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Update files from the datasets library (from 1.2.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bin.* filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.xz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zstandard filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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1
+ ---
2
+ annotations_creators:
3
+ - machine-generated
4
+ language_creators:
5
+ - expert-generated
6
+ languages:
7
+ - tr
8
+ licenses:
9
+ - cc-by-4-0
10
+ multilinguality:
11
+ - monolingual
12
+ size_categories:
13
+ - 100K<n<1M
14
+ source_datasets:
15
+ - extended|other-turkish_ner
16
+ task_categories:
17
+ - structure-prediction
18
+ task_ids:
19
+ - named-entity-recognition
20
+ ---
21
+
22
+ # Dataset Card for turkish_shrinked_ner
23
+
24
+ ## Table of Contents
25
+ - [Dataset Description](#dataset-description)
26
+ - [Dataset Summary](#dataset-summary)
27
+ - [Supported Tasks](#supported-tasks-and-leaderboards)
28
+ - [Languages](#languages)
29
+ - [Dataset Structure](#dataset-structure)
30
+ - [Data Instances](#data-instances)
31
+ - [Data Fields](#data-instances)
32
+ - [Data Splits](#data-instances)
33
+ - [Dataset Creation](#dataset-creation)
34
+ - [Curation Rationale](#curation-rationale)
35
+ - [Source Data](#source-data)
36
+ - [Annotations](#annotations)
37
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
38
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
39
+ - [Social Impact of Dataset](#social-impact-of-dataset)
40
+ - [Discussion of Biases](#discussion-of-biases)
41
+ - [Other Known Limitations](#other-known-limitations)
42
+ - [Additional Information](#additional-information)
43
+ - [Dataset Curators](#dataset-curators)
44
+ - [Licensing Information](#licensing-information)
45
+ - [Citation Information](#citation-information)
46
+
47
+ ## Dataset Description
48
+
49
+ - **Homepage:** https://www.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar
50
+ - **Repository:** [Needs More Information]
51
+ - **Paper:** [Needs More Information]
52
+ - **Leaderboard:** [Needs More Information]
53
+ - **Point of Contact:** https://www.kaggle.com/behcetsenturk
54
+
55
+ ### Dataset Summary
56
+
57
+ Shrinked processed version (48 entity type) of the turkish_ner.
58
+
59
+ Original turkish_ner dataset: Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.
60
+
61
+ Shrinked entity types are: academic, academic_person, aircraft, album_person, anatomy, animal, architect_person, capital, chemical, clothes, country, culture, currency, date, food, genre, government, government_person, language, location, material, measure, medical, military, military_person, nation, newspaper, organization, organization_person, person, production_art_music, production_art_music_person, quantity, religion, science, shape, ship, software, space, space_person, sport, sport_name, sport_person, structure, subject, tech, train, vehicle
62
+
63
+ ### Supported Tasks and Leaderboards
64
+
65
+ [Needs More Information]
66
+
67
+ ### Languages
68
+
69
+ Turkish
70
+
71
+ ## Dataset Structure
72
+
73
+ ### Data Instances
74
+
75
+ [Needs More Information]
76
+
77
+ ### Data Fields
78
+
79
+ [Needs More Information]
80
+
81
+ ### Data Splits
82
+
83
+ There's only the training set.
84
+
85
+ ## Dataset Creation
86
+
87
+ ### Curation Rationale
88
+
89
+ [Needs More Information]
90
+
91
+ ### Source Data
92
+
93
+ #### Initial Data Collection and Normalization
94
+
95
+ [Needs More Information]
96
+
97
+ #### Who are the source language producers?
98
+
99
+ [Needs More Information]
100
+
101
+ ### Annotations
102
+
103
+ #### Annotation process
104
+
105
+ [Needs More Information]
106
+
107
+ #### Who are the annotators?
108
+
109
+ [Needs More Information]
110
+
111
+ ### Personal and Sensitive Information
112
+
113
+ [Needs More Information]
114
+
115
+ ## Considerations for Using the Data
116
+
117
+ ### Social Impact of Dataset
118
+
119
+ [Needs More Information]
120
+
121
+ ### Discussion of Biases
122
+
123
+ [Needs More Information]
124
+
125
+ ### Other Known Limitations
126
+
127
+ [Needs More Information]
128
+
129
+ ## Additional Information
130
+
131
+ ### Dataset Curators
132
+
133
+ Behcet Senturk
134
+
135
+ ### Licensing Information
136
+
137
+ Creative Commons Attribution 4.0 International
138
+
139
+ ### Citation Information
140
+
141
+ [Needs More Information]
dataset_infos.json ADDED
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+ {"default": {"description": "Shrinked version (48 entity type) of the turkish_ner.\n\nOriginal turkish_ner dataset: Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.\n\nShrinked entity types are: academic, academic_person, aircraft, album_person, anatomy, animal, architect_person, capital, chemical, clothes, country, culture, currency, date, food, genre, government, government_person, language, location, material, measure, medical, military, military_person, nation, newspaper, organization, organization_person, person, production_art_music, production_art_music_person, quantity, religion, science, shape, ship, software, space, space_person, sport, sport_name, sport_person, structure, subject, tech, train, vehicle\n", "citation": "", "homepage": "https://www.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar", "license": "Attribution 4.0 International (CC BY 4.0)", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 97, "names": ["O", "B-academic", "I-academic", "B-academic_person", "I-academic_person", "B-aircraft", "I-aircraft", "B-album_person", "I-album_person", "B-anatomy", "I-anatomy", "B-animal", "I-animal", "B-architect_person", "I-architect_person", "B-capital", "I-capital", "B-chemical", "I-chemical", "B-clothes", "I-clothes", "B-country", "I-country", "B-culture", "I-culture", "B-currency", "I-currency", "B-date", "I-date", "B-food", "I-food", "B-genre", "I-genre", "B-government", "I-government", "B-government_person", "I-government_person", "B-language", "I-language", "B-location", "I-location", "B-material", "I-material", "B-measure", "I-measure", "B-medical", "I-medical", "B-military", "I-military", "B-military_person", "I-military_person", "B-nation", "I-nation", "B-newspaper", "I-newspaper", "B-organization", "I-organization", "B-organization_person", "I-organization_person", "B-person", "I-person", "B-production_art_music", "I-production_art_music", "B-production_art_music_person", "I-production_art_music_person", "B-quantity", "I-quantity", "B-religion", "I-religion", "B-science", "I-science", "B-shape", "I-shape", "B-ship", "I-ship", "B-software", "I-software", "B-space", "I-space", "B-space_person", "I-space_person", "B-sport", "I-sport", "B-sport_name", "I-sport_name", "B-sport_person", "I-sport_person", "B-structure", "I-structure", "B-subject", "I-subject", "B-tech", "I-tech", "B-train", "I-train", "B-vehicle", "I-vehicle"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "turkish_shrinked_ner", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 200728389, "num_examples": 614515, "dataset_name": "turkish_shrinked_ner"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 200728389, "size_in_bytes": 200728389}}
dummy/0.0.0/dummy_data.zip ADDED
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1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:66afcc7a8328814a5fe5beb583bba3cc384f0897b7bf9ffa7ff37b927a148859
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+ size 30664
turkish_shrinked_ner.py ADDED
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1
+ # 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
+ """ Shrinked Turkish NER """
16
+
17
+ from __future__ import absolute_import, division, print_function
18
+
19
+ import logging
20
+ import os
21
+
22
+ import datasets
23
+
24
+
25
+ _CITATION = """\
26
+ """
27
+
28
+ _DESCRIPTION = """\
29
+ Shrinked version (48 entity type) of the turkish_ner.
30
+
31
+ Original turkish_ner dataset: Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.
32
+
33
+ Shrinked entity types are: academic, academic_person, aircraft, album_person, anatomy, animal, architect_person, capital, chemical, clothes, country, culture, currency, date, food, genre, government, government_person, language, location, material, measure, medical, military, military_person, nation, newspaper, organization, organization_person, person, production_art_music, production_art_music_person, quantity, religion, science, shape, ship, software, space, space_person, sport, sport_name, sport_person, structure, subject, tech, train, vehicle
34
+ """
35
+
36
+ _HOMEPAGE = "https://www.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar"
37
+
38
+ _LICENSE = "Attribution 4.0 International (CC BY 4.0)"
39
+
40
+ _FILENAME = "train.txt"
41
+
42
+
43
+ class TurkishShrinkedNER(datasets.GeneratorBasedBuilder):
44
+ @property
45
+ def manual_download_instructions(self):
46
+ return """\
47
+ You need to go to https://www.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar,
48
+ and manually download the turkish_shrinked_ner. Once it is completed,
49
+ a file named archive.zip will be appeared in your Downloads folder
50
+ or whichever folder your browser chooses to save files to. You then have
51
+ to unzip the file and move train.txt under <path/to/folder>.
52
+ The <path/to/folder> can e.g. be "~/manual_data".
53
+ turkish_shrinked_ner can then be loaded using the following command `datasets.load_dataset("turkish_shrinked_ner", data_dir="<path/to/folder>")`.
54
+ """
55
+
56
+ def _info(self):
57
+ return datasets.DatasetInfo(
58
+ description=_DESCRIPTION,
59
+ features=datasets.Features(
60
+ {
61
+ "id": datasets.Value("string"),
62
+ "tokens": datasets.Sequence(datasets.Value("string")),
63
+ "ner_tags": datasets.Sequence(
64
+ datasets.features.ClassLabel(
65
+ names=[
66
+ "O",
67
+ "B-academic",
68
+ "I-academic",
69
+ "B-academic_person",
70
+ "I-academic_person",
71
+ "B-aircraft",
72
+ "I-aircraft",
73
+ "B-album_person",
74
+ "I-album_person",
75
+ "B-anatomy",
76
+ "I-anatomy",
77
+ "B-animal",
78
+ "I-animal",
79
+ "B-architect_person",
80
+ "I-architect_person",
81
+ "B-capital",
82
+ "I-capital",
83
+ "B-chemical",
84
+ "I-chemical",
85
+ "B-clothes",
86
+ "I-clothes",
87
+ "B-country",
88
+ "I-country",
89
+ "B-culture",
90
+ "I-culture",
91
+ "B-currency",
92
+ "I-currency",
93
+ "B-date",
94
+ "I-date",
95
+ "B-food",
96
+ "I-food",
97
+ "B-genre",
98
+ "I-genre",
99
+ "B-government",
100
+ "I-government",
101
+ "B-government_person",
102
+ "I-government_person",
103
+ "B-language",
104
+ "I-language",
105
+ "B-location",
106
+ "I-location",
107
+ "B-material",
108
+ "I-material",
109
+ "B-measure",
110
+ "I-measure",
111
+ "B-medical",
112
+ "I-medical",
113
+ "B-military",
114
+ "I-military",
115
+ "B-military_person",
116
+ "I-military_person",
117
+ "B-nation",
118
+ "I-nation",
119
+ "B-newspaper",
120
+ "I-newspaper",
121
+ "B-organization",
122
+ "I-organization",
123
+ "B-organization_person",
124
+ "I-organization_person",
125
+ "B-person",
126
+ "I-person",
127
+ "B-production_art_music",
128
+ "I-production_art_music",
129
+ "B-production_art_music_person",
130
+ "I-production_art_music_person",
131
+ "B-quantity",
132
+ "I-quantity",
133
+ "B-religion",
134
+ "I-religion",
135
+ "B-science",
136
+ "I-science",
137
+ "B-shape",
138
+ "I-shape",
139
+ "B-ship",
140
+ "I-ship",
141
+ "B-software",
142
+ "I-software",
143
+ "B-space",
144
+ "I-space",
145
+ "B-space_person",
146
+ "I-space_person",
147
+ "B-sport",
148
+ "I-sport",
149
+ "B-sport_name",
150
+ "I-sport_name",
151
+ "B-sport_person",
152
+ "I-sport_person",
153
+ "B-structure",
154
+ "I-structure",
155
+ "B-subject",
156
+ "I-subject",
157
+ "B-tech",
158
+ "I-tech",
159
+ "B-train",
160
+ "I-train",
161
+ "B-vehicle",
162
+ "I-vehicle",
163
+ ]
164
+ )
165
+ ),
166
+ }
167
+ ),
168
+ supervised_keys=None,
169
+ # Homepage of the dataset for documentation
170
+ homepage=_HOMEPAGE,
171
+ # License for the dataset if available
172
+ license=_LICENSE,
173
+ # Citation for the dataset
174
+ citation=_CITATION,
175
+ )
176
+
177
+ def _split_generators(self, dl_manager):
178
+ """Returns SplitGenerators."""
179
+ path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
180
+ if not os.path.exists(path_to_manual_file):
181
+ raise FileNotFoundError(
182
+ "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('turkish_shrinked_ner', data_dir=...)` that includes file name {}. Manual download instructions: {}".format(
183
+ path_to_manual_file,
184
+ _FILENAME,
185
+ self.manual_download_instructions,
186
+ )
187
+ )
188
+ return [
189
+ datasets.SplitGenerator(
190
+ name=datasets.Split.TRAIN,
191
+ # These kwargs will be passed to _generate_examples
192
+ gen_kwargs={
193
+ "filepath": os.path.join(path_to_manual_file, "train.txt"),
194
+ "split": "train",
195
+ },
196
+ ),
197
+ ]
198
+
199
+ def _generate_examples(self, filepath, split):
200
+ """ Yields examples. """
201
+ logging.info("⏳ Generating examples from = %s", filepath)
202
+
203
+ with open(filepath, encoding="utf-8") as f:
204
+ id_ = 0
205
+ tokens = []
206
+ ner_tags = []
207
+ for row in f:
208
+ if row == "":
209
+ continue
210
+ elif row == "\n":
211
+ yield id_, {
212
+ "id": str(id_),
213
+ "tokens": tokens,
214
+ "ner_tags": ner_tags,
215
+ }
216
+ tokens = []
217
+ ner_tags = []
218
+ id_ += 1
219
+ else:
220
+ token, tag = row.split(" ")
221
+ tokens.append(token)
222
+ ner_tags.append(tag)
223
+
224
+ if len(tokens) > 0:
225
+ yield id_, {
226
+ "id": str(id_),
227
+ "tokens": tokens,
228
+ "ner_tags": ner_tags,
229
+ }