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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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README.md ADDED
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1
+ ---
2
+ annotations_creators:
3
+ - expert-generated
4
+ language_creators:
5
+ - expert-generated
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+ languages:
7
+ - fa
8
+ licenses:
9
+ - cc-by-4-0
10
+ multilinguality:
11
+ - monolingual
12
+ size_categories:
13
+ - 1K<n<10K
14
+ source_datasets:
15
+ - original
16
+ task_categories:
17
+ - structure-prediction
18
+ task_ids:
19
+ - named-entity-recognition
20
+ ---
21
+
22
+ # Dataset Card for [Persian 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:** [Github](https://github.com/HaniehP/PersianNER)
50
+ - **Repository:** [Github](https://github.com/HaniehP/PersianNER)
51
+ - **Paper:** [Aclweb](https://www.aclweb.org/anthology/C16-1319)
52
+ - **Leaderboard:**
53
+ - **Point of Contact:**
54
+
55
+ ### Dataset Summary
56
+
57
+ The dataset includes 7,682 Persian sentences, split into 250,015 tokens and their NER labels. It is available in 3 folds to be used in turn as training and test sets. The NER tags are in IOB format.
58
+
59
+ ### Supported Tasks and Leaderboards
60
+
61
+ [More Information Needed]
62
+
63
+ ### Languages
64
+
65
+ [More Information Needed]
66
+
67
+ ## Dataset Structure
68
+
69
+ ### Data Instances
70
+
71
+
72
+ ### Data Fields
73
+
74
+ - `id`: id of the sample
75
+ - `tokens`: the tokens of the example text
76
+ - `ner_tags`: the NER tags of each token
77
+
78
+ The NER tags correspond to this list:
79
+ ```
80
+ "O", "I-event", "I-fac", "I-loc", "I-org", "I-pers", "I-pro", "B-event", "B-fac", "B-loc", "B-org", "B-pers", "B-pro"
81
+ ```
82
+
83
+ ### Data Splits
84
+
85
+ Training and test splits
86
+
87
+ ## Dataset Creation
88
+
89
+ ### Curation Rationale
90
+
91
+ [More Information Needed]
92
+
93
+ ### Source Data
94
+
95
+ #### Initial Data Collection and Normalization
96
+
97
+ [More Information Needed]
98
+
99
+ #### Who are the source language producers?
100
+
101
+ Hanieh Poostchi, Ehsan Zare Borzeshi, Mohammad Abdous, Massimo Piccardi
102
+
103
+ ### Annotations
104
+
105
+ #### Annotation process
106
+
107
+ [More Information Needed]
108
+
109
+ #### Who are the annotators?
110
+
111
+ Hanieh Poostchi, Ehsan Zare Borzeshi, Mohammad Abdous, Massimo Piccardi
112
+
113
+ ### Personal and Sensitive Information
114
+
115
+ [More Information Needed]
116
+
117
+ ## Considerations for Using the Data
118
+
119
+ ### Social Impact of Dataset
120
+
121
+ [More Information Needed]
122
+
123
+ ### Discussion of Biases
124
+
125
+ [More Information Needed]
126
+
127
+ ### Other Known Limitations
128
+
129
+ [More Information Needed]
130
+
131
+ ## Additional Information
132
+
133
+ Dataset is published for academic use only
134
+
135
+ ### Dataset Curators
136
+
137
+ [More Information Needed]
138
+
139
+ ### Licensing Information
140
+
141
+ Creative Commons Attribution 4.0 International License.
142
+
143
+ ### Citation Information
144
+
145
+ @inproceedings{poostchi-etal-2016-personer,
146
+ title = "{P}erso{NER}: {P}ersian Named-Entity Recognition",
147
+ author = "Poostchi, Hanieh and
148
+ Zare Borzeshi, Ehsan and
149
+ Abdous, Mohammad and
150
+ Piccardi, Massimo",
151
+ booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
152
+ month = dec,
153
+ year = "2016",
154
+ address = "Osaka, Japan",
155
+ publisher = "The COLING 2016 Organizing Committee",
156
+ url = "https://www.aclweb.org/anthology/C16-1319",
157
+ pages = "3381--3389",
158
+ abstract = "Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network.",
159
+ }
dataset_infos.json ADDED
@@ -0,0 +1 @@
 
1
+ {"fold1": {"description": "The dataset includes 250,015 tokens and 7,682 Persian sentences in total. It is available in 3 folds to be used in turn as training and test sets. The NER tags are in IOB format.\n", "citation": "@inproceedings{poostchi-etal-2016-personer,\n title = \"{P}erso{NER}: {P}ersian Named-Entity Recognition\",\n author = \"Poostchi, Hanieh and\n Zare Borzeshi, Ehsan and\n Abdous, Mohammad and\n Piccardi, Massimo\",\n booktitle = \"Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers\",\n month = dec,\n year = \"2016\",\n address = \"Osaka, Japan\",\n publisher = \"The COLING 2016 Organizing Committee\",\n url = \"https://www.aclweb.org/anthology/C16-1319\",\n pages = \"3381--3389\",\n abstract = \"Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network.\",\n}\n", "homepage": "", "license": "Creative Commons Attribution 4.0 International License", "features": {"tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 13, "names": ["O", "I-event", "I-fac", "I-loc", "I-org", "I-pers", "I-pro", "B-event", "B-fac", "B-loc", "B-org", "B-pers", "B-pro"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": {"input": "tokens", "output": "ner_tags"}, "builder_name": "persian_ner", "config_name": "fold1", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3362102, "num_examples": 5121, "dataset_name": "persian_ner"}, "test": {"name": "test", "num_bytes": 1646481, "num_examples": 2560, "dataset_name": "persian_ner"}}, "download_checksums": {"https://github.com/HaniehP/PersianNER/raw/master/ArmanPersoNERCorpus.zip": {"num_bytes": 1931170, "checksum": "4a7aa9b3a52707468bb40f00b152f83fa922a76d20b6b0e2c24845712c34b664"}}, "download_size": 1931170, "post_processing_size": null, "dataset_size": 5008583, "size_in_bytes": 6939753}, "fold2": {"description": "The dataset includes 250,015 tokens and 7,682 Persian sentences in total. It is available in 3 folds to be used in turn as training and test sets. The NER tags are in IOB format.\n", "citation": "@inproceedings{poostchi-etal-2016-personer,\n title = \"{P}erso{NER}: {P}ersian Named-Entity Recognition\",\n author = \"Poostchi, Hanieh and\n Zare Borzeshi, Ehsan and\n Abdous, Mohammad and\n Piccardi, Massimo\",\n booktitle = \"Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers\",\n month = dec,\n year = \"2016\",\n address = \"Osaka, Japan\",\n publisher = \"The COLING 2016 Organizing Committee\",\n url = \"https://www.aclweb.org/anthology/C16-1319\",\n pages = \"3381--3389\",\n abstract = \"Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network.\",\n}\n", "homepage": "", "license": "Creative Commons Attribution 4.0 International License", "features": {"tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 13, "names": ["O", "I-event", "I-fac", "I-loc", "I-org", "I-pers", "I-pro", "B-event", "B-fac", "B-loc", "B-org", "B-pers", "B-pro"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": {"input": "tokens", "output": "ner_tags"}, "builder_name": "persian_ner", "config_name": "fold2", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3344561, "num_examples": 5120, "dataset_name": "persian_ner"}, "test": {"name": "test", "num_bytes": 1664022, "num_examples": 2561, "dataset_name": "persian_ner"}}, "download_checksums": {"https://github.com/HaniehP/PersianNER/raw/master/ArmanPersoNERCorpus.zip": {"num_bytes": 1931170, "checksum": "4a7aa9b3a52707468bb40f00b152f83fa922a76d20b6b0e2c24845712c34b664"}}, "download_size": 1931170, "post_processing_size": null, "dataset_size": 5008583, "size_in_bytes": 6939753}, "fold3": {"description": "The dataset includes 250,015 tokens and 7,682 Persian sentences in total. It is available in 3 folds to be used in turn as training and test sets. The NER tags are in IOB format.\n", "citation": "@inproceedings{poostchi-etal-2016-personer,\n title = \"{P}erso{NER}: {P}ersian Named-Entity Recognition\",\n author = \"Poostchi, Hanieh and\n Zare Borzeshi, Ehsan and\n Abdous, Mohammad and\n Piccardi, Massimo\",\n booktitle = \"Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers\",\n month = dec,\n year = \"2016\",\n address = \"Osaka, Japan\",\n publisher = \"The COLING 2016 Organizing Committee\",\n url = \"https://www.aclweb.org/anthology/C16-1319\",\n pages = \"3381--3389\",\n abstract = \"Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network.\",\n}\n", "homepage": "", "license": "Creative Commons Attribution 4.0 International License", "features": {"tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 13, "names": ["O", "I-event", "I-fac", "I-loc", "I-org", "I-pers", "I-pro", "B-event", "B-fac", "B-loc", "B-org", "B-pers", "B-pro"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": {"input": "tokens", "output": "ner_tags"}, "builder_name": "persian_ner", "config_name": "fold3", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3310491, "num_examples": 5121, "dataset_name": "persian_ner"}, "test": {"name": "test", "num_bytes": 1698092, "num_examples": 2560, "dataset_name": "persian_ner"}}, "download_checksums": {"https://github.com/HaniehP/PersianNER/raw/master/ArmanPersoNERCorpus.zip": {"num_bytes": 1931170, "checksum": "4a7aa9b3a52707468bb40f00b152f83fa922a76d20b6b0e2c24845712c34b664"}}, "download_size": 1931170, "post_processing_size": null, "dataset_size": 5008583, "size_in_bytes": 6939753}}
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persian_ner.py ADDED
@@ -0,0 +1,168 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ """ArmanPerosNERCorpus - the first manually-annotated Persian NER corpus."""
16
+
17
+ from __future__ import absolute_import, division, print_function
18
+
19
+ import os
20
+
21
+ import datasets
22
+
23
+
24
+ _CITATION = """\
25
+ @inproceedings{poostchi-etal-2016-personer,
26
+ title = "{P}erso{NER}: {P}ersian Named-Entity Recognition",
27
+ author = "Poostchi, Hanieh and
28
+ Zare Borzeshi, Ehsan and
29
+ Abdous, Mohammad and
30
+ Piccardi, Massimo",
31
+ booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
32
+ month = dec,
33
+ year = "2016",
34
+ address = "Osaka, Japan",
35
+ publisher = "The COLING 2016 Organizing Committee",
36
+ url = "https://www.aclweb.org/anthology/C16-1319",
37
+ pages = "3381--3389",
38
+ abstract = "Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network.",
39
+ }
40
+ """
41
+
42
+ # TODO: Add description of the dataset here
43
+ # You can copy an official description
44
+ _DESCRIPTION = """\
45
+ The dataset includes 250,015 tokens and 7,682 Persian sentences in total. It is available in 3 folds to be used in turn as training and test sets. The NER tags are in IOB format.
46
+ """
47
+
48
+ _HOMEPAGE = ""
49
+
50
+ _LICENSE = "Creative Commons Attribution 4.0 International License"
51
+
52
+ _URL = "https://github.com/HaniehP/PersianNER/raw/master/ArmanPersoNERCorpus.zip"
53
+
54
+
55
+ class PersianNER(datasets.GeneratorBasedBuilder):
56
+ """ArmanPerosNERCorpus - the first manually-annotated Persian NER corpus."""
57
+
58
+ VERSION = datasets.Version("1.1.0")
59
+
60
+ # This is an example of a dataset with multiple configurations.
61
+ # If you don't want/need to define several sub-sets in your dataset,
62
+ # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
63
+
64
+ # If you need to make complex sub-parts in the datasets with configurable options
65
+ # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
66
+ # BUILDER_CONFIG_CLASS = MyBuilderConfig
67
+
68
+ # You will be able to load one or the other configurations in the following list with
69
+ # data = datasets.load_dataset('my_dataset', 'first_domain')
70
+ # data = datasets.load_dataset('my_dataset', 'second_domain')
71
+ BUILDER_CONFIGS = [
72
+ datasets.BuilderConfig(
73
+ name="fold1", version=VERSION, description="This is the first fold of the ArmanPersoNERCorpus"
74
+ ),
75
+ datasets.BuilderConfig(
76
+ name="fold2", version=VERSION, description="This is the second fold of the ArmanPersoNERCorpus"
77
+ ),
78
+ datasets.BuilderConfig(
79
+ name="fold3", version=VERSION, description="This is the third fold of the ArmanPersoNERCorpus"
80
+ ),
81
+ ]
82
+
83
+ DEFAULT_CONFIG_NAME = "fold1" # It's not mandatory to have a default configuration. Just use one if it make sense.
84
+
85
+ def _info(self):
86
+ features = datasets.Features(
87
+ {
88
+ "tokens": datasets.Sequence(datasets.Value("string")),
89
+ "ner_tags": datasets.Sequence(
90
+ datasets.ClassLabel(
91
+ names=[
92
+ "O",
93
+ "I-event",
94
+ "I-fac",
95
+ "I-loc",
96
+ "I-org",
97
+ "I-pers",
98
+ "I-pro",
99
+ "B-event",
100
+ "B-fac",
101
+ "B-loc",
102
+ "B-org",
103
+ "B-pers",
104
+ "B-pro",
105
+ ]
106
+ )
107
+ ),
108
+ }
109
+ )
110
+
111
+ return datasets.DatasetInfo(
112
+ description=_DESCRIPTION,
113
+ features=features,
114
+ supervised_keys=("tokens", "ner_tags"),
115
+ homepage=_HOMEPAGE,
116
+ license=_LICENSE,
117
+ citation=_CITATION,
118
+ )
119
+
120
+ def _split_generators(self, dl_manager):
121
+ """Returns SplitGenerators."""
122
+
123
+ my_urls = _URL
124
+ data_dir = dl_manager.download_and_extract(my_urls)
125
+
126
+ return [
127
+ datasets.SplitGenerator(
128
+ name=datasets.Split.TRAIN,
129
+ gen_kwargs={
130
+ "filepath": os.path.join(data_dir, f"train_{self.config.name}.txt"),
131
+ "split": "train",
132
+ },
133
+ ),
134
+ datasets.SplitGenerator(
135
+ name=datasets.Split.TEST,
136
+ gen_kwargs={"filepath": os.path.join(data_dir, f"test_{self.config.name}.txt"), "split": "test"},
137
+ ),
138
+ ]
139
+
140
+ def _generate_examples(self, filepath, split):
141
+ """ Yields examples. """
142
+
143
+ with open(filepath, "r", encoding="utf-8") as f:
144
+ id_ = 0
145
+ tokens = []
146
+ ner_labels = []
147
+ for line in f:
148
+ stripped_line = line.strip(" \n") # strip away whitespaces AND new line characters
149
+ if len(stripped_line) == 0:
150
+ # If line is empty, it means we reached the end of a sentence.
151
+ # We can yield the tokens and labels
152
+ if len(tokens) > 0 and len(ner_labels) > 0:
153
+ yield id_, {
154
+ "tokens": tokens,
155
+ "ner_tags": ner_labels,
156
+ }
157
+ else:
158
+ # Do not yield if tokens or ner_labels is empty
159
+ # It can be the case if several empty lines are contiguous
160
+ continue
161
+ # Then we need to increment the _id and reset the tokens and ner_labels list
162
+ id_ += 1
163
+ tokens = []
164
+ ner_labels = []
165
+ else:
166
+ token, ner_label = line.split(" ") # Retrieve token and label
167
+ tokens.append(token)
168
+ ner_labels.append(ner_label)