]
111
+ s3 = ["boto3", "botocore", "fsspec", "s3fs"]
112
+ tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)"]
113
+ tensorflow_gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
114
+ tests = ["Pillow (>=6.2.1)", "Werkzeug (>=1.0.1)", "absl-py", "aiobotocore (>=2.0.1)", "apache-beam (>=2.26.0)", "bert-score (>=0.3.6)", "boto3 (>=1.19.8)", "botocore (>=1.22.8)", "bs4", "conllu", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "fsspec[s3]", "h5py", "jiwer", "langdetect", "librosa", "lxml", "lz4", "mauve-text", "moto[s3,server] (==2.0.4)", "mwparserfromhell", "nltk", "openpyxl", "py7zr", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "requests-file (>=1.5.1)", "rouge-score", "s3fs (>=2021.11.1)", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "soundfile", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "torchaudio (<0.12.0)", "transformers", "zstandard"]
115
+ torch = ["torch"]
116
+ vision = ["Pillow (>=6.2.1)"]
117
+
118
+ [[package]]
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+ name = "dill"
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+ version = "0.3.5.1"
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+ category = "main"
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+
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+
141
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1036
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1037
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1038
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1039
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1040
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1042
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1043
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1044
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1045
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1046
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1047
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1048
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1049
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1050
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1052
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1054
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1055
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1056
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1058
+ ]
pyproject.toml ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [tool.poetry]
2
+ name = "cross_domain_reviews"
3
+ version = "0.1.0"
4
+ description = "Hugging face dataset combining a bunch of review datasets from different domains"
5
+ authors = ["Frankie Robertson <[email protected]>"]
6
+ license = "Apache-v2"
7
+ readme = "README.md"
8
+
9
+ [tool.poetry.dependencies]
10
+ python = "^3.8"
11
+ datasets = "^2.5.2"
12
+ more-itertools = "^8.14.0"
13
+ kaggle = "^1.5.12"
14
+
15
+ [build-system]
16
+ requires = ["poetry-core"]
17
+ build-backend = "poetry.core.masonry.api"