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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
+ - found
6
+ languages:
7
+ ca:
8
+ - ca
9
+ eu:
10
+ - eu
11
+ licenses:
12
+ - cc-by-3-0
13
+ multilinguality:
14
+ - monolingual
15
+ size_categories:
16
+ - n<1K
17
+ source_datasets:
18
+ - original
19
+ task_categories:
20
+ - text-classification
21
+ task_ids:
22
+ - sentiment-classification
23
+ ---
24
+
25
+ # Dataset Card for MultiBooked
26
+
27
+ ## Table of Contents
28
+ - [Dataset Description](#dataset-description)
29
+ - [Dataset Summary](#dataset-summary)
30
+ - [Supported Tasks](#supported-tasks-and-leaderboards)
31
+ - [Languages](#languages)
32
+ - [Dataset Structure](#dataset-structure)
33
+ - [Data Instances](#data-instances)
34
+ - [Data Fields](#data-instances)
35
+ - [Data Splits](#data-instances)
36
+ - [Dataset Creation](#dataset-creation)
37
+ - [Curation Rationale](#curation-rationale)
38
+ - [Source Data](#source-data)
39
+ - [Annotations](#annotations)
40
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
41
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
42
+ - [Social Impact of Dataset](#social-impact-of-dataset)
43
+ - [Discussion of Biases](#discussion-of-biases)
44
+ - [Other Known Limitations](#other-known-limitations)
45
+ - [Additional Information](#additional-information)
46
+ - [Dataset Curators](#dataset-curators)
47
+ - [Licensing Information](#licensing-information)
48
+ - [Citation Information](#citation-information)
49
+
50
+ ## Dataset Description
51
+
52
+ - **Homepage:** http://hdl.handle.net/10230/33928
53
+ - **Repository:** https://github.com/jerbarnes/multibooked
54
+ - **Paper:** https://arxiv.org/abs/1803.08614
55
+ - **Leaderboard:**
56
+ - **Point of Contact:**
57
+
58
+ ### Dataset Summary
59
+
60
+ MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.
61
+
62
+ The corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is
63
+ an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and
64
+ word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two
65
+ annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the
66
+ guidelines set out in the OpeNER project.
67
+
68
+ ### Supported Tasks and Leaderboards
69
+
70
+ [More Information Needed]
71
+
72
+ ### Languages
73
+
74
+ Each sub-dataset is monolingual in the languages:
75
+ - ca: Catalan
76
+ - eu: Basque
77
+
78
+ ## Dataset Structure
79
+
80
+ ### Data Instances
81
+
82
+ [More Information Needed]
83
+
84
+ ### Data Fields
85
+
86
+ - `text`: layer of the original text.
87
+ - `wid`: list of word IDs for each word within the example.
88
+ - `sent`: list of sentence IDs for each sentence within the example.
89
+ - `para`: list of paragraph IDs for each paragraph within the example.
90
+ - `word`: list of words.
91
+ - `terms`: layer of the terms resulting from the analysis of the original text (lemmatization, morphological,
92
+ PoS tagging)
93
+ - `tid`: list of term IDs for each term within the example.
94
+ - `lemma`: list of lemmas.
95
+ - `morphofeat`: list of morphological features.
96
+ - `pos`: list of PoS tags.
97
+ - `target`: list of sublists of the corresponding word IDs (normally, the sublists contain only one element,
98
+ in a one-to-one correspondence between words and terms).
99
+ - `opinions`: layer of the opinions in the text.
100
+ - `oid`: list of opinion IDs
101
+ - `opinion_holder_target`: list of sublists of the corresponding term IDs that span the opinion holder.
102
+ - `opinion_target_target`: list of sublists of the corresponding term IDs that span the opinion target.
103
+ - `opinion_expression_polarity`: list of the opinion expression polarities. The polarity can take one of the values:
104
+ `StrongNegative`, `Negative`, `Positive`, or `StrongPositive`.
105
+ - `opinion_expression_target`: list of sublists of the corresponding term IDs that span the opinion expression.
106
+
107
+ ### Data Splits
108
+
109
+ [More Information Needed]
110
+
111
+ ## Dataset Creation
112
+
113
+ ### Curation Rationale
114
+
115
+ [More Information Needed]
116
+
117
+ ### Source Data
118
+
119
+ #### Initial Data Collection and Normalization
120
+
121
+ [More Information Needed]
122
+
123
+ #### Who are the source language producers?
124
+
125
+ [More Information Needed]
126
+
127
+ ### Annotations
128
+
129
+ #### Annotation process
130
+
131
+ [More Information Needed]
132
+
133
+ #### Who are the annotators?
134
+
135
+ [More Information Needed]
136
+
137
+ ### Personal and Sensitive Information
138
+
139
+ [More Information Needed]
140
+
141
+ ## Considerations for Using the Data
142
+
143
+ ### Social Impact of Dataset
144
+
145
+ [More Information Needed]
146
+
147
+ ### Discussion of Biases
148
+
149
+ [More Information Needed]
150
+
151
+ ### Other Known Limitations
152
+
153
+ [More Information Needed]
154
+
155
+ ## Additional Information
156
+
157
+ ### Dataset Curators
158
+
159
+ [More Information Needed]
160
+
161
+ ### Licensing Information
162
+
163
+ Dataset is under the [CC-BY 3.0](https://creativecommons.org/licenses/by/3.0/) license.
164
+
165
+ ### Citation Information
166
+
167
+ ```
168
+ @inproceedings{Barnes2018multibooked,
169
+ author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni},
170
+ title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification},
171
+ booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18)},
172
+ year = {2018},
173
+ month = {May},
174
+ date = {7-12},
175
+ address = {Miyazaki, Japan},
176
+ publisher = {European Language Resources Association (ELRA)},
177
+ language = {english}
178
+ }
179
+ ```
dataset_infos.json ADDED
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+ {"ca": {"description": "MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.\n\nThe corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is\nan xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and\nword-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two\nannotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the\nguidelines set out in the OpeNER project.\n", "citation": "@inproceedings{Barnes2018multibooked,\n author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni},\n title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification},\n booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18)},\n year = {2018},\n month = {May},\n date = {7-12},\n address = {Miyazaki, Japan},\n publisher = {European Language Resources Association (ELRA)},\n language = {english}\n}\n", "homepage": "http://hdl.handle.net/10230/33928", "license": "CC-BY 3.0", "features": {"text": {"feature": {"wid": {"dtype": "string", "id": null, "_type": "Value"}, "sent": {"dtype": "string", "id": null, "_type": "Value"}, "para": {"dtype": "string", "id": null, "_type": "Value"}, "word": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "terms": {"feature": {"tid": {"dtype": "string", "id": null, "_type": "Value"}, "lemma": {"dtype": "string", "id": null, "_type": "Value"}, "morphofeat": {"dtype": "string", "id": null, "_type": "Value"}, "pos": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "opinions": {"feature": {"oid": {"dtype": "string", "id": null, "_type": "Value"}, "opinion_holder_target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "opinion_target_target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "opinion_expression_polarity": {"num_classes": 4, "names": ["StrongNegative", "Negative", "Positive", "StrongPositive"], "names_file": null, "id": null, "_type": "ClassLabel"}, "opinion_expression_target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "multi_booked", "config_name": "ca", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1952731, "num_examples": 567, "dataset_name": "multi_booked"}}, "download_checksums": {"https://github.com/jerbarnes/multibooked/archive/master.zip": {"num_bytes": 4429415, "checksum": "c9512b7fe0e5afc690aaf0205d61bc37e68836e008db05f17643a0de57007c80"}}, "download_size": 4429415, "post_processing_size": null, "dataset_size": 1952731, "size_in_bytes": 6382146}, "eu": {"description": "MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.\n\nThe corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is\nan xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and\nword-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two\nannotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the\nguidelines set out in the OpeNER project.\n", "citation": "@inproceedings{Barnes2018multibooked,\n author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni},\n title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification},\n booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18)},\n year = {2018},\n month = {May},\n date = {7-12},\n address = {Miyazaki, Japan},\n publisher = {European Language Resources Association (ELRA)},\n language = {english}\n}\n", "homepage": "http://hdl.handle.net/10230/33928", "license": "CC-BY 3.0", "features": {"text": {"feature": {"wid": {"dtype": "string", "id": null, "_type": "Value"}, "sent": {"dtype": "string", "id": null, "_type": "Value"}, "para": {"dtype": "string", "id": null, "_type": "Value"}, "word": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "terms": {"feature": {"tid": {"dtype": "string", "id": null, "_type": "Value"}, "lemma": {"dtype": "string", "id": null, "_type": "Value"}, "morphofeat": {"dtype": "string", "id": null, "_type": "Value"}, "pos": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "opinions": {"feature": {"oid": {"dtype": "string", "id": null, "_type": "Value"}, "opinion_holder_target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "opinion_target_target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "opinion_expression_polarity": {"num_classes": 4, "names": ["StrongNegative", "Negative", "Positive", "StrongPositive"], "names_file": null, "id": null, "_type": "ClassLabel"}, "opinion_expression_target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "multi_booked", "config_name": "eu", "version": "0.0.0", "splits": {"train": {"name": "train", "num_bytes": 1175816, "num_examples": 343, "dataset_name": "multi_booked"}}, "download_checksums": {"https://github.com/jerbarnes/multibooked/archive/master.zip": {"num_bytes": 4429415, "checksum": "c9512b7fe0e5afc690aaf0205d61bc37e68836e008db05f17643a0de57007c80"}}, "download_size": 4429415, "post_processing_size": null, "dataset_size": 1175816, "size_in_bytes": 5605231}}
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multi_booked.py ADDED
@@ -0,0 +1,165 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ """MultiBooked dataset."""
16
+
17
+ import os
18
+ import xml.etree.ElementTree as ET
19
+ from collections import defaultdict
20
+ from pathlib import Path
21
+
22
+ import datasets
23
+
24
+
25
+ _CITATION = """\
26
+ @inproceedings{Barnes2018multibooked,
27
+ author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni},
28
+ title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification},
29
+ booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18)},
30
+ year = {2018},
31
+ month = {May},
32
+ date = {7-12},
33
+ address = {Miyazaki, Japan},
34
+ publisher = {European Language Resources Association (ELRA)},
35
+ language = {english}
36
+ }
37
+ """
38
+
39
+ _DESCRIPTION = """\
40
+ MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.
41
+
42
+ The corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is
43
+ an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and
44
+ word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two
45
+ annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the
46
+ guidelines set out in the OpeNER project.
47
+ """
48
+
49
+ _HOMEPAGE = "http://hdl.handle.net/10230/33928"
50
+
51
+ _LICENSE = "CC-BY 3.0"
52
+
53
+ _URL = "https://github.com/jerbarnes/multibooked/archive/master.zip"
54
+
55
+
56
+ class MultiBooked(datasets.GeneratorBasedBuilder):
57
+ """MultiBooked dataset."""
58
+
59
+ VERSION = datasets.Version("0.0.0")
60
+
61
+ BUILDER_CONFIGS = [
62
+ datasets.BuilderConfig(name="ca", description="MultiBooked dataset in Catalan language."),
63
+ datasets.BuilderConfig(name="eu", description="MultiBooked dataset in Basque language."),
64
+ ]
65
+
66
+ def _info(self):
67
+ return datasets.DatasetInfo(
68
+ description=_DESCRIPTION,
69
+ features=datasets.Features(
70
+ {
71
+ "text": datasets.features.Sequence(
72
+ {
73
+ "wid": datasets.Value("string"),
74
+ "sent": datasets.Value("string"),
75
+ "para": datasets.Value("string"),
76
+ "word": datasets.Value("string"),
77
+ }
78
+ ),
79
+ "terms": datasets.features.Sequence(
80
+ {
81
+ "tid": datasets.Value("string"),
82
+ "lemma": datasets.Value("string"),
83
+ "morphofeat": datasets.Value("string"),
84
+ "pos": datasets.Value("string"),
85
+ "target": datasets.features.Sequence(datasets.Value("string")),
86
+ }
87
+ ),
88
+ "opinions": datasets.features.Sequence(
89
+ {
90
+ "oid": datasets.Value("string"),
91
+ "opinion_holder_target": datasets.features.Sequence(datasets.Value("string")),
92
+ "opinion_target_target": datasets.features.Sequence(datasets.Value("string")),
93
+ "opinion_expression_polarity": datasets.features.ClassLabel(
94
+ names=["StrongNegative", "Negative", "Positive", "StrongPositive"]
95
+ ),
96
+ "opinion_expression_target": datasets.features.Sequence(datasets.Value("string")),
97
+ }
98
+ ),
99
+ }
100
+ ),
101
+ supervised_keys=None,
102
+ homepage=_HOMEPAGE,
103
+ license=_LICENSE,
104
+ citation=_CITATION,
105
+ )
106
+
107
+ def _split_generators(self, dl_manager):
108
+ data_dir = dl_manager.download_and_extract(_URL)
109
+ return [
110
+ datasets.SplitGenerator(
111
+ name=datasets.Split.TRAIN,
112
+ gen_kwargs={
113
+ "dirpath": os.path.join(data_dir, "multibooked-master", "corpora", self.config.name),
114
+ },
115
+ ),
116
+ ]
117
+
118
+ def _generate_examples(self, dirpath):
119
+ for id_, filepath in enumerate(sorted(Path(dirpath).iterdir())):
120
+ example = defaultdict(lambda: defaultdict(list))
121
+ with open(filepath, encoding="utf-8") as f:
122
+ for _, elem in ET.iterparse(f):
123
+ if elem.tag == "text":
124
+ for child in elem:
125
+ # sometimes wid is missing in the eu configuration
126
+ example["text"]["wid"].append(child.attrib.get("wid", ""))
127
+ example["text"]["sent"].append(child.attrib["sent"])
128
+ example["text"]["para"].append(child.attrib["para"])
129
+ example["text"]["word"].append(child.text)
130
+ elif elem.tag == "terms":
131
+ for child in elem:
132
+ # sometimes tid is missing in the eu configuration
133
+ example["terms"]["tid"].append(child.attrib.get("tid", ""))
134
+ example["terms"]["lemma"].append(child.attrib["lemma"])
135
+ example["terms"]["morphofeat"].append(child.attrib["morphofeat"])
136
+ example["terms"]["pos"].append(child.attrib["pos"])
137
+ targets = []
138
+ for target in child.findall("span/target"):
139
+ targets.append(target.attrib["id"])
140
+ example["terms"]["target"].append(targets)
141
+ elif elem.tag == "opinions":
142
+ for child in elem:
143
+ example["opinions"]["oid"].append(child.attrib["oid"])
144
+ # Opinion holder
145
+ opinion_holder = child.find("opinion_holder")
146
+ targets = []
147
+ for target in opinion_holder.findall("span/target"):
148
+ targets.append(target.attrib["id"])
149
+ example["opinions"]["opinion_holder_target"].append(targets)
150
+ # Opinion target
151
+ opinion_target = child.find("opinion_target")
152
+ targets = []
153
+ for target in opinion_target.findall("span/target"):
154
+ targets.append(target.attrib["id"])
155
+ example["opinions"]["opinion_target_target"].append(targets)
156
+ # Opinion expression
157
+ opinion_expression = child.find("opinion_expression")
158
+ example["opinions"]["opinion_expression_polarity"].append(
159
+ opinion_expression.attrib["polarity"]
160
+ )
161
+ targets = []
162
+ for target in opinion_expression.findall("span/target"):
163
+ targets.append(target.attrib["id"])
164
+ example["opinions"]["opinion_expression_target"].append(targets)
165
+ yield id_, example