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Update files from the datasets library (from 1.0.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

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+ # coding=utf-8
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+ # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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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,
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
+ """Reuters 21578"""
18
+
19
+ from __future__ import absolute_import, division, print_function
20
+
21
+ import os
22
+ from textwrap import dedent
23
+
24
+ import datasets
25
+
26
+
27
+ _CITATION = """\
28
+ @article{APTE94,
29
+ author = {Chidanand Apt{\'{e}} and Fred Damerau and Sholom M. Weiss},
30
+ title = {Automated Learning of Decision Rules for Text Categorization},
31
+ journal = {ACM Transactions on Information Systems},
32
+ year = {1994},
33
+ note = {To appear.}
34
+ }
35
+
36
+ @inproceedings{APTE94b,
37
+ author = {Chidanand Apt{\'{e}} and Fred Damerau and Sholom M. Weiss},
38
+ title = {Toward Language Independent Automated Learning of Text Categorization Models},
39
+ booktitle = {sigir94},
40
+ year = {1994},
41
+ note = {To appear.}
42
+ }
43
+
44
+ @inproceedings{HAYES8},
45
+ author = {Philip J. Hayes and Peggy M. Anderson and Irene B. Nirenburg and
46
+ Linda M. Schmandt},
47
+ title = {{TCS}: A Shell for Content-Based Text Categorization},
48
+ booktitle = {IEEE Conference on Artificial Intelligence Applications},
49
+ year = {1990}
50
+ }
51
+
52
+ @inproceedings{HAYES90b,
53
+ author = {Philip J. Hayes and Steven P. Weinstein},
54
+ title = {{CONSTRUE/TIS:} A System for Content-Based Indexing of a
55
+ Database of News Stories},
56
+ booktitle = {Second Annual Conference on Innovative Applications of
57
+ Artificial Intelligence},
58
+ year = {1990}
59
+ }
60
+
61
+ @incollection{HAYES92 ,
62
+ author = {Philip J. Hayes},
63
+ title = {Intelligent High-Volume Text Processing using Shallow,
64
+ Domain-Specific Techniques},
65
+ booktitle = {Text-Based Intelligent Systems},
66
+ publisher = {Lawrence Erlbaum},
67
+ address = {Hillsdale, NJ},
68
+ year = {1992},
69
+ editor = {Paul S. Jacobs}
70
+ }
71
+
72
+ @inproceedings{LEWIS91c ,
73
+ author = {David D. Lewis},
74
+ title = {Evaluating Text Categorization},
75
+ booktitle = {Proceedings of Speech and Natural Language Workshop},
76
+ year = {1991},
77
+ month = {feb},
78
+ organization = {Defense Advanced Research Projects Agency},
79
+ publisher = {Morgan Kaufmann},
80
+ pages = {312--318}
81
+
82
+ }
83
+
84
+ @phdthesis{LEWIS91d,
85
+ author = {David Dolan Lewis},
86
+ title = {Representation and Learning in Information Retrieval},
87
+ school = {Computer Science Dept.; Univ. of Massachusetts; Amherst, MA 01003},
88
+ year = 1992},
89
+ note = {Technical Report 91--93.}
90
+ }
91
+
92
+ @inproceedings{LEWIS91e,
93
+ author = {David D. Lewis},
94
+ title = {Data Extraction as Text Categorization: An Experiment with
95
+ the {MUC-3} Corpus},
96
+ booktitle = {Proceedings of the Third Message Understanding Evaluation
97
+ and Conference},
98
+ year = {1991},
99
+ month = {may},
100
+ organization = {Defense Advanced Research Projects Agency},
101
+ publisher = {Morgan Kaufmann},
102
+ address = {Los Altos, CA}
103
+
104
+ }
105
+
106
+ @inproceedings{LEWIS92b,
107
+ author = {David D. Lewis},
108
+ title = {An Evaluation of Phrasal and Clustered Representations on a Text
109
+ Categorization Task},
110
+ booktitle = {Fifteenth Annual International ACM SIGIR Conference on
111
+ Research and Development in Information Retrieval},
112
+ year = {1992},
113
+ pages = {37--50}
114
+ }
115
+
116
+ @inproceedings{LEWIS92d ,
117
+ author = {David D. Lewis and Richard M. Tong},
118
+ title = {Text Filtering in {MUC-3} and {MUC-4}},
119
+ booktitle = {Proceedings of the Fourth Message Understanding Conference ({MUC-4})},
120
+ year = {1992},
121
+ month = {jun},
122
+ organization = {Defense Advanced Research Projects Agency},
123
+ publisher = {Morgan Kaufmann},
124
+ address = {Los Altos, CA}
125
+ }
126
+
127
+ @inproceedings{LEWIS92e,
128
+ author = {David D. Lewis},
129
+ title = {Feature Selection and Feature Extraction for Text Categorization},
130
+ booktitle = {Proceedings of Speech and Natural Language Workshop},
131
+ year = {1992},
132
+ month = {feb} ,
133
+ organization = {Defense Advanced Research Projects Agency},
134
+ publisher = {Morgan Kaufmann},
135
+ pages = {212--217}
136
+ }
137
+
138
+ @inproceedings{LEWIS94b,
139
+ author = {David D. Lewis and Marc Ringuette},
140
+ title = {A Comparison of Two Learning Algorithms for Text Categorization},
141
+ booktitle = {Symposium on Document Analysis and Information Retrieval},
142
+ year = {1994},
143
+ organization = {ISRI; Univ. of Nevada, Las Vegas},
144
+ address = {Las Vegas, NV},
145
+ month = {apr},
146
+ pages = {81--93}
147
+ }
148
+
149
+ @article{LEWIS94d,
150
+ author = {David D. Lewis and Philip J. Hayes},
151
+ title = {Guest Editorial},
152
+ journal = {ACM Transactions on Information Systems},
153
+ year = {1994},
154
+ volume = {12},
155
+ number = {3},
156
+ pages = {231},
157
+ month = {jul}
158
+ }
159
+
160
+ @article{SPARCKJONES76,
161
+ author = {K. {Sparck Jones} and C. J. {van Rijsbergen}},
162
+ title = {Information Retrieval Test Collections},
163
+ journal = {Journal of Documentation},
164
+ year = {1976},
165
+ volume = {32},
166
+ number = {1},
167
+ pages = {59--75}
168
+ }
169
+
170
+ @book{WEISS91,
171
+ author = {Sholom M. Weiss and Casimir A. Kulikowski},
172
+ title = {Computer Systems That Learn},
173
+ publisher = {Morgan Kaufmann},
174
+ year = {1991},
175
+ address = {San Mateo, CA}
176
+ }
177
+ """
178
+
179
+ _DESCRIPTION = """\
180
+ The Reuters-21578 dataset is one of the most widely used data collections for text
181
+ categorization research. It is collected from the Reuters financial newswire service in 1987.
182
+ """
183
+
184
+ _DATA_URL = "https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz"
185
+
186
+
187
+ class Reuters21578Config(datasets.BuilderConfig):
188
+ """BuilderConfig for reuters-21578."""
189
+
190
+ def __init__(self, **kwargs):
191
+ """BuilderConfig for Reuters21578.
192
+
193
+ Args:
194
+ **kwargs: keyword arguments forwarded to super.
195
+ """
196
+ super(Reuters21578Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
197
+
198
+
199
+ class Reuters21578(datasets.GeneratorBasedBuilder):
200
+ """Reuters 21578"""
201
+
202
+ BUILDER_CONFIGS = [
203
+ Reuters21578Config(
204
+ name="ModHayes",
205
+ description=dedent(
206
+ """Training Set (20856 docs): CGISPLIT="TRAINING-SET"
207
+ Test Set (722 docs): CGISPLIT="PUBLISHED-TESTSET"
208
+ Unused (0 docs)"""
209
+ ),
210
+ ),
211
+ Reuters21578Config(
212
+ name="ModLewis",
213
+ description=dedent(
214
+ """Training Set (13,625 docs): LEWISSPLIT="TRAIN"; TOPICS="YES" or "NO"
215
+ Test Set (6,188 docs): LEWISSPLIT="TEST"; TOPICS="YES" or "NO"
216
+ Unused (1,765): LEWISSPLIT="NOT-USED" or TOPICS="BYPASS"""
217
+ ),
218
+ ),
219
+ Reuters21578Config(
220
+ name="ModApte",
221
+ description=dedent(
222
+ """Training Set (9,603 docs): LEWISSPLIT="TRAIN"; TOPICS="YES"
223
+ Test Set (3,299 docs): LEWISSPLIT="TEST"; TOPICS="YES"
224
+ Unused (8,676 docs): LEWISSPLIT="NOT-USED"; TOPICS="YES" or TOPICS="NO" or TOPICS="BYPASS" """
225
+ ),
226
+ ),
227
+ ]
228
+
229
+ def _info(self):
230
+ return datasets.DatasetInfo(
231
+ description=_DESCRIPTION,
232
+ features=datasets.Features(
233
+ {
234
+ "text": datasets.Value("string"),
235
+ "topics": datasets.Sequence(datasets.Value("string")),
236
+ "lewis_split": datasets.Value("string"),
237
+ "cgis_split": datasets.Value("string"),
238
+ "old_id": datasets.Value("string"),
239
+ "new_id": datasets.Value("string"),
240
+ "places": datasets.Sequence(datasets.Value("string")),
241
+ "people": datasets.Sequence(datasets.Value("string")),
242
+ "orgs": datasets.Sequence(datasets.Value("string")),
243
+ "exchanges": datasets.Sequence(datasets.Value("string")),
244
+ "date": datasets.Value("string"),
245
+ "title": datasets.Value("string"),
246
+ }
247
+ ),
248
+ # No default supervised_keys (as we have to pass both premise
249
+ # and hypothesis as input).
250
+ supervised_keys=None,
251
+ homepage="https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html",
252
+ citation=_CITATION,
253
+ )
254
+
255
+ def _split_generators(self, dl_manager):
256
+ dl_dir = dl_manager.download_and_extract(_DATA_URL)
257
+ files = [os.path.join(dl_dir, "reut2-" + "%03d" % i + ".sgm") for i in range(22)]
258
+ if self.config.name == "ModHayes":
259
+ return [
260
+ datasets.SplitGenerator(
261
+ name=datasets.Split.TEST,
262
+ gen_kwargs={
263
+ "filepath": files,
264
+ "split": "PUBLISHED-TESTSET",
265
+ },
266
+ ),
267
+ datasets.SplitGenerator(
268
+ name=datasets.Split.TRAIN,
269
+ gen_kwargs={
270
+ "filepath": files,
271
+ "split": "TRAINING-SET",
272
+ },
273
+ ),
274
+ ]
275
+ else:
276
+ return [
277
+ datasets.SplitGenerator(
278
+ name=datasets.Split.TEST,
279
+ gen_kwargs={
280
+ "filepath": files,
281
+ "split": "TEST",
282
+ },
283
+ ),
284
+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": files, "split": "TRAIN"}),
285
+ datasets.SplitGenerator(name="unused", gen_kwargs={"filepath": files, "split": "NOT-USED"}),
286
+ ]
287
+
288
+ def _generate_examples(self, filepath, split):
289
+ """This function returns the examples in the raw (text) form."""
290
+ for file in filepath:
291
+ with open(
292
+ file, encoding="utf-8", errors="ignore"
293
+ ) as f: # only the file reut2-017 has one line non UTF-8 encoded so we can ignore it
294
+ line = f.readline()
295
+ lewis_split = ""
296
+ cgis_split = ""
297
+ old_id = ""
298
+ new_id = ""
299
+ topics = []
300
+ places = []
301
+ people = []
302
+ orgs = []
303
+ exchanges = []
304
+ date = ""
305
+ title = ""
306
+ while line:
307
+ if line.startswith("<REUTERS"):
308
+ line = line.split()
309
+ lewis_split = line[2].split("=")[1]
310
+ cgis_split = line[3].split("=")[1]
311
+ old_id = line[4].split("=")[1]
312
+ new_id = line[5].split("=")[1][:-1]
313
+ has_topic = line[1].split("=")[1]
314
+ line = f.readline()
315
+ if (
316
+ (self.config.name == "ModHayes" and split not in cgis_split)
317
+ or (
318
+ self.config.name == "ModLewis"
319
+ and (
320
+ (split not in lewis_split)
321
+ or (split == "TRAIN" and has_topic not in ['"YES"', '"NO"'])
322
+ or (split == "TEST" and has_topic not in ['"YES"', '"NO"'])
323
+ or (split == "NOT-USED" and has_topic not in ['"YES"', '"NO"', '"BYPASS"'])
324
+ )
325
+ )
326
+ or (
327
+ self.config.name == "ModApte"
328
+ and (
329
+ split not in lewis_split
330
+ or (split == "TRAIN" and has_topic != '"YES"')
331
+ or (split == "TEST" and has_topic != '"YES"')
332
+ or (split == "NOT-USED" and has_topic not in ['"YES"', '"NO"', '"BYPASS"'])
333
+ )
334
+ )
335
+ ): # skip example that are not in the current split
336
+ li = line
337
+ while li and not li.startswith("<REUTERS"):
338
+ li = f.readline()
339
+ if li:
340
+ line = li
341
+ elif line.startswith("<TOPICS>"):
342
+ if line.replace("\n", "") != "<TOPICS></TOPICS>":
343
+ line = line.split("<D>")
344
+ topics = [topic.replace("</D>", "") for topic in line[1:-1]]
345
+ topics = [topic.replace("</TOPICS>", "") for topic in topics]
346
+ line = f.readline()
347
+ elif line.startswith("<PLACES>"):
348
+ if line.replace("\n", "") != "<PLACES></PLACES>":
349
+ line = line.split("<D>")
350
+ places = [place.replace("</D>", "") for place in line[1:-1]]
351
+ places = [place.replace("</PLACES>", "") for place in places]
352
+ line = f.readline()
353
+ elif line.startswith("<PEOPLE>"):
354
+ if line.replace("\n", "") != "<PEOPLE></PEOPLE>":
355
+ line = line.split("<D>")
356
+ people = [p.replace("</D>", "") for p in line[1:-1]]
357
+ people = [p.replace("</PEOPLE>", "") for p in people]
358
+ line = f.readline()
359
+ elif line.startswith("<ORGS>"):
360
+ if line.replace("\n", "") != "<ORGS></ORGS>":
361
+ line = line.split("<D>")
362
+ orgs = [org.replace("</D>", "") for org in line[1:-1]]
363
+ orgs = [org.replace("</ORGS>", "") for org in orgs]
364
+ line = f.readline()
365
+ elif line.startswith("<EXCHANGES>"):
366
+ if line.replace("\n", "") != "<EXCHANGES></EXCHANGES>":
367
+ line = line.split("<D>")
368
+ exchanges = [ex.replace("</D>", "") for ex in line[1:-1]]
369
+ exchanges = [ex.replace("</EXCHANGES>", "") for ex in exchanges]
370
+ line = f.readline()
371
+ elif line.startswith("<DATE>"):
372
+ date = line.replace("\n", "")
373
+ date = line[6:-8]
374
+ line = f.readline()
375
+ elif line.startswith("<TITLE>"):
376
+ title = line[7:-9]
377
+ line = f.readline()
378
+ elif "<BODY>" in line:
379
+ text = line.split("<BODY>")[1]
380
+ line = f.readline()
381
+ while "</BODY>" not in line:
382
+ text += line
383
+ line = f.readline()
384
+
385
+ yield new_id, {
386
+ "lewis_split": lewis_split,
387
+ "cgis_split": cgis_split,
388
+ "old_id": old_id,
389
+ "new_id": new_id,
390
+ "topics": topics,
391
+ "places": places,
392
+ "people": people,
393
+ "orgs": orgs,
394
+ "exchanges": exchanges,
395
+ "date": date,
396
+ "title": title,
397
+ "text": text,
398
+ }
399
+ line = f.readline()
400
+
401
+ else:
402
+ line = f.readline()