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Datasets:
wino_bias

Tasks:
Token Classification
Sub-tasks:
coreference-resolution
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
Dataset card Viewer Files Community
4
albertvillanova HF staff commited on
Commit
3f31267
•
1 Parent(s): 4f16479

Convert dataset to Parquet (#4)

Browse files

- Convert dataset to Parquet (7513644f4ae12e1d3ad8eb81d39a849c3f6fdaa0)
- Add type1_anti data files (2c8e91335d2b32e30f5da789ea708473d6539d6c)
- Add type2_pro data files (da99c0d1fd49507b1c5309822637462b46b0aa4b)
- Add type2_anti data files (36d8a15228a5739c68ba74a8fbcbd52a6a288612)
- Delete loading script (a62a994662bbeda43a307e3d69ac0c02abeb50fe)
- Delete legacy dataset_infos.json (69058ae899685439ab33e37324b3589b04cb3e2e)

Files changed (11) hide show
  1. README.md +57 -32
  2. dataset_infos.json +0 -1315
  3. type1_anti/test-00000-of-00001.parquet +3 -0
  4. type1_anti/validation-00000-of-00001.parquet +3 -0
  5. type1_pro/test-00000-of-00001.parquet +3 -0
  6. type1_pro/validation-00000-of-00001.parquet +3 -0
  7. type2_anti/test-00000-of-00001.parquet +3 -0
  8. type2_anti/validation-00000-of-00001.parquet +3 -0
  9. type2_pro/test-00000-of-00001.parquet +3 -0
  10. type2_pro/validation-00000-of-00001.parquet +3 -0
  11. wino_bias.py +0 -372
README.md CHANGED
@@ -20,7 +20,7 @@ task_ids:
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  paperswithcode_id: winobias
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  pretty_name: WinoBias
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  dataset_info:
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- - config_name: wino_bias
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  features:
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  - name: document_id
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  dtype: string
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  '36': '*'
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  sequence: string
 
 
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- - name: train
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- num_examples: 150335
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- download_size: 268725744
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- dataset_size: 173899234
 
 
 
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  - config_name: type1_pro
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  features:
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  - name: document_id
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- dataset_size: 746019
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
691
  ---
692
 
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  # Dataset Card for Wino_Bias dataset
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  paperswithcode_id: winobias
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  pretty_name: WinoBias
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  dataset_info:
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+ - split: validation
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  ---
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718
  # Dataset Card for Wino_Bias dataset
dataset_infos.json DELETED
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wino_bias.py DELETED
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- # coding=utf-8
2
- # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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
- """WinoBias: Winograd-schema dataset for detecting gender bias"""
16
-
17
-
18
- import collections
19
-
20
- import datasets
21
-
22
-
23
- _CITATION = """\
24
- @article{DBLP:journals/corr/abs-1804-06876,
25
- author = {Jieyu Zhao and
26
- Tianlu Wang and
27
- Mark Yatskar and
28
- Vicente Ordonez and
29
- Kai{-}Wei Chang},
30
- title = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
31
- journal = {CoRR},
32
- volume = {abs/1804.06876},
33
- year = {2018},
34
- url = {http://arxiv.org/abs/1804.06876},
35
- archivePrefix = {arXiv},
36
- eprint = {1804.06876},
37
- timestamp = {Mon, 13 Aug 2018 16:47:01 +0200},
38
- biburl = {https://dblp.org/rec/journals/corr/abs-1804-06876.bib},
39
- bibsource = {dblp computer science bibliography, https://dblp.org}
40
- }
41
- """
42
-
43
- _DESCRIPTION = """\
44
- WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
45
- The corpus contains Winograd-schema style sentences with entities corresponding to people
46
- referred by their occupation (e.g. the nurse, the doctor, the carpenter).
47
- """
48
-
49
- _HOMEPAGE = "https://uclanlp.github.io/corefBias/overview"
50
-
51
- _LICENSE = "MIT License (https://github.com/uclanlp/corefBias/blob/master/LICENSE)"
52
-
53
- _URL = "https://raw.githubusercontent.com/uclanlp/corefBias/master/WinoBias/wino/data/conll_format"
54
-
55
-
56
- class WinoBiasConfig(datasets.BuilderConfig):
57
- def __init__(self, **kwargs):
58
- super(WinoBiasConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
59
-
60
-
61
- class WinoBias(datasets.GeneratorBasedBuilder):
62
- """WinoBias: Winograd-schema dataset for detecting gender bias"""
63
-
64
- # This is an example of a dataset with multiple configurations.
65
- # If you don't want/need to define several sub-sets in your dataset,
66
- # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
67
-
68
- # If you need to make complex sub-parts in the datasets with configurable options
69
- # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
70
- # BUILDER_CONFIG_CLASS = MyBuilderConfig
71
-
72
- # You will be able to load one or the other configurations in the following list with
73
- # data = datasets.load_dataset('my_dataset', 'first_domain')
74
- # data = datasets.load_dataset('my_dataset', 'second_domain')
75
- def __init__(self, *args, writer_batch_size=None, **kwargs):
76
- super(WinoBias, self).__init__(*args, **kwargs)
77
- # Batch size used by the ArrowWriter
78
- # It defines the number of samples that are kept in memory before writing them
79
- # and also the length of the arrow chunks
80
- # None means that the ArrowWriter will use its default value
81
- self._writer_batch_size = writer_batch_size or 100
82
-
83
- BUILDER_CONFIGS = [
84
- WinoBiasConfig(
85
- name="type1_pro",
86
- description="winoBias type1_pro_stereotype data in cornll format",
87
- ),
88
- WinoBiasConfig(
89
- name="type1_anti",
90
- description="winoBias type1_anti_stereotype data in cornll format",
91
- ),
92
- WinoBiasConfig(
93
- name="type2_pro",
94
- description="winoBias type2_pro_stereotype data in cornll format",
95
- ),
96
- WinoBiasConfig(
97
- name="type2_anti",
98
- description="winoBias type2_anti_stereotype data in cornll format",
99
- ),
100
- ]
101
-
102
- def _info(self):
103
- return datasets.DatasetInfo(
104
- # This is the description that will appear on the datasets page.
105
- description=_DESCRIPTION,
106
- # This defines the different columns of the dataset and their types
107
- # Info about features for this: http://cemantix.org/data/ontonotes.html
108
- features=datasets.Features(
109
- {
110
- "document_id": datasets.Value("string"),
111
- "part_number": datasets.Value("string"),
112
- "word_number": datasets.Sequence(datasets.Value("int32")),
113
- "tokens": datasets.Sequence(datasets.Value("string")),
114
- "pos_tags": datasets.Sequence(
115
- datasets.features.ClassLabel(
116
- names=[
117
- '"',
118
- "''",
119
- "#",
120
- "$",
121
- "(",
122
- ")",
123
- ",",
124
- ".",
125
- ":",
126
- "``",
127
- "CC",
128
- "CD",
129
- "DT",
130
- "EX",
131
- "FW",
132
- "IN",
133
- "JJ",
134
- "JJR",
135
- "JJS",
136
- "LS",
137
- "MD",
138
- "NN",
139
- "NNP",
140
- "NNPS",
141
- "NNS",
142
- "NN|SYM",
143
- "PDT",
144
- "POS",
145
- "PRP",
146
- "PRP$",
147
- "RB",
148
- "RBR",
149
- "RBS",
150
- "RP",
151
- "SYM",
152
- "TO",
153
- "UH",
154
- "VB",
155
- "VBD",
156
- "VBG",
157
- "VBN",
158
- "VBP",
159
- "VBZ",
160
- "WDT",
161
- "WP",
162
- "WP$",
163
- "WRB",
164
- "HYPH",
165
- "XX",
166
- "NFP",
167
- "AFX",
168
- "ADD",
169
- "-LRB-",
170
- "-RRB-",
171
- "-",
172
- ]
173
- )
174
- ),
175
- "parse_bit": datasets.Sequence(datasets.Value("string")),
176
- "predicate_lemma": datasets.Sequence(datasets.Value("string")),
177
- "predicate_framenet_id": datasets.Sequence(datasets.Value("string")),
178
- "word_sense": datasets.Sequence(datasets.Value("string")),
179
- "speaker": datasets.Sequence(datasets.Value("string")),
180
- "ner_tags": datasets.Sequence(
181
- datasets.features.ClassLabel(
182
- names=[
183
- "B-PERSON",
184
- "I-PERSON",
185
- "B-NORP",
186
- "I-NORP",
187
- "B-FAC",
188
- "I-FAC",
189
- "B-ORG",
190
- "I-ORG",
191
- "B-GPE",
192
- "I-GPE",
193
- "B-LOC",
194
- "I-LOC",
195
- "B-PRODUCT",
196
- "I-PRODUCT",
197
- "B-EVENT",
198
- "I-EVENT",
199
- "B-WORK_OF_ART",
200
- "I-WORK_OF_ART",
201
- "B-LAW",
202
- "I-LAW",
203
- "B-LANGUAGE",
204
- "I-LANGUAGE",
205
- "B-DATE",
206
- "I-DATE",
207
- "B-TIME",
208
- "I-TIME",
209
- "B-PERCENT",
210
- "I-PERCENT",
211
- "B-MONEY",
212
- "I-MONEY",
213
- "B-QUANTITY",
214
- "I-QUANTITY",
215
- "B-ORDINAL",
216
- "I-ORDINAL",
217
- "B-CARDINAL",
218
- "I-CARDINAL",
219
- "*",
220
- "0",
221
- "-",
222
- ]
223
- )
224
- ),
225
- "verbal_predicates": datasets.Sequence(datasets.Value("string")),
226
- "coreference_clusters": datasets.Sequence(datasets.Value("string")),
227
- }
228
- ),
229
- supervised_keys=None,
230
- # Homepage of the dataset for documentation
231
- homepage=_HOMEPAGE,
232
- # License for the dataset if available
233
- license=_LICENSE,
234
- # Citation for the dataset
235
- citation=_CITATION,
236
- )
237
-
238
- def _split_generators(self, dl_manager):
239
- """Returns SplitGenerators."""
240
-
241
- dev_data_dir = dl_manager.download(_URL + "/dev_" + self.config.name + "_stereotype.v4_auto_conll")
242
- test_data_dir = dl_manager.download(_URL + "/test_" + self.config.name + "_stereotype.v4_auto_conll")
243
- return [
244
- datasets.SplitGenerator(
245
- name=datasets.Split.VALIDATION,
246
- # These kwargs will be passed to _generate_examples
247
- gen_kwargs={"filepath": dev_data_dir},
248
- ),
249
- datasets.SplitGenerator(
250
- name=datasets.Split.TEST,
251
- # These kwargs will be passed to _generate_examples
252
- gen_kwargs={"filepath": test_data_dir},
253
- ),
254
- ]
255
-
256
- def _generate_examples(self, filepath):
257
- """Yields examples."""
258
- with open(filepath, encoding="utf-8") as f:
259
- id_ = 0
260
- document_id = None
261
- part_number = 0
262
- word_num = []
263
- tokens = []
264
- pos_tags = []
265
- parse_bit = []
266
- predicate_lemma = []
267
- predicate_framenet_id = []
268
- word_sense = []
269
- speaker = []
270
- ner_tags = []
271
- ner_start = False
272
- verbal_predicates = []
273
- coreference = []
274
- clusters = collections.defaultdict(list)
275
- coref_stacks = collections.defaultdict(list)
276
- for line in f:
277
- if line.startswith("#begin") or line.startswith("#end"):
278
- continue
279
- elif not line.strip():
280
- id_ += 1
281
- yield str(id_), {
282
- "document_id": document_id,
283
- "part_number": part_number,
284
- "word_number": word_num,
285
- "tokens": tokens,
286
- "pos_tags": pos_tags,
287
- "parse_bit": parse_bit,
288
- "predicate_lemma": predicate_lemma,
289
- "predicate_framenet_id": predicate_framenet_id,
290
- "word_sense": word_sense,
291
- "speaker": speaker,
292
- "ner_tags": ner_tags,
293
- "verbal_predicates": verbal_predicates,
294
- "coreference_clusters": sum(
295
- clusters[1], []
296
- ), # flatten the list as writing the exmaples needs an array.
297
- }
298
-
299
- word_num = []
300
- tokens = []
301
- pos_tags = []
302
- parse_bit = []
303
- predicate_lemma = []
304
- predicate_framenet_id = []
305
- word_sense = []
306
- speaker = []
307
- ner_tags = []
308
- verbal_predicates = []
309
- coreference = []
310
- clusters = collections.defaultdict(list)
311
- coref_stacks = collections.defaultdict(list)
312
- else:
313
- splits = [s for s in line.split() if s]
314
- if len(splits) > 7:
315
- document_id = splits[0]
316
- part_number = splits[1]
317
- word_num.append(splits[2])
318
- tokens.append(splits[3])
319
- pos_tags.append(splits[4])
320
- parse_bit.append(splits[5])
321
- predicate_lemma.append(splits[6])
322
- predicate_framenet_id.append(splits[7])
323
- word_sense.append(splits[8])
324
- speaker.append(splits[9])
325
- ner_word = splits[10]
326
- coreference = splits[-1]
327
- if ")" in ner_word and ner_start:
328
- ner_start = False
329
- ner_word = "0"
330
- if "(" in ner_word:
331
- ner_start = True
332
- ner_word = ner_word.strip(" ").replace("(", "B-").replace("*", "").replace(")", "")
333
- start_word = ner_word.strip(" ").replace("B-", "")
334
- if ner_start:
335
- if ner_word.strip(" ") == "*":
336
- ner_word = "I-" + start_word
337
- ner_tags.append(ner_word)
338
- word_is_verbal_predicate = any(["(V" in x for x in splits[11:-1]])
339
- if word_is_verbal_predicate:
340
- verbal_predicates.append(splits[3])
341
- if coreference != "-":
342
- for segment in coreference.split("|"):
343
- if segment[0] == "(":
344
- if segment[-1] == ")":
345
- cluster_id = int(segment[1:-1])
346
- clusters[cluster_id].append([splits[2], splits[2]])
347
- else:
348
- cluster_id = int(segment[1:])
349
- coref_stacks[cluster_id].append(splits[2])
350
- else:
351
- cluster_id = int(segment[:-1])
352
- start = coref_stacks[cluster_id].pop()
353
- clusters[cluster_id].append([start, splits[2]])
354
-
355
- if tokens:
356
- # add the last one
357
- id_ += 1
358
- yield str(id_), {
359
- "document_id": document_id,
360
- "part_number": part_number,
361
- "word_number": word_num,
362
- "tokens": tokens,
363
- "pos_tags": pos_tags,
364
- "parse_bit": parse_bit,
365
- "predicate_lemma": predicate_lemma,
366
- "predicate_framenet_id": predicate_framenet_id,
367
- "word_sense": word_sense,
368
- "speaker": speaker,
369
- "ner_tags": ner_tags,
370
- "verbal_predicates": verbal_predicates,
371
- "coreference_clusters": sum(clusters[1], []),
372
- }