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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
+ - found
4
+ language_creators:
5
+ - machine-generated
6
+ languages:
7
+ - en
8
+ licenses:
9
+ - unknown
10
+ multilinguality:
11
+ - monolingual
12
+ size_categories:
13
+ - 100K<n<1M
14
+ source_datasets:
15
+ - original
16
+ task_categories:
17
+ - other
18
+ task_ids:
19
+ - other-other-open-information-extraction
20
+ ---
21
+
22
+ # Dataset Card for [Dataset Name]
23
+
24
+ ## Table of Contents
25
+ - [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
26
+ - [Table of Contents](#table-of-contents)
27
+ - [Dataset Description](#dataset-description)
28
+ - [Dataset Summary](#dataset-summary)
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
+ - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
39
+ - [Who are the source language producers?](#who-are-the-source-language-producers)
40
+ - [Annotations](#annotations)
41
+ - [Annotation process](#annotation-process)
42
+ - [Who are the annotators?](#who-are-the-annotators)
43
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
44
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
45
+ - [Social Impact of Dataset](#social-impact-of-dataset)
46
+ - [Discussion of Biases](#discussion-of-biases)
47
+ - [Other Known Limitations](#other-known-limitations)
48
+ - [Additional Information](#additional-information)
49
+ - [Dataset Curators](#dataset-curators)
50
+ - [Licensing Information](#licensing-information)
51
+ - [Citation Information](#citation-information)
52
+
53
+ ## Dataset Description
54
+
55
+ - **Homepage: [Tuple IE Homepage](https://allenai.org/data/tuple-ie)**
56
+ - **Repository:**
57
+ - **Paper: [Answering Complex Questions Using Open Information Extraction](https://www.semanticscholar.org/paper/Answering-Complex-Questions-Using-Open-Information-Khot-Sabharwal/0ff595f0645a3e25a2f37145768985b10ead0509)**
58
+ - **Leaderboard:**
59
+ - **Point of Contact:**
60
+
61
+ ### Dataset Summary
62
+
63
+ The TupleInf Open IE dataset contains Open IE tuples extracted from 263K sentences that were used by the solver in “Answering Complex Questions Using Open Information Extraction” (referred as Tuple KB, T). These sentences were collected from a large Web corpus using training questions from 4th and 8th grade as queries. This dataset contains 156K sentences collected for 4th grade questions and 107K sentences for 8th grade questions. Each sentence is followed by the Open IE v4 tuples using their simple format.
64
+
65
+ ### Supported Tasks and Leaderboards
66
+
67
+ [More Information Needed]
68
+
69
+ ### Languages
70
+
71
+ The text in the dataset is in English, collected from a large Web corpus using training questions from 4th and 8th grade as queries.
72
+
73
+ ## Dataset Structure
74
+
75
+ ### Data Instances
76
+
77
+ This dataset contains setences with corresponding relation tuples extracted from each sentence. Each instance should contain a sentence and followed by the [Open IE v4](https://github.com/allenai/openie-standalone) tuples using their *simple format*.
78
+ An example of an instance:
79
+
80
+ ```JSON
81
+ {
82
+ "sentence": "0.04593 kg Used a triple beam balance to mass a golf ball.",
83
+ "tuples": {
84
+ "score": 0.8999999761581421,
85
+ "tuple_text": "(0.04593 kg; Used; a triple beam balance; to mass a golf ball)",
86
+ "context": "",
87
+ "arg1": "0.04593 kg",
88
+ "rel": "Used",
89
+ "arg2s": ["a triple beam balance", "to mass a golf ball"],
90
+ }
91
+ }
92
+ ```
93
+
94
+ ### Data Fields
95
+
96
+ - `setence`: the input text/sentence.
97
+ - `tuples`: the extracted relation tuples from the sentence.
98
+ - `score`: the confident score for each tuple.
99
+ - `tuple_text`: the relationship representation text of the extraction, in the *simple format* of [Open IE v4](https://github.com/allenai/openie-standalone).
100
+ - `context`: an optional representation of the context for this extraction. Defaults to `""` if there's no context.
101
+ - `arg1`: the first argument in the relationship.
102
+ - `rel`: the relation.
103
+ - `arg2s`: a sequence of the 2nd arguments in the realtionship.
104
+
105
+ ### Data Splits
106
+
107
+ [More Information Needed]
108
+
109
+ ## Dataset Creation
110
+
111
+ ### Curation Rationale
112
+
113
+ [More Information Needed]
114
+
115
+ ### Source Data
116
+
117
+ #### Initial Data Collection and Normalization
118
+
119
+ [More Information Needed]
120
+
121
+ #### Who are the source language producers?
122
+
123
+ [More Information Needed]
124
+
125
+ ### Annotations
126
+
127
+ #### Annotation process
128
+
129
+ [More Information Needed]
130
+
131
+ #### Who are the annotators?
132
+
133
+ [More Information Needed]
134
+
135
+ ### Personal and Sensitive Information
136
+
137
+ [More Information Needed]
138
+
139
+ ## Considerations for Using the Data
140
+
141
+ ### Social Impact of Dataset
142
+
143
+ [More Information Needed]
144
+
145
+ ### Discussion of Biases
146
+
147
+ [More Information Needed]
148
+
149
+ ### Other Known Limitations
150
+
151
+ [More Information Needed]
152
+
153
+ ## Additional Information
154
+
155
+ ### Dataset Curators
156
+
157
+ [More Information Needed]
158
+
159
+ ### Licensing Information
160
+
161
+ [More Information Needed]
162
+
163
+ ### Citation Information
164
+
165
+ [More Information Needed]
dataset_infos.json ADDED
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+ {"all": {"description": "The TupleInf Open IE dataset contains Open IE tuples extracted from 263K sentences that were used by the solver in \u201cAnswering Complex Questions Using Open Information Extraction\u201d (referred as Tuple KB, T). These sentences were collected from a large Web corpus using training questions from 4th and 8th grade as queries. This dataset contains 156K sentences collected for 4th grade questions and 107K sentences for 8th grade questions. Each sentence is followed by the Open IE v4 tuples using their simple format.\n", "citation": "@article{Khot2017AnsweringCQ,\n title={Answering Complex Questions Using Open Information Extraction},\n author={Tushar Khot and A. Sabharwal and Peter Clark},\n journal={ArXiv},\n year={2017},\n volume={abs/1704.05572}\n}\n", "homepage": "https://allenai.org/data/tuple-ie", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "tuples": {"feature": {"score": {"dtype": "float32", "id": null, "_type": "Value"}, "tuple_text": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "arg1": {"dtype": "string", "id": null, "_type": "Value"}, "rel": {"dtype": "string", "id": null, "_type": "Value"}, "arg2s": {"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": "tuple_ie", "config_name": "all", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 115621096, "num_examples": 267719, "dataset_name": "tuple_ie"}}, "download_checksums": {"https://ai2-datasets.s3-us-west-2.amazonaws.com/tuple-ie/TupleInfKB.zip": {"num_bytes": 18026102, "checksum": "5ae98009e45ef2d29570d9c6c4575ff5339ddd019f9524d8b5c62b8a50aba56d"}}, "download_size": 18026102, "post_processing_size": null, "dataset_size": 115621096, "size_in_bytes": 133647198}, "4th_grade": {"description": "The TupleInf Open IE dataset contains Open IE tuples extracted from 263K sentences that were used by the solver in \u201cAnswering Complex Questions Using Open Information Extraction\u201d (referred as Tuple KB, T). These sentences were collected from a large Web corpus using training questions from 4th and 8th grade as queries. This dataset contains 156K sentences collected for 4th grade questions and 107K sentences for 8th grade questions. Each sentence is followed by the Open IE v4 tuples using their simple format.\n", "citation": "@article{Khot2017AnsweringCQ,\n title={Answering Complex Questions Using Open Information Extraction},\n author={Tushar Khot and A. Sabharwal and Peter Clark},\n journal={ArXiv},\n year={2017},\n volume={abs/1704.05572}\n}\n", "homepage": "https://allenai.org/data/tuple-ie", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "tuples": {"feature": {"score": {"dtype": "float32", "id": null, "_type": "Value"}, "tuple_text": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "arg1": {"dtype": "string", "id": null, "_type": "Value"}, "rel": {"dtype": "string", "id": null, "_type": "Value"}, "arg2s": {"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": "tuple_ie", "config_name": "4th_grade", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 65363445, "num_examples": 158910, "dataset_name": "tuple_ie"}}, "download_checksums": {"https://ai2-datasets.s3-us-west-2.amazonaws.com/tuple-ie/TupleInfKB.zip": {"num_bytes": 18026102, "checksum": "5ae98009e45ef2d29570d9c6c4575ff5339ddd019f9524d8b5c62b8a50aba56d"}}, "download_size": 18026102, "post_processing_size": null, "dataset_size": 65363445, "size_in_bytes": 83389547}, "8th_grade": {"description": "The TupleInf Open IE dataset contains Open IE tuples extracted from 263K sentences that were used by the solver in \u201cAnswering Complex Questions Using Open Information Extraction\u201d (referred as Tuple KB, T). These sentences were collected from a large Web corpus using training questions from 4th and 8th grade as queries. This dataset contains 156K sentences collected for 4th grade questions and 107K sentences for 8th grade questions. Each sentence is followed by the Open IE v4 tuples using their simple format.\n", "citation": "@article{Khot2017AnsweringCQ,\n title={Answering Complex Questions Using Open Information Extraction},\n author={Tushar Khot and A. Sabharwal and Peter Clark},\n journal={ArXiv},\n year={2017},\n volume={abs/1704.05572}\n}\n", "homepage": "https://allenai.org/data/tuple-ie", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "tuples": {"feature": {"score": {"dtype": "float32", "id": null, "_type": "Value"}, "tuple_text": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "arg1": {"dtype": "string", "id": null, "_type": "Value"}, "rel": {"dtype": "string", "id": null, "_type": "Value"}, "arg2s": {"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": "tuple_ie", "config_name": "8th_grade", "version": "0.0.0", "splits": {"train": {"name": "train", "num_bytes": 50257651, "num_examples": 108809, "dataset_name": "tuple_ie"}}, "download_checksums": {"https://ai2-datasets.s3-us-west-2.amazonaws.com/tuple-ie/TupleInfKB.zip": {"num_bytes": 18026102, "checksum": "5ae98009e45ef2d29570d9c6c4575ff5339ddd019f9524d8b5c62b8a50aba56d"}}, "download_size": 18026102, "post_processing_size": null, "dataset_size": 50257651, "size_in_bytes": 68283753}}
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tuple_ie.py ADDED
@@ -0,0 +1,161 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ """TupleInf Open IE Dataset"""
16
+
17
+ from __future__ import absolute_import, division, print_function
18
+
19
+ import os
20
+
21
+ import datasets
22
+
23
+
24
+ _CITATION = """\
25
+ @article{Khot2017AnsweringCQ,
26
+ title={Answering Complex Questions Using Open Information Extraction},
27
+ author={Tushar Khot and A. Sabharwal and Peter Clark},
28
+ journal={ArXiv},
29
+ year={2017},
30
+ volume={abs/1704.05572}
31
+ }
32
+ """
33
+
34
+ _DESCRIPTION = """\
35
+ The TupleInf Open IE dataset contains Open IE tuples extracted from 263K sentences that were used by the solver \
36
+ in “Answering Complex Questions Using Open Information Extraction” (referred as Tuple KB, T). \
37
+ These sentences were collected from a large Web corpus using training questions from 4th and 8th grade as queries. \
38
+ This dataset contains 156K sentences collected for 4th grade questions and 107K sentences for 8th grade questions. \
39
+ Each sentence is followed by the Open IE v4 tuples using their simple format.
40
+ """
41
+
42
+ _HOMEPAGE = "https://allenai.org/data/tuple-ie"
43
+
44
+ _URL = "https://ai2-datasets.s3-us-west-2.amazonaws.com/tuple-ie/TupleInfKB.zip"
45
+
46
+ _DOMAIN_FILES = {"4th_grade": "4thGradeOpenIE.txt", "8th_grade": "8thGradeOpenIE.txt"}
47
+
48
+
49
+ class TupleIEConfig(datasets.BuilderConfig):
50
+ """BuilderConfig for TupleIE"""
51
+
52
+ def __init__(self, *args, domains=None, **kwargs):
53
+ super().__init__(*args, **kwargs)
54
+ self.domains = domains
55
+
56
+
57
+ class TupleIE(datasets.GeneratorBasedBuilder):
58
+ """TupleInf Open IE Dataset"""
59
+
60
+ BUILDER_CONFIGS = [
61
+ TupleIEConfig(
62
+ name="all",
63
+ domains=list(_DOMAIN_FILES.keys()),
64
+ description="collected using training questions from 4th and 8th grade as queries.",
65
+ )
66
+ ] + [
67
+ TupleIEConfig(
68
+ name=name, domains=[name], description=f"collected using training questions from {name} as queries."
69
+ )
70
+ for name in _DOMAIN_FILES.keys()
71
+ ]
72
+ BUILDER_CONFIG_CLASS = TupleIEConfig
73
+ DEFAULT_CONFIG_NAME = "all"
74
+
75
+ def _info(self):
76
+ return datasets.DatasetInfo(
77
+ description=_DESCRIPTION,
78
+ features=datasets.Features(
79
+ {
80
+ "sentence": datasets.Value("string"),
81
+ "tuples": datasets.features.Sequence(
82
+ {
83
+ "score": datasets.Value("float"),
84
+ "tuple_text": datasets.Value("string"),
85
+ "context": datasets.Value("string"),
86
+ "arg1": datasets.Value("string"),
87
+ "rel": datasets.Value("string"),
88
+ "arg2s": datasets.features.Sequence(datasets.Value("string")),
89
+ }
90
+ ),
91
+ }
92
+ ),
93
+ supervised_keys=None,
94
+ homepage=_HOMEPAGE,
95
+ citation=_CITATION,
96
+ )
97
+
98
+ def _split_generators(self, dl_manager):
99
+ """Returns SplitGenerators."""
100
+ data_dir = os.path.join(dl_manager.download_and_extract(_URL), "TupleInfKB")
101
+ return [
102
+ datasets.SplitGenerator(
103
+ name=datasets.Split.TRAIN,
104
+ gen_kwargs={"data_dir": data_dir},
105
+ )
106
+ ]
107
+
108
+ def _generate_examples(self, data_dir):
109
+ """ Yields examples. """
110
+ id_ = -1
111
+ for domain in self.config.domains:
112
+ with open(os.path.join(data_dir, _DOMAIN_FILES[domain]), encoding="utf-8") as f:
113
+ all_text = f.read()
114
+ samples = all_text.split("\n\n")
115
+ for sample in samples:
116
+ rows = sample.split("\n")
117
+ item = {"sentence": rows[0], "tuples": []}
118
+ tuple_lines = rows[1:]
119
+ for tuple_line in tuple_lines:
120
+ score, tuple_text = tuple_line.split(" ", 1)
121
+ context, arg1, rel, arg2s = self._decode_tuple_text(tuple_text)
122
+ item["tuples"].append(
123
+ {
124
+ "score": score,
125
+ "tuple_text": tuple_text,
126
+ "context": context,
127
+ "arg1": arg1,
128
+ "rel": rel,
129
+ "arg2s": arg2s,
130
+ }
131
+ )
132
+ id_ += 1
133
+ yield id_, item
134
+
135
+ def _decode_tuple_text(self, tuple_text):
136
+ """Decompose the tuple text into arguments and relations
137
+
138
+ Args:
139
+ tuple_text (str): Format of extraction text:
140
+ .. code-block::
141
+ {Context(<context>):}(<arg1>; <rel>; {[L|T]:}<arg2_1>; {[L|T]:}<arg2_2>; ...)
142
+
143
+ .. note::
144
+ * ``{}`` means one can be optionally appear
145
+ * ``[L|T]`` means ``L`` or ``T``
146
+ * ``L`` means spatial/location argument
147
+ * ``T`` means temporal argument
148
+ * We can have multiple arg2s
149
+ """
150
+ context = ""
151
+ arg1 = ""
152
+ rel = ""
153
+ arg2s = []
154
+ if tuple_text.startswith("Context("):
155
+ context, tuple_text = tuple_text.split(":", 1)
156
+ context = context[len("Context(") : -1]
157
+
158
+ args = tuple_text[1:-1].split("; ")
159
+ arg1, rel = args[:2]
160
+ arg2s = args[2:]
161
+ return context, arg1, rel, arg2s