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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

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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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1
+ ---
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+ annotations_creators:
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+ - expert-generated
4
+ language_creators:
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+ - expert-generated
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+ languages:
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+ - yo
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+ licenses:
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+ - Creative Commons 3-0
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+ multilinguality:
11
+ - monolingual
12
+ size_categories:
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+ - 200<n<1k
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - structure-prediction
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+ task_ids:
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+ - named-entity-recognition
20
+ ---
21
+
22
+ # Dataset Card for Yoruba GV NER Corpus
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+
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)
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+ - [Licensing Information](#licensing-information)
45
+ - [Citation Information](#citation-information)
46
+
47
+ ## Dataset Description
48
+
49
+ - **Homepage:**
50
+ - **Repository:** [Yoruba GV NER](https://github.com/ajesujoba/YorubaTwi-Embedding/tree/master/Yoruba/Yoruba-NER)
51
+ - **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335/
52
+ - **Leaderboard:**
53
+ - **Point of Contact:** [David Adelani](mailto:didelani@lsv.uni-saarland.de)
54
+
55
+ ### Dataset Summary
56
+ The Yoruba GV NER is a named entity recognition (NER) dataset for Yorùbá language based on the [Global Voices news](https://yo.globalvoices.org/) corpus. Global Voices (GV) is a multilingual news platform with articles contributed by journalists, translators, bloggers, and human rights activists from around the world with a coverage of over 50 languages. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá.
57
+
58
+ ### Supported Tasks and Leaderboards
59
+
60
+ [More Information Needed]
61
+
62
+ ### Languages
63
+
64
+ The language supported is Yorùbá.
65
+
66
+ ## Dataset Structure
67
+
68
+ ### Data Instances
69
+
70
+ A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
71
+ {'id': '0',
72
+ 'ner_tags': [B-LOC, 0, 0, 0, 0],
73
+ 'tokens': ['Tanzania', 'fi', 'Ajìjàgbara', 'Ọmọ', 'Orílẹ̀-èdèe']
74
+ }
75
+
76
+ ### Data Fields
77
+
78
+ - `id`: id of the sample
79
+ - `tokens`: the tokens of the example text
80
+ - `ner_tags`: the NER tags of each token
81
+
82
+ The NER tags correspond to this list:
83
+ ```
84
+ "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE",
85
+ ```
86
+ The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & times (DATE). (O) is used for tokens not considered part of any named entity.
87
+
88
+ ### Data Splits
89
+
90
+ Training (19,421 tokens), validation (2,695 tokens) and test split (5,235 tokens)
91
+
92
+ ## Dataset Creation
93
+
94
+ ### Curation Rationale
95
+
96
+ The data was created to help introduce resources to new language - Yorùbá.
97
+
98
+ [More Information Needed]
99
+
100
+ ### Source Data
101
+
102
+ #### Initial Data Collection and Normalization
103
+
104
+ The dataset is based on the news domain and was crawled from [Global Voices Yorùbá news](https://yo.globalvoices.org/).
105
+
106
+
107
+ [More Information Needed]
108
+
109
+ #### Who are the source language producers?
110
+
111
+ The dataset contributed by journalists, translators, bloggers, and human rights activists from around the world. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá
112
+ [More Information Needed]
113
+
114
+ ### Annotations
115
+
116
+ #### Annotation process
117
+
118
+ [More Information Needed]
119
+
120
+ #### Who are the annotators?
121
+
122
+ The data was annotated by Jesujoba Alabi and David Adelani for the paper:
123
+ [Massive vs. Curated Embeddings for Low-Resourced Languages: the case of Yorùbá and Twi](https://www.aclweb.org/anthology/2020.lrec-1.335/).
124
+
125
+ [More Information Needed]
126
+
127
+ ### Personal and Sensitive Information
128
+
129
+ [More Information Needed]
130
+
131
+ ## Considerations for Using the Data
132
+
133
+ ### Social Impact of Dataset
134
+
135
+ [More Information Needed]
136
+
137
+ ### Discussion of Biases
138
+
139
+ [More Information Needed]
140
+
141
+ ### Other Known Limitations
142
+
143
+ [More Information Needed]
144
+
145
+ ## Additional Information
146
+
147
+ ### Dataset Curators
148
+
149
+ The annotated data sets were developed by students of Saarland University, Saarbrücken, Germany .
150
+
151
+
152
+ ### Licensing Information
153
+
154
+ The data is under the [Creative Commons Attribution 3.0 ](https://creativecommons.org/licenses/by/3.0/)
155
+
156
+ ### Citation Information
157
+ ```
158
+ @inproceedings{alabi-etal-2020-massive,
159
+ title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi",
160
+ author = "Alabi, Jesujoba and
161
+ Amponsah-Kaakyire, Kwabena and
162
+ Adelani, David and
163
+ Espa{\~n}a-Bonet, Cristina",
164
+ booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
165
+ month = may,
166
+ year = "2020",
167
+ address = "Marseille, France",
168
+ publisher = "European Language Resources Association",
169
+ url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
170
+ pages = "2754--2762",
171
+ language = "English",
172
+ ISBN = "979-10-95546-34-4",
173
+ }
174
+ ```
dataset_infos.json ADDED
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+ {"yoruba_gv_ner": {"description": "The Yoruba GV NER dataset is a labeled dataset for named entity recognition in Yoruba. The texts were obtained from\nYoruba Global Voices News articles https://yo.globalvoices.org/ . We concentrate on\nfour types of named entities: persons [PER], locations [LOC], organizations [ORG], and dates & time [DATE].\n\nThe Yoruba GV NER data files contain 2 columns separated by a tab ('\t'). Each word has been put on a separate line and\nthere is an empty line after each sentences i.e the CoNLL format. The first item on each line is a word, the second\nis the named entity tag. The named entity tags have the format I-TYPE which means that the word is inside a phrase\nof type TYPE. For every multi-word expression like 'New York', the first word gets a tag B-TYPE and the subsequent words\nhave tags I-TYPE, a word with tag O is not part of a phrase. The dataset is in the BIO tagging scheme.\n\nFor more details, see https://www.aclweb.org/anthology/2020.lrec-1.335/\n", "citation": "@inproceedings{alabi-etal-2020-massive,\n title = \"Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Yor\u00f9b\u00e1} and {T}wi\",\n author = \"Alabi, Jesujoba and\n Amponsah-Kaakyire, Kwabena and\n Adelani, David and\n Espa{\\~n}a-Bonet, Cristina\",\n booktitle = \"Proceedings of the 12th Language Resources and Evaluation Conference\",\n month = may,\n year = \"2020\",\n address = \"Marseille, France\",\n publisher = \"European Language Resources Association\",\n url = \"https://www.aclweb.org/anthology/2020.lrec-1.335\",\n pages = \"2754--2762\",\n language = \"English\",\n ISBN = \"979-10-95546-34-4\",\n}\n", "homepage": "https://www.aclweb.org/anthology/2020.lrec-1.335/", "license": "", "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": 9, "names": ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "yoruba_gv_ner", "config_name": "yoruba_gv_ner", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 358885, "num_examples": 817, "dataset_name": "yoruba_gv_ner"}, "validation": {"name": "validation", "num_bytes": 50161, "num_examples": 117, "dataset_name": "yoruba_gv_ner"}, "test": {"name": "test", "num_bytes": 96518, "num_examples": 237, "dataset_name": "yoruba_gv_ner"}}, "download_checksums": {"https://github.com/ajesujoba/YorubaTwi-Embedding/raw/master/Yoruba/Yoruba-NER/train.tsv": {"num_bytes": 180612, "checksum": "7479c7a8eb7a576b7d38bcc740c50af10c2b452e81e6d3788fe48ace1e1c94f7"}, "https://github.com/ajesujoba/YorubaTwi-Embedding/raw/master/Yoruba/Yoruba-NER/valid.tsv": {"num_bytes": 25701, "checksum": "da82cc5bbdf4084d39089b6ca381eb3f9af1639b4ec4782dffc0824e0854e6be"}, "https://github.com/ajesujoba/YorubaTwi-Embedding/raw/master/Yoruba/Yoruba-NER/test.tsv": {"num_bytes": 48034, "checksum": "d20485e56ab680ba7216c212dd680a35d91217175600d0d2aec1b03d593fffe4"}}, "download_size": 254347, "post_processing_size": null, "dataset_size": 505564, "size_in_bytes": 759911}}
dummy/yoruba_gv_ner/1.0.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:3b38139d90e6afabee5449ad99abb18eb9c0453bab0bd73e167a3ac4d72459aa
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+ size 614
yoruba_gv_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
+
16
+ """Introduction to the Yoruba GV NER dataset: A Yoruba Global Voices (News) Named Entity Recognition Dataset"""
17
+
18
+
19
+ from __future__ import absolute_import, division, print_function
20
+
21
+ import logging
22
+
23
+ import datasets
24
+
25
+
26
+ # TODO: Add BibTeX citation
27
+ # Find for instance the citation on arxiv or on the dataset repo/website
28
+ _CITATION = """\
29
+ @inproceedings{alabi-etal-2020-massive,
30
+ title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Yorùbá} and {T}wi",
31
+ author = "Alabi, Jesujoba and
32
+ Amponsah-Kaakyire, Kwabena and
33
+ Adelani, David and
34
+ Espa{\\~n}a-Bonet, Cristina",
35
+ booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
36
+ month = may,
37
+ year = "2020",
38
+ address = "Marseille, France",
39
+ publisher = "European Language Resources Association",
40
+ url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
41
+ pages = "2754--2762",
42
+ language = "English",
43
+ ISBN = "979-10-95546-34-4",
44
+ }
45
+ """
46
+
47
+ # TODO: Add description of the dataset here
48
+ # You can copy an official description
49
+ _DESCRIPTION = """\
50
+ The Yoruba GV NER dataset is a labeled dataset for named entity recognition in Yoruba. The texts were obtained from
51
+ Yoruba Global Voices News articles https://yo.globalvoices.org/ . We concentrate on
52
+ four types of named entities: persons [PER], locations [LOC], organizations [ORG], and dates & time [DATE].
53
+
54
+ The Yoruba GV NER data files contain 2 columns separated by a tab ('\t'). Each word has been put on a separate line and
55
+ there is an empty line after each sentences i.e the CoNLL format. The first item on each line is a word, the second
56
+ is the named entity tag. The named entity tags have the format I-TYPE which means that the word is inside a phrase
57
+ of type TYPE. For every multi-word expression like 'New York', the first word gets a tag B-TYPE and the subsequent words
58
+ have tags I-TYPE, a word with tag O is not part of a phrase. The dataset is in the BIO tagging scheme.
59
+
60
+ For more details, see https://www.aclweb.org/anthology/2020.lrec-1.335/
61
+ """
62
+
63
+ _URL = "https://github.com/ajesujoba/YorubaTwi-Embedding/raw/master/Yoruba/Yoruba-NER/"
64
+ _TRAINING_FILE = "train.tsv"
65
+ _DEV_FILE = "valid.tsv"
66
+ _TEST_FILE = "test.tsv"
67
+
68
+
69
+ class YorubaGvNerConfig(datasets.BuilderConfig):
70
+ """BuilderConfig for YorubaGvNer"""
71
+
72
+ def __init__(self, **kwargs):
73
+ """BuilderConfig for YorubaGvNer.
74
+ Args:
75
+ **kwargs: keyword arguments forwarded to super.
76
+ """
77
+ super(YorubaGvNerConfig, self).__init__(**kwargs)
78
+
79
+
80
+ class YorubaGvNer(datasets.GeneratorBasedBuilder):
81
+ """Yoruba GV NER dataset."""
82
+
83
+ BUILDER_CONFIGS = [
84
+ YorubaGvNerConfig(
85
+ name="yoruba_gv_ner", version=datasets.Version("1.0.0"), description="Yoruba GV NER dataset"
86
+ ),
87
+ ]
88
+
89
+ def _info(self):
90
+ return datasets.DatasetInfo(
91
+ description=_DESCRIPTION,
92
+ features=datasets.Features(
93
+ {
94
+ "id": datasets.Value("string"),
95
+ "tokens": datasets.Sequence(datasets.Value("string")),
96
+ "ner_tags": datasets.Sequence(
97
+ datasets.features.ClassLabel(
98
+ names=[
99
+ "O",
100
+ "B-PER",
101
+ "I-PER",
102
+ "B-ORG",
103
+ "I-ORG",
104
+ "B-LOC",
105
+ "I-LOC",
106
+ "B-DATE",
107
+ "I-DATE",
108
+ ]
109
+ )
110
+ ),
111
+ }
112
+ ),
113
+ supervised_keys=None,
114
+ homepage="https://www.aclweb.org/anthology/2020.lrec-1.335/",
115
+ citation=_CITATION,
116
+ )
117
+
118
+ def _split_generators(self, dl_manager):
119
+ """Returns SplitGenerators."""
120
+ urls_to_download = {
121
+ "train": f"{_URL}{_TRAINING_FILE}",
122
+ "dev": f"{_URL}{_DEV_FILE}",
123
+ "test": f"{_URL}{_TEST_FILE}",
124
+ }
125
+ downloaded_files = dl_manager.download_and_extract(urls_to_download)
126
+
127
+ return [
128
+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
129
+ datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
130
+ datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
131
+ ]
132
+
133
+ def _generate_examples(self, filepath):
134
+ logging.info("⏳ Generating examples from = %s", filepath)
135
+ with open(filepath, encoding="utf-8") as f:
136
+ guid = 0
137
+ tokens = []
138
+ ner_tags = []
139
+ for line in f:
140
+ line = line.strip()
141
+ if line.startswith("-DOCSTART-") or line == "" or line == "\n":
142
+ if tokens:
143
+ yield guid, {
144
+ "id": str(guid),
145
+ "tokens": tokens,
146
+ "ner_tags": ner_tags,
147
+ }
148
+ guid += 1
149
+ tokens = []
150
+ ner_tags = []
151
+ else:
152
+ # yoruba_gv_ner tokens are tab separated
153
+ splits = line.strip().split("\t")
154
+ tokens.append(splits[0])
155
+ ner_tags.append(splits[1].rstrip())
156
+ # last example
157
+ yield guid, {
158
+ "id": str(guid),
159
+ "tokens": tokens,
160
+ "ner_tags": ner_tags,
161
+ }