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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
+ - en
8
+ licenses:
9
+ - unknown
10
+ multilinguality:
11
+ - monolingual
12
+ size_categories:
13
+ - 10K<n<100K
14
+ source_datasets:
15
+ - original
16
+ task_categories:
17
+ - text-classification
18
+ task_ids:
19
+ - text-classification-other-acceptability-classification
20
+ ---
21
+
22
+ # Dataset Card for peer_read
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://arxiv.org/abs/1804.09635
50
+ - **Repository:** https://github.com/allenai/PeerRead
51
+ - **Paper:** https://arxiv.org/pdf/1804.09635.pdf
52
+ - **Leaderboard:** [Needs More Information]
53
+ - **Point of Contact:** [Needs More Information]
54
+
55
+ ### Dataset Summary
56
+
57
+ PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers.
58
+
59
+ ### Supported Tasks and Leaderboards
60
+
61
+ [More Information Needed]
62
+
63
+ ### Languages
64
+
65
+ en-English
66
+
67
+ ## Dataset Structure
68
+
69
+ ### Data Instances
70
+
71
+ [More Information Needed]
72
+
73
+ ### Data Fields
74
+
75
+ #### parsed_pdfs
76
+ - `name`: `string` Filename in the dataset
77
+ - `metadata`: `dict` Paper metadata
78
+ - `source`: `string` Paper source
79
+ - `authors`: `list<string>` List of paper authors
80
+ - `title`: `string` Paper title
81
+ - `sections`: `list<dict>` List of section heading and corresponding description
82
+ - `heading`: `string` Section heading
83
+ - `text`: `string` Section description
84
+ - `references`: `string` List of references
85
+ - `title`: `string` Title of reference paper
86
+ - `author`: `list<string>` List of reference paper authors
87
+ - `venue`: `string` Reference venue
88
+ - `citeRegEx`: `string` Reference citeRegEx
89
+ - `shortCiteRegEx`: `string` Reference shortCiteRegEx
90
+ - `year`: `int` Reference publish year
91
+ - `referenceMentions`: `list<string>` List of reference mentions
92
+ - `referenceID`: `int` Reference mention ID
93
+ - `context`: `string` Reference mention context
94
+ - `startOffset`: `int` Reference startOffset
95
+ - `endOffset`: `int` Reference endOffset
96
+ - `year`: `int` Paper publish year
97
+ - `abstractText`: `string` Paper abstract
98
+ - `creator`: `string` Paper creator
99
+
100
+ #### reviews
101
+ - `id`: `int` Review ID
102
+ - `conference`: `string` Conference name
103
+ - `comments`: `string` Review comments
104
+ - `subjects`: `string` Review subjects
105
+ - `version`: `string` Review version
106
+ - `date_of_submission`: `string` Submission date
107
+ - `title`: `string` Paper title
108
+ - `authors`: `list<string>` List of paper authors
109
+ - `accepted`: `bool` Paper accepted flag
110
+ - `abstract`: `string` Paper abstract
111
+ - `histories`: `list<string>` Paper details with link
112
+ - `reviews`: `dict` Paper reviews
113
+ - `date`: `string` Date of review
114
+ - `title`: `string` Paper title
115
+ - `other_keys`: `string` Reviewer other details
116
+ - `originality`: `string` Originality score
117
+ - `comments`: `string` Reviewer comments
118
+ - `is_meta_review`: `bool` Review type flag
119
+ - `recommendation`: `string` Reviewer recommendation
120
+ - `replicability`: `string` Replicability score
121
+ - `presentation_format`: `string` Presentation type
122
+ - `clarity`: `string` Clarity score
123
+ - `meaningful_comparison`: `string` Meaningful comparison score
124
+ - `substance`: `string` Substance score
125
+ - `reviewer_confidence`: `string` Reviewer confidence score
126
+ - `soundness_correctness`: `string` Soundness correctness score
127
+ - `appropriateness`: `string` Appropriateness score
128
+ - `impact`: `string` Impact score
129
+
130
+ ### Data Splits
131
+
132
+ [More Information Needed]
133
+
134
+ ## Dataset Creation
135
+
136
+ ### Curation Rationale
137
+
138
+ [More Information Needed]
139
+
140
+ ### Source Data
141
+
142
+ #### Initial Data Collection and Normalization
143
+
144
+ [More Information Needed]
145
+
146
+ #### Who are the source language producers?
147
+
148
+ [More Information Needed]
149
+
150
+ ### Annotations
151
+
152
+ #### Annotation process
153
+
154
+ [More Information Needed]
155
+
156
+ #### Who are the annotators?
157
+
158
+ [More Information Needed]
159
+
160
+ ### Personal and Sensitive Information
161
+
162
+ [More Information Needed]
163
+
164
+ ## Considerations for Using the Data
165
+
166
+ ### Social Impact of Dataset
167
+
168
+ [More Information Needed]
169
+
170
+ ### Discussion of Biases
171
+
172
+ [More Information Needed]
173
+
174
+ ### Other Known Limitations
175
+
176
+ [More Information Needed]
177
+
178
+ ## Additional Information
179
+
180
+ ### Dataset Curators
181
+
182
+ Dongyeop Kang, Waleed Ammar, Bhavana Dalvi Mishra, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz
183
+
184
+ ### Licensing Information
185
+
186
+ [More Information Needed]
187
+
188
+ ### Citation Information
189
+
190
+ @inproceedings{kang18naacl,
191
+ title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications},
192
+ author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz},
193
+ booktitle = {Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL)},
194
+ address = {New Orleans, USA},
195
+ month = {June},
196
+ url = {https://arxiv.org/abs/1804.09635},
197
+ year = {2018}
198
+ }
dataset_infos.json ADDED
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+ {"parsed_pdfs": {"description": "PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers.\n", "citation": "@inproceedings{kang18naacl,\n title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications},\n author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz},\n booktitle = {Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL)},\n address = {New Orleans, USA},\n month = {June},\n url = {https://arxiv.org/abs/1804.09635},\n year = {2018}\n}\n", "homepage": "https://github.com/allenai/PeerRead", "license": "Creative Commons Public License", "features": {"name": {"dtype": "string", "id": null, "_type": "Value"}, "metadata": {"source": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "authors": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "emails": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "sections": {"feature": {"heading": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "references": {"feature": {"title": {"dtype": "string", "id": null, "_type": "Value"}, "author": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "venue": {"dtype": "string", "id": null, "_type": "Value"}, "citeRegEx": {"dtype": "string", "id": null, "_type": "Value"}, "shortCiteRegEx": {"dtype": "string", "id": null, "_type": "Value"}, "year": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "referenceMentions": {"feature": {"referenceID": {"dtype": "int32", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "startOffset": {"dtype": "int32", "id": null, "_type": "Value"}, "endOffset": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "year": {"dtype": "int32", "id": null, "_type": "Value"}, "abstractText": {"dtype": "string", "id": null, "_type": "Value"}, "creator": {"dtype": "string", "id": null, "_type": "Value"}}}, "post_processed": null, "supervised_keys": null, "builder_name": "peer_read", "config_name": "parsed_pdfs", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 571263679, "num_examples": 11090, "dataset_name": "peer_read"}, "test": {"name": "test", "num_bytes": 34284777, "num_examples": 637, "dataset_name": "peer_read"}, "validation": {"name": "validation", "num_bytes": 32488519, "num_examples": 637, "dataset_name": "peer_read"}}, "download_checksums": {"https://github.com/allenai/PeerRead/archive/master.zip": {"num_bytes": 1246688292, "checksum": "c8010a97ddc74184c15916576976c565acc68c843a5c9dbe3da987dd2cd2e518"}}, "download_size": 1246688292, "post_processing_size": null, "dataset_size": 638036975, "size_in_bytes": 1884725267}, "reviews": {"description": "PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers.\n", "citation": "@inproceedings{kang18naacl,\n title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications},\n author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz},\n booktitle = {Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL)},\n address = {New Orleans, USA},\n month = {June},\n url = {https://arxiv.org/abs/1804.09635},\n year = {2018}\n}\n", "homepage": "https://github.com/allenai/PeerRead", "license": "Creative Commons Public License", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "conference": {"dtype": "string", "id": null, "_type": "Value"}, "comments": {"dtype": "string", "id": null, "_type": "Value"}, "subjects": {"dtype": "string", "id": null, "_type": "Value"}, "version": {"dtype": "string", "id": null, "_type": "Value"}, "date_of_submission": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "authors": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "accepted": {"dtype": "bool", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "histories": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "reviews": {"feature": {"date": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "other_keys": {"dtype": "string", "id": null, "_type": "Value"}, "originality": {"dtype": "string", "id": null, "_type": "Value"}, "comments": {"dtype": "string", "id": null, "_type": "Value"}, "is_meta_review": {"dtype": "bool", "id": null, "_type": "Value"}, "is_annotated": {"dtype": "bool", "id": null, "_type": "Value"}, "recommendation": {"dtype": "string", "id": null, "_type": "Value"}, "replicability": {"dtype": "string", "id": null, "_type": "Value"}, "presentation_format": {"dtype": "string", "id": null, "_type": "Value"}, "clarity": {"dtype": "string", "id": null, "_type": "Value"}, "meaningful_comparison": {"dtype": "string", "id": null, "_type": "Value"}, "substance": {"dtype": "string", "id": null, "_type": "Value"}, "reviewer_confidence": {"dtype": "string", "id": null, "_type": "Value"}, "soundness_correctness": {"dtype": "string", "id": null, "_type": "Value"}, "appropriateness": {"dtype": "string", "id": null, "_type": "Value"}, "impact": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "peer_read", "config_name": "reviews", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 15234922, "num_examples": 11090, "dataset_name": "peer_read"}, "test": {"name": "test", "num_bytes": 878906, "num_examples": 637, "dataset_name": "peer_read"}, "validation": {"name": "validation", "num_bytes": 864799, "num_examples": 637, "dataset_name": "peer_read"}}, "download_checksums": {"https://github.com/allenai/PeerRead/archive/master.zip": {"num_bytes": 1246688292, "checksum": "c8010a97ddc74184c15916576976c565acc68c843a5c9dbe3da987dd2cd2e518"}}, "download_size": 1246688292, "post_processing_size": null, "dataset_size": 16978627, "size_in_bytes": 1263666919}}
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peer_read.py ADDED
@@ -0,0 +1,311 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ """A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications"""
16
+
17
+ from __future__ import absolute_import, division, print_function
18
+
19
+ import glob
20
+ import json
21
+ import os
22
+
23
+ import datasets
24
+
25
+
26
+ _CITATION = """\
27
+ @inproceedings{kang18naacl,
28
+ title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications},
29
+ author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz},
30
+ booktitle = {Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL)},
31
+ address = {New Orleans, USA},
32
+ month = {June},
33
+ url = {https://arxiv.org/abs/1804.09635},
34
+ year = {2018}
35
+ }
36
+ """
37
+
38
+ _DESCRIPTION = """\
39
+ PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers.
40
+ """
41
+
42
+ _HOMEPAGE = "https://github.com/allenai/PeerRead"
43
+
44
+ _LICENSE = "Creative Commons Public License"
45
+
46
+ _URLs = {
47
+ "dataset_repo": "https://github.com/allenai/PeerRead/archive/master.zip",
48
+ }
49
+
50
+
51
+ class PeerRead(datasets.GeneratorBasedBuilder):
52
+ """A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications"""
53
+
54
+ VERSION = datasets.Version("1.1.0")
55
+
56
+ BUILDER_CONFIGS = [
57
+ datasets.BuilderConfig(
58
+ name="parsed_pdfs",
59
+ version=VERSION,
60
+ description="Research paper drafts",
61
+ ),
62
+ datasets.BuilderConfig(
63
+ name="reviews",
64
+ version=VERSION,
65
+ description="Accept/reject decisions in top-tier venues including ACL, NIPS and ICLR",
66
+ ),
67
+ ]
68
+
69
+ @staticmethod
70
+ def _get_paths(data_dir, domain):
71
+ paths = {"train": [], "test": [], "dev": []}
72
+ conference_paths = glob.glob(os.path.join(data_dir, "PeerRead-master/data/*"))
73
+ for conference_path in conference_paths:
74
+ for dtype in ["test", "train", "dev"]:
75
+ file_paths = glob.glob(os.path.join(conference_path, dtype, domain, "*.json"))
76
+ for file_path in file_paths:
77
+ paths[dtype].append(file_path)
78
+ return paths
79
+
80
+ @staticmethod
81
+ def _parse_histories(histories):
82
+ if histories is None:
83
+ return [[]]
84
+ if isinstance(histories, str):
85
+ return [[histories]]
86
+ return histories
87
+
88
+ @staticmethod
89
+ def _parse_reviews(data):
90
+ reviews = []
91
+ for review in data.get("metadata", {}).get("reviews", []):
92
+ if isinstance(review, dict):
93
+ reviews.append(
94
+ {
95
+ "date": str(review.get("date", "")),
96
+ "title": str(review.get("title", "")),
97
+ "other_keys": str(review.get("other_keys", "")),
98
+ "originality": str(review.get("originality", "")),
99
+ "comments": str(review.get("comments", "")),
100
+ "is_meta_review": str(review.get("is_meta_review", "")),
101
+ "is_annotated": str(review.get("is_annotated", "")),
102
+ "recommendation": str(review.get("recommendation", "")),
103
+ "replicability": str(review.get("replicability", "")),
104
+ "presentation_format": str(review.get("presentation_format", "")),
105
+ "clarity": str(review.get("clarity", "")),
106
+ "meaningful_comparison": str(review.get("meaningful_comparison", "")),
107
+ "substance": str(review.get("substance", "")),
108
+ "reviewer_confidence": str(review.get("reviewer_confidence", "")),
109
+ "soundness_correctness": str(review.get("soundness_correctness", "")),
110
+ "appropriateness": str(review.get("appropriateness", "")),
111
+ "impact": str(review.get("impact")),
112
+ }
113
+ )
114
+ return reviews
115
+
116
+ @staticmethod
117
+ def _decode(text):
118
+ return str(text).encode("utf-8", "replace").decode("utf-8")
119
+
120
+ def _info(self):
121
+ if (
122
+ self.config.name == "parsed_pdfs"
123
+ ): # This is the name of the configuration selected in BUILDER_CONFIGS above
124
+ features = datasets.Features(
125
+ {
126
+ "name": datasets.Value("string"),
127
+ "metadata": {
128
+ "source": datasets.Value("string"),
129
+ "title": datasets.Value("string"),
130
+ "authors": datasets.features.Sequence(datasets.Value("string")),
131
+ "emails": datasets.features.Sequence(datasets.Value("string")),
132
+ "sections": datasets.features.Sequence(
133
+ {
134
+ "heading": datasets.Value("string"),
135
+ "text": datasets.Value("string"),
136
+ }
137
+ ),
138
+ "references": datasets.features.Sequence(
139
+ {
140
+ "title": datasets.Value("string"),
141
+ "author": datasets.features.Sequence(datasets.Value("string")),
142
+ "venue": datasets.Value("string"),
143
+ "citeRegEx": datasets.Value("string"),
144
+ "shortCiteRegEx": datasets.Value("string"),
145
+ "year": datasets.Value("int32"),
146
+ }
147
+ ),
148
+ "referenceMentions": datasets.features.Sequence(
149
+ {
150
+ "referenceID": datasets.Value("int32"),
151
+ "context": datasets.Value("string"),
152
+ "startOffset": datasets.Value("int32"),
153
+ "endOffset": datasets.Value("int32"),
154
+ }
155
+ ),
156
+ "year": datasets.Value("int32"),
157
+ "abstractText": datasets.Value("string"),
158
+ "creator": datasets.Value("string"),
159
+ },
160
+ }
161
+ )
162
+ else:
163
+ features = datasets.Features(
164
+ {
165
+ "id": datasets.Value("string"),
166
+ "conference": datasets.Value("string"),
167
+ "comments": datasets.Value("string"),
168
+ "subjects": datasets.Value("string"),
169
+ "version": datasets.Value("string"),
170
+ "date_of_submission": datasets.Value("string"),
171
+ "title": datasets.Value("string"),
172
+ "authors": datasets.features.Sequence(datasets.Value("string")),
173
+ "accepted": datasets.Value("bool"),
174
+ "abstract": datasets.Value("string"),
175
+ "histories": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
176
+ "reviews": datasets.features.Sequence(
177
+ {
178
+ "date": datasets.Value("string"),
179
+ "title": datasets.Value("string"),
180
+ "other_keys": datasets.Value("string"),
181
+ "originality": datasets.Value("string"),
182
+ "comments": datasets.Value("string"),
183
+ "is_meta_review": datasets.Value("bool"),
184
+ "is_annotated": datasets.Value("bool"),
185
+ "recommendation": datasets.Value("string"),
186
+ "replicability": datasets.Value("string"),
187
+ "presentation_format": datasets.Value("string"),
188
+ "clarity": datasets.Value("string"),
189
+ "meaningful_comparison": datasets.Value("string"),
190
+ "substance": datasets.Value("string"),
191
+ "reviewer_confidence": datasets.Value("string"),
192
+ "soundness_correctness": datasets.Value("string"),
193
+ "appropriateness": datasets.Value("string"),
194
+ "impact": datasets.Value("string"),
195
+ }
196
+ ),
197
+ }
198
+ )
199
+ return datasets.DatasetInfo(
200
+ description=_DESCRIPTION,
201
+ features=features,
202
+ supervised_keys=None,
203
+ homepage=_HOMEPAGE,
204
+ license=_LICENSE,
205
+ citation=_CITATION,
206
+ )
207
+
208
+ def _split_generators(self, dl_manager):
209
+ """Returns SplitGenerators."""
210
+ url = _URLs["dataset_repo"]
211
+ data_dir = dl_manager.download_and_extract(url)
212
+ paths = self._get_paths(
213
+ data_dir=data_dir,
214
+ domain=self.config.name,
215
+ )
216
+
217
+ return [
218
+ datasets.SplitGenerator(
219
+ name=datasets.Split.TRAIN,
220
+ gen_kwargs={
221
+ "filepaths": paths["train"],
222
+ "split": "train",
223
+ },
224
+ ),
225
+ datasets.SplitGenerator(
226
+ name=datasets.Split.TEST,
227
+ gen_kwargs={"filepaths": paths["test"], "split": "test"},
228
+ ),
229
+ datasets.SplitGenerator(
230
+ name=datasets.Split.VALIDATION,
231
+ gen_kwargs={
232
+ "filepaths": paths["dev"],
233
+ "split": "dev",
234
+ },
235
+ ),
236
+ ]
237
+
238
+ def _generate_examples(self, filepaths, split):
239
+ """ Yields examples. """
240
+ for id_, filepath in enumerate(sorted(filepaths)):
241
+ with open(filepath, encoding="utf-8", errors="replace") as f:
242
+ data = json.load(f)
243
+ if self.config.name == "parsed_pdfs":
244
+ metadata = data.get(
245
+ "metadata",
246
+ {
247
+ "source": "",
248
+ "authors": [],
249
+ "title": [],
250
+ "sections": [],
251
+ "references": [],
252
+ "referenceMentions": [],
253
+ "year": "",
254
+ "abstractText": "",
255
+ "creator": "",
256
+ },
257
+ )
258
+ metadata["sections"] = [] if metadata["sections"] is None else metadata["sections"]
259
+ metadata["sections"] = [
260
+ {
261
+ "heading": self._decode(section.get("heading", "")),
262
+ "text": self._decode(section.get("text", "")),
263
+ }
264
+ for section in metadata["sections"]
265
+ ]
266
+ metadata["references"] = [] if metadata["references"] is None else metadata["references"]
267
+ metadata["references"] = [
268
+ {
269
+ "title": reference.get("title", ""),
270
+ "author": reference.get("author", []),
271
+ "venue": reference.get("venue", ""),
272
+ "citeRegEx": reference.get("citeRegEx", ""),
273
+ "shortCiteRegEx": reference.get("shortCiteRegEx", ""),
274
+ "year": reference.get("year", ""),
275
+ }
276
+ for reference in metadata["references"]
277
+ ]
278
+ metadata["referenceMentions"] = (
279
+ [] if metadata["referenceMentions"] is None else metadata["referenceMentions"]
280
+ )
281
+ metadata["referenceMentions"] = [
282
+ {
283
+ "referenceID": self._decode(reference_mention.get("referenceID", "")),
284
+ "context": self._decode(reference_mention.get("context", "")),
285
+ "startOffset": self._decode(reference_mention.get("startOffset", "")),
286
+ "endOffset": self._decode(reference_mention.get("endOffset", "")),
287
+ }
288
+ for reference_mention in metadata["referenceMentions"]
289
+ ]
290
+
291
+ yield id_, {
292
+ "name": data.get("name", ""),
293
+ "metadata": metadata,
294
+ }
295
+ elif self.config.name == "reviews":
296
+ yield id_, {
297
+ "id": str(data.get("id", "")),
298
+ "conference": str(data.get("conference", "")),
299
+ "comments": str(data.get("comments", "")),
300
+ "subjects": str(data.get("subjects", "")),
301
+ "version": str(data.get("version", "")),
302
+ "date_of_submission": str(data.get("date_of_submission", "")),
303
+ "title": str(data.get("title", "")),
304
+ "authors": data.get("authors", [])
305
+ if isinstance(data.get("authors"), list)
306
+ else ([data.get("authors")] if data.get("authors") else []),
307
+ "accepted": str(data.get("accepted", "")),
308
+ "abstract": str(data.get("abstract", "")),
309
+ "histories": self._parse_histories(data.get("histories", [])),
310
+ "reviews": self._parse_reviews(data),
311
+ }