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Update files from the datasets library (from 1.0.2)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.2

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  1. .gitattributes +27 -0
  2. dataset_infos.json +1 -0
  3. dummy/ar/1.0.0/dummy_data.zip +3 -0
  4. dummy/bg/1.0.0/dummy_data.zip +3 -0
  5. dummy/ca/1.0.0/dummy_data.zip +3 -0
  6. dummy/combined/1.0.0/dummy_data.zip +3 -0
  7. dummy/cs/1.0.0/dummy_data.zip +3 -0
  8. dummy/da/1.0.0/dummy_data.zip +3 -0
  9. dummy/de/1.0.0/dummy_data.zip +3 -0
  10. dummy/el/1.0.0/dummy_data.zip +3 -0
  11. dummy/en/1.0.0/dummy_data.zip +3 -0
  12. dummy/es/1.0.0/dummy_data.zip +3 -0
  13. dummy/et/1.0.0/dummy_data.zip +3 -0
  14. dummy/fa/1.0.0/dummy_data.zip +3 -0
  15. dummy/fi/1.0.0/dummy_data.zip +3 -0
  16. dummy/fr/1.0.0/dummy_data.zip +3 -0
  17. dummy/he/1.0.0/dummy_data.zip +3 -0
  18. dummy/hi/1.0.0/dummy_data.zip +3 -0
  19. dummy/hr/1.0.0/dummy_data.zip +3 -0
  20. dummy/hu/1.0.0/dummy_data.zip +3 -0
  21. dummy/id/1.0.0/dummy_data.zip +3 -0
  22. dummy/it/1.0.0/dummy_data.zip +3 -0
  23. dummy/ja/1.0.0/dummy_data.zip +3 -0
  24. dummy/ko/1.0.0/dummy_data.zip +3 -0
  25. dummy/lt/1.0.0/dummy_data.zip +3 -0
  26. dummy/lv/1.0.0/dummy_data.zip +3 -0
  27. dummy/ms/1.0.0/dummy_data.zip +3 -0
  28. dummy/nl/1.0.0/dummy_data.zip +3 -0
  29. dummy/no/1.0.0/dummy_data.zip +3 -0
  30. dummy/pl/1.0.0/dummy_data.zip +3 -0
  31. dummy/pt/1.0.0/dummy_data.zip +3 -0
  32. dummy/ro/1.0.0/dummy_data.zip +3 -0
  33. dummy/ru/1.0.0/dummy_data.zip +3 -0
  34. dummy/sk/1.0.0/dummy_data.zip +3 -0
  35. dummy/sl/1.0.0/dummy_data.zip +3 -0
  36. dummy/sr/1.0.0/dummy_data.zip +3 -0
  37. dummy/sv/1.0.0/dummy_data.zip +3 -0
  38. dummy/th/1.0.0/dummy_data.zip +3 -0
  39. dummy/tl/1.0.0/dummy_data.zip +3 -0
  40. dummy/tr/1.0.0/dummy_data.zip +3 -0
  41. dummy/uk/1.0.0/dummy_data.zip +3 -0
  42. dummy/vi/1.0.0/dummy_data.zip +3 -0
  43. dummy/zh/1.0.0/dummy_data.zip +3 -0
  44. polyglot_ner.py +188 -0
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dataset_infos.json ADDED
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polyglot_ner.py ADDED
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1
+ # coding=utf-8
2
+ # Copyright 2020 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
+ """The Polyglot-NER Dataset."""
18
+
19
+ from __future__ import absolute_import, division, print_function
20
+
21
+ import os
22
+
23
+ import datasets
24
+
25
+
26
+ _CITATION = """\
27
+ @article{polyglotner,
28
+ author = {Al-Rfou, Rami and Kulkarni, Vivek and Perozzi, Bryan and Skiena, Steven},
29
+ title = {{Polyglot-NER}: Massive Multilingual Named Entity Recognition},
30
+ journal = {{Proceedings of the 2015 {SIAM} International Conference on Data Mining, Vancouver, British Columbia, Canada, April 30- May 2, 2015}},
31
+ month = {April},
32
+ year = {2015},
33
+ publisher = {SIAM},
34
+ }
35
+ """
36
+
37
+ _LANGUAGES = [
38
+ "ca",
39
+ "de",
40
+ "es",
41
+ "fi",
42
+ "hi",
43
+ "id",
44
+ "ko",
45
+ "ms",
46
+ "pl",
47
+ "ru",
48
+ "sr",
49
+ "tl",
50
+ "vi",
51
+ "ar",
52
+ "cs",
53
+ "el",
54
+ "et",
55
+ "fr",
56
+ "hr",
57
+ "it",
58
+ "lt",
59
+ "nl",
60
+ "pt",
61
+ "sk",
62
+ "sv",
63
+ "tr",
64
+ "zh",
65
+ "bg",
66
+ "da",
67
+ "en",
68
+ "fa",
69
+ "he",
70
+ "hu",
71
+ "ja",
72
+ "lv",
73
+ "no",
74
+ "ro",
75
+ "sl",
76
+ "th",
77
+ "uk",
78
+ ]
79
+
80
+ _LANG_FILEPATHS = {
81
+ lang: os.path.join(
82
+ "acl_datasets",
83
+ lang,
84
+ "data" if lang != "zh" else "", # they're all lang/data/lang_wiki.conll except "zh"
85
+ f"{lang}_wiki.conll",
86
+ )
87
+ for lang in _LANGUAGES
88
+ }
89
+
90
+ _DESCRIPTION = """\
91
+ Polyglot-NER
92
+ A training dataset automatically generated from Wikipedia and Freebase the task
93
+ of named entity recognition. The dataset contains the basic Wikipedia based
94
+ training data for 40 languages we have (with coreference resolution) for the task of
95
+ named entity recognition. The details of the procedure of generating them is outlined in
96
+ Section 3 of the paper (https://arxiv.org/abs/1410.3791). Each config contains the data
97
+ corresponding to a different language. For example, "es" includes only spanish examples.
98
+ """
99
+
100
+ _DATA_URL = "http://cs.stonybrook.edu/~polyglot/ner2/emnlp_datasets.tgz"
101
+ _HOMEPAGE_URL = "https://sites.google.com/site/rmyeid/projects/polylgot-ner"
102
+ _VERSION = "1.0.0"
103
+
104
+
105
+ class PolyglotNERConfig(datasets.BuilderConfig):
106
+ def __init__(self, *args, languages=None, **kwargs):
107
+ super().__init__(*args, version=datasets.Version(_VERSION, ""), **kwargs)
108
+ self.languages = languages
109
+
110
+ @property
111
+ def filepaths(self):
112
+ return [_LANG_FILEPATHS[lang] for lang in self.languages]
113
+
114
+
115
+ class PolyglotNER(datasets.GeneratorBasedBuilder):
116
+ """The Polyglot-NER Dataset"""
117
+
118
+ BUILDER_CONFIGS = [
119
+ PolyglotNERConfig(name=lang, languages=[lang], description=f"Polyglot-NER examples in {lang}.")
120
+ for lang in _LANGUAGES
121
+ ] + [
122
+ PolyglotNERConfig(
123
+ name="combined", languages=_LANGUAGES, description=f"Complete Polyglot-NER dataset with all languages."
124
+ )
125
+ ]
126
+
127
+ def _info(self):
128
+ return datasets.DatasetInfo(
129
+ description=_DESCRIPTION,
130
+ features=datasets.Features(
131
+ {
132
+ "id": datasets.Value("string"),
133
+ "lang": datasets.Value("string"),
134
+ "words": datasets.Sequence(datasets.Value("string")),
135
+ "ner": datasets.Sequence(datasets.Value("string")),
136
+ }
137
+ ),
138
+ supervised_keys=None,
139
+ homepage=_HOMEPAGE_URL,
140
+ citation=_CITATION,
141
+ )
142
+
143
+ def _split_generators(self, dl_manager):
144
+ """Returns SplitGenerators."""
145
+ path = dl_manager.download_and_extract(_DATA_URL)
146
+
147
+ return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"datapath": path})]
148
+
149
+ def _generate_examples(self, datapath):
150
+ sentence_counter = 0
151
+ for filepath, lang in zip(self.config.filepaths, self.config.languages):
152
+ filepath = os.path.join(datapath, filepath)
153
+ with open(filepath, encoding="utf-8") as f:
154
+ current_words = []
155
+ current_ner = []
156
+ for row in f:
157
+ row = row.rstrip()
158
+ if row:
159
+ token, label = row.split("\t")
160
+ current_words.append(token)
161
+ current_ner.append(label)
162
+ else:
163
+ # New sentence
164
+ if not current_words:
165
+ # Consecutive empty lines will cause empty sentences
166
+ continue
167
+ assert len(current_words) == len(current_ner), "💔 between len of words & ner"
168
+ sentence = (
169
+ sentence_counter,
170
+ {
171
+ "id": str(sentence_counter),
172
+ "lang": lang,
173
+ "words": current_words,
174
+ "ner": current_ner,
175
+ },
176
+ )
177
+ sentence_counter += 1
178
+ current_words = []
179
+ current_ner = []
180
+ yield sentence
181
+ # Don't forget last sentence in dataset 🧐
182
+ if current_words:
183
+ yield sentence_counter, {
184
+ "id": str(sentence_counter),
185
+ "lang": lang,
186
+ "words": current_words,
187
+ "ner": current_ner,
188
+ }