cakiki commited on
Commit
c3518fc
1 Parent(s): b697c26

Initial commit

Browse files
Files changed (3) hide show
  1. .gitattributes +3 -0
  2. README.md +174 -0
  3. args_me.py +115 -0
.gitattributes CHANGED
@@ -14,3 +14,6 @@
14
  *.pb filter=lfs diff=lfs merge=lfs -text
15
  *.pt filter=lfs diff=lfs merge=lfs -text
16
  *.pth filter=lfs diff=lfs merge=lfs -text
 
 
 
 
14
  *.pb filter=lfs diff=lfs merge=lfs -text
15
  *.pt filter=lfs diff=lfs merge=lfs -text
16
  *.pth filter=lfs diff=lfs merge=lfs -text
17
+ *.json filter=lfs diff=lfs merge=lfs -text
18
+ *.zip filter=lfs diff=lfs merge=lfs -text
19
+ *.jsonl filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,174 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ annotations_creators:
3
+ - machine-generated
4
+ language_creators:
5
+ - crowdsourced
6
+ languages:
7
+ - '''en-US'''
8
+ licenses:
9
+ - cc-by-4.0
10
+ multilinguality:
11
+ - monolingual
12
+ pretty_name: Webis args.me argument corpus
13
+ size_categories:
14
+ - 100K<n<1M
15
+ source_datasets:
16
+ - original
17
+ task_categories:
18
+ - text-retrieval
19
+ task_ids:
20
+ - document-retrieval
21
+ ---
22
+ # Dataset Card for the args.me corpus
23
+
24
+ ## Table of Contents
25
+ - [Table of Contents](#table-of-contents)
26
+ - [Dataset Description](#dataset-description)
27
+ - [Dataset Summary](#dataset-summary)
28
+ - [Dataset Usage](#dataset-usage)
29
+ - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
30
+ - [Languages](#languages)
31
+ - [Dataset Structure](#dataset-structure)
32
+ - [Data Instances](#data-instances)
33
+ - [Data Fields](#data-fields)
34
+ - [Data Splits](#data-splits)
35
+ - [Dataset Creation](#dataset-creation)
36
+ - [Curation Rationale](#curation-rationale)
37
+ - [Source Data](#source-data)
38
+ - [Annotations](#annotations)
39
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
40
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
41
+ - [Social Impact of Dataset](#social-impact-of-dataset)
42
+ - [Discussion of Biases](#discussion-of-biases)
43
+ - [Other Known Limitations](#other-known-limitations)
44
+ - [Additional Information](#additional-information)
45
+ - [Dataset Curators](#dataset-curators)
46
+ - [Licensing Information](#licensing-information)
47
+ - [Citation Information](#citation-information)
48
+ - [Contributions](#contributions)
49
+
50
+ ## Dataset Description
51
+
52
+ - **Homepage:** https://zenodo.org/record/4139439
53
+ - **Repository:** https://git.webis.de/code-research/arguana/args/args-framework
54
+ - **Paper:** [Building an Argument Search Engine for the Web](https://webis.de/downloads/publications/papers/wachsmuth_2017f.pdf)
55
+ - **Leaderboard:** https://touche.webis.de/
56
+ - **Point of Contact:** [Webis Group](https://webis.de/people.html)
57
+
58
+ ### Dataset Summary
59
+
60
+ The args.me corpus (version 1.0, cleaned) comprises 382 545 arguments crawled from four debate portals in the middle of 2019. The debate portals are Debatewise, IDebate.org, Debatepedia, and Debate.org. The arguments are extracted using heuristics that are designed for each debate portal.
61
+
62
+ ### Dataset Usage
63
+
64
+ ```python
65
+ import datasets
66
+ args = datasets.load_dataset('cakiki/args_me', 'corpus', streaming=True)
67
+ args_iterator = iter(args)
68
+ for arg in args_iterator:
69
+ print(args['conclusion'])
70
+ print(args['id'])
71
+ print(args['argument'])
72
+ print(args['stance'])
73
+ break
74
+ ```
75
+
76
+ ### Supported Tasks and Leaderboards
77
+
78
+ Document Retrieval, Argument Retrieval for Controversial Questions
79
+
80
+ ### Languages
81
+
82
+ The args.me corpus is monolingual; it only includes English (mostly en-US) documents.
83
+
84
+ ## Dataset Structure
85
+
86
+ ### Data Instances
87
+
88
+ #### Corpus
89
+ ```
90
+ {'conclusion': 'Science is the best!',
91
+ 'id': 'd6517702-2019-04-18T12:36:24Z-00000-000',
92
+ 'argument': 'Science is aright I guess, but Physical Education (P.E) is better. Think about it, you could sit in a classroom for and hour learning about molecular reconfiguration, or you could play football with your mates. Why would you want to learn about molecular reconfiguration anyway? I think the argument here would be based on, healthy mind or healthy body. With science being the healthy mind and P.E being the healthy body. To work this one out all you got to do is ask Steven Hawkins. Only 500 words',
93
+ 'stance': 'CON'}
94
+ ```
95
+ ### Data Fields
96
+
97
+ [More Information Needed]
98
+
99
+ ### Data Splits
100
+
101
+ [More Information Needed]
102
+
103
+ ## Dataset Creation
104
+
105
+ ### Curation Rationale
106
+
107
+ [More Information Needed]
108
+
109
+ ### Source Data
110
+
111
+ #### Initial Data Collection and Normalization
112
+
113
+ [More Information Needed]
114
+
115
+ #### Who are the source language producers?
116
+
117
+ [More Information Needed]
118
+
119
+ ### Annotations
120
+
121
+ #### Annotation process
122
+
123
+ [More Information Needed]
124
+
125
+ #### Who are the annotators?
126
+
127
+ [More Information Needed]
128
+
129
+ ### Personal and Sensitive Information
130
+
131
+ [More Information Needed]
132
+
133
+ ## Considerations for Using the Data
134
+
135
+ ### Social Impact of Dataset
136
+
137
+ [More Information Needed]
138
+
139
+ ### Discussion of Biases
140
+
141
+ [More Information Needed]
142
+
143
+ ### Other Known Limitations
144
+
145
+ [More Information Needed]
146
+
147
+ ## Additional Information
148
+
149
+ ### Dataset Curators
150
+
151
+ [More Information Needed]
152
+
153
+ ### Licensing Information
154
+ [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
155
+
156
+ ### Citation Information
157
+
158
+ ```
159
+ @dataset{yamen_ajjour_2020_4139439,
160
+ author = {Yamen Ajjour and
161
+ Henning Wachsmuth and
162
+ Johannes Kiesel and
163
+ Martin Potthast and
164
+ Matthias Hagen and
165
+ Benno Stein},
166
+ title = {args.me corpus},
167
+ month = oct,
168
+ year = 2020,
169
+ publisher = {Zenodo},
170
+ version = {1.0-cleaned},
171
+ doi = {10.5281/zenodo.4139439},
172
+ url = {https://doi.org/10.5281/zenodo.4139439}
173
+ }
174
+ ```
args_me.py ADDED
@@ -0,0 +1,115 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ """args.me Dataset"""
16
+
17
+
18
+ import json
19
+
20
+ import datasets
21
+
22
+ _CITATION = """\
23
+ @dataset{yamen_ajjour_2020_4139439,
24
+ author = {Yamen Ajjour and
25
+ Henning Wachsmuth and
26
+ Johannes Kiesel and
27
+ Martin Potthast and
28
+ Matthias Hagen and
29
+ Benno Stein},
30
+ title = {args.me corpus},
31
+ month = oct,
32
+ year = 2020,
33
+ publisher = {Zenodo},
34
+ version = {1.0-cleaned},
35
+ doi = {10.5281/zenodo.4139439},
36
+ url = {https://doi.org/10.5281/zenodo.4139439}
37
+ }
38
+ """
39
+
40
+
41
+ _DESCRIPTION = """\
42
+ The args.me corpus (version 1.0, cleaned) comprises 382 545 arguments crawled from four debate portals in the middle of 2019. The debate portals are Debatewise, IDebate.org, Debatepedia, and Debate.org. The arguments are extracted using heuristics that are designed for each debate portal.
43
+ """
44
+
45
+ _HOMEPAGE = "https://zenodo.org/record/4139439"
46
+
47
+ _LICENSE = "https://creativecommons.org/licenses/by/4.0/legalcode"
48
+
49
+
50
+ _REPO = "https://huggingface.co/datasets/webis/args_me/resolve/main"
51
+ _URLs = {
52
+ 'corpus': f"{_REPO}/args-me.jsonl",
53
+ 'topics': f"{_REPO}/topics.jsonl",
54
+ 'judgments': f"{_REPO}/judgments.jsonl"
55
+ }
56
+
57
+
58
+ class ArgsMe(datasets.GeneratorBasedBuilder):
59
+ """382,545 arguments crawled from debate portals"""
60
+
61
+ VERSION = datasets.Version("1.1.0")
62
+ BUILDER_CONFIGS = [
63
+ datasets.BuilderConfig(name="corpus", version=VERSION, description="The args.me dataset"),
64
+ datasets.BuilderConfig(name="topics", version=VERSION, description="The args.me dataset"),
65
+ datasets.BuilderConfig(name="judgments", version=VERSION, description="The args.me dataset"),
66
+ ]
67
+
68
+ DEFAULT_CONFIG_NAME = "corpus"
69
+
70
+ def _info(self):
71
+ features = datasets.Features(
72
+ {
73
+ "argument": datasets.Value("string"),
74
+ "conclusion": datasets.Value("string"),
75
+ "stance": datasets.Value("string"),
76
+ "id": datasets.Value("string")
77
+ }
78
+ )
79
+ return datasets.DatasetInfo(
80
+ description=_DESCRIPTION,
81
+ features=features,
82
+ supervised_keys=None,
83
+ homepage=_HOMEPAGE,
84
+ license=_LICENSE,
85
+ citation=_CITATION,
86
+ )
87
+
88
+ def _split_generators(self, dl_manager):
89
+ """Returns SplitGenerators."""
90
+ URL = _URLs[self.config.name]
91
+ data_file = dl_manager.download(URL)
92
+ return [
93
+ datasets.SplitGenerator(
94
+ name=datasets.Split.TRAIN,
95
+ gen_kwargs={
96
+ "data_file": data_file,
97
+ },
98
+ ),
99
+ ]
100
+
101
+ def _generate_examples(self, data_file):
102
+ """ Yields examples as (key, example) tuples. """
103
+
104
+
105
+ with open(data_file, encoding="utf-8") as f:
106
+ for row in f:
107
+ data = json.loads(row)
108
+ id_ = data['id']
109
+ content = data["premises"][0]
110
+ yield id_, {
111
+ "argument": content['text'],
112
+ "conclusion": data["conclusion"],
113
+ "stance": content['stance'],
114
+ "id": id_
115
+ }