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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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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bin.* filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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1
+ ---
2
+ annotations_creators:
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+ - crowdsourced
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+ language_creators:
5
+ - crowdsourced
6
+ languages:
7
+ - en
8
+ licenses:
9
+ - cc-by-4-0
10
+ multilinguality:
11
+ - monolingual
12
+ size_categories:
13
+ - 100K<n<1M
14
+ source_datasets:
15
+ - original
16
+ task_categories:
17
+ - text-classification
18
+ task_ids:
19
+ - fact-checking
20
+ ---
21
+
22
+ # Dataset Card Creation Guide
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:** [TabFact](https://tabfact.github.io/index.html)
50
+ - **Repository:** [GitHub](https://github.com/wenhuchen/Table-Fact-Checking)
51
+ - **Paper:** [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://arxiv.org/abs/1909.02164)
52
+ - **Leaderboard:** [Leaderboard](https://competitions.codalab.org/competitions/21611)
53
+ - **Point of Contact:** [Wenhu Chen](wenhuchen@cs.ucsb.edu)
54
+
55
+ ### Dataset Summary
56
+
57
+ The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are restricted to dealing with unstructured textual evidence (e.g., sentences and passages, a pool of passages), while verification using structured forms of evidence, such as tables, graphs, and databases, remains unexplored. TABFACT is large scale dataset with 16k Wikipedia tables as evidence for 118k human annotated statements designed for fact verification with semi-structured evidence. The statements are labeled as either ENTAILED or REFUTED. TABFACT is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning.
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
+ [More Information Needed]
72
+
73
+ ### Data Fields
74
+
75
+ [More Information Needed]
76
+
77
+ ### Data Splits
78
+
79
+ [More Information Needed]
80
+ ## Dataset Creation
81
+
82
+ ### Curation Rationale
83
+
84
+ [More Information Needed]
85
+
86
+ ### Source Data
87
+
88
+ [More Information Needed]
89
+
90
+ #### Initial Data Collection and Normalization
91
+
92
+ [More Information Needed]
93
+
94
+ #### Who are the source language producers?
95
+
96
+ [More Information Needed]
97
+
98
+ ### Annotations
99
+
100
+ [More Information Needed]
101
+
102
+ #### Annotation process
103
+
104
+ [More Information Needed]
105
+
106
+ #### Who are the annotators?
107
+
108
+ [More Information Needed]
109
+
110
+ ### Personal and Sensitive Information
111
+
112
+ [More Information Needed]
113
+
114
+ ## Considerations for Using the Data
115
+
116
+ ### Social Impact of Dataset
117
+
118
+ [More Information Needed]
119
+
120
+ ### Discussion of Biases
121
+
122
+ [More Information Needed]
123
+
124
+ ### Other Known Limitations
125
+
126
+ [More Information Needed]
127
+
128
+ ## Additional Information
129
+
130
+ ### Dataset Curators
131
+
132
+ [More Information Needed]
133
+
134
+ ### Licensing Information
135
+
136
+ [More Information Needed]
137
+
138
+ ### Citation Information
139
+
140
+ ```
141
+ @inproceedings{2019TabFactA,
142
+ title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
143
+ author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
144
+ booktitle = {International Conference on Learning Representations (ICLR)},
145
+ address = {Addis Ababa, Ethiopia},
146
+ month = {April},
147
+ year = {2020}
148
+ }
149
+ ```
dataset_infos.json ADDED
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+ {"tab_fact": {"description": "The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are restricted to dealing with unstructured textual evidence (e.g., sentences and passages, a pool of passages), while verification using structured forms of evidence, such as tables, graphs, and databases, remains unexplored. TABFACT is large scale dataset with 16k Wikipedia tables as evidence for 118k human annotated statements designed for fact verification with semi-structured evidence. The statements are labeled as either ENTAILED or REFUTED. TABFACT is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning.\n", "citation": "@inproceedings{2019TabFactA,\n title={TabFact : A Large-scale Dataset for Table-based Fact Verification},\n author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},\n booktitle = {International Conference on Learning Representations (ICLR)},\n address = {Addis Ababa, Ethiopia},\n month = {April},\n year = {2020}\n}\n", "homepage": "https://tabfact.github.io/", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "table_id": {"dtype": "string", "id": null, "_type": "Value"}, "table_text": {"dtype": "string", "id": null, "_type": "Value"}, "table_caption": {"dtype": "string", "id": null, "_type": "Value"}, "statement": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["refuted", "entailed"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "tab_fact", "config_name": "tab_fact", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 99852664, "num_examples": 92283, "dataset_name": "tab_fact"}, "validation": {"name": "validation", "num_bytes": 13846872, "num_examples": 12792, "dataset_name": "tab_fact"}, "test": {"name": "test", "num_bytes": 13493391, "num_examples": 12779, "dataset_name": "tab_fact"}}, "download_checksums": {"https://github.com/wenhuchen/Table-Fact-Checking/archive/948b5560e2f7f8c9139bd91c7f093346a2bb56a8.zip": {"num_bytes": 196508436, "checksum": "4f0bffb6e53b59760173dac82979a0e5272c2d97514659ac3f4b44c7a008df4a"}}, "download_size": 196508436, "post_processing_size": null, "dataset_size": 127192927, "size_in_bytes": 323701363}, "blind_test": {"description": "The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are restricted to dealing with unstructured textual evidence (e.g., sentences and passages, a pool of passages), while verification using structured forms of evidence, such as tables, graphs, and databases, remains unexplored. TABFACT is large scale dataset with 16k Wikipedia tables as evidence for 118k human annotated statements designed for fact verification with semi-structured evidence. The statements are labeled as either ENTAILED or REFUTED. TABFACT is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning.\n", "citation": "@inproceedings{2019TabFactA,\n title={TabFact : A Large-scale Dataset for Table-based Fact Verification},\n author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},\n booktitle = {International Conference on Learning Representations (ICLR)},\n address = {Addis Ababa, Ethiopia},\n month = {April},\n year = {2020}\n}\n", "homepage": "https://tabfact.github.io/", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "table_id": {"dtype": "string", "id": null, "_type": "Value"}, "table_text": {"dtype": "string", "id": null, "_type": "Value"}, "table_caption": {"dtype": "string", "id": null, "_type": "Value"}, "statement": {"dtype": "string", "id": null, "_type": "Value"}, "test_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "tab_fact", "config_name": "blind_test", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 10954442, "num_examples": 9750, "dataset_name": "tab_fact"}}, "download_checksums": {"https://github.com/wenhuchen/Table-Fact-Checking/archive/948b5560e2f7f8c9139bd91c7f093346a2bb56a8.zip": {"num_bytes": 196508436, "checksum": "4f0bffb6e53b59760173dac82979a0e5272c2d97514659ac3f4b44c7a008df4a"}}, "download_size": 196508436, "post_processing_size": null, "dataset_size": 10954442, "size_in_bytes": 207462878}}
dummy/blind_test/1.0.0/dummy_data.zip ADDED
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+ size 8567
dummy/tab_fact/1.0.0/dummy_data.zip ADDED
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tab_fact.py ADDED
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1
+ # coding=utf-8
2
+ # Copyright 2020 The HuggingFace Datasets Authors.
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
+ """TabFact: A Large-scale Dataset for Table-based Fact Verification"""
16
+
17
+ from __future__ import absolute_import, division, print_function
18
+
19
+ import json
20
+ import os
21
+
22
+ import datasets
23
+
24
+
25
+ _CITATION = """\
26
+ @inproceedings{2019TabFactA,
27
+ title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
28
+ author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
29
+ booktitle = {International Conference on Learning Representations (ICLR)},
30
+ address = {Addis Ababa, Ethiopia},
31
+ month = {April},
32
+ year = {2020}
33
+ }
34
+ """
35
+
36
+ _DESCRIPTION = """\
37
+ The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, \
38
+ also known as fact verification, plays an important role in the study of natural language \
39
+ understanding and semantic representation. However, existing studies are restricted to \
40
+ dealing with unstructured textual evidence (e.g., sentences and passages, a pool of passages), \
41
+ while verification using structured forms of evidence, such as tables, graphs, and databases, remains unexplored. \
42
+ TABFACT is large scale dataset with 16k Wikipedia tables as evidence for 118k human annotated statements \
43
+ designed for fact verification with semi-structured evidence. \
44
+ The statements are labeled as either ENTAILED or REFUTED. \
45
+ TABFACT is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning.
46
+ """
47
+
48
+ _HOMEPAGE = "https://tabfact.github.io/"
49
+
50
+ _GIT_ARCHIVE_URL = (
51
+ "https://github.com/wenhuchen/Table-Fact-Checking/archive/948b5560e2f7f8c9139bd91c7f093346a2bb56a8.zip"
52
+ )
53
+
54
+
55
+ class TabFact(datasets.GeneratorBasedBuilder):
56
+ """TabFact: A Large-scale Dataset for Table-based Fact Verification"""
57
+
58
+ VERSION = datasets.Version("1.0.0")
59
+ BUILDER_CONFIGS = [
60
+ datasets.BuilderConfig(
61
+ name="tab_fact",
62
+ version=datasets.Version("1.0.0"),
63
+ ),
64
+ datasets.BuilderConfig(
65
+ name="blind_test",
66
+ version=datasets.Version("1.0.0"),
67
+ description="Blind test dataset",
68
+ ),
69
+ ]
70
+
71
+ def _info(self):
72
+ features = {
73
+ "id": datasets.Value("int32"),
74
+ "table_id": datasets.Value("string"),
75
+ "table_text": datasets.Value("string"),
76
+ "table_caption": datasets.Value("string"),
77
+ "statement": datasets.Value("string"),
78
+ }
79
+ if self.config.name == "tab_fact":
80
+ features["label"] = datasets.ClassLabel(names=["refuted", "entailed"])
81
+ else:
82
+ features["test_id"] = datasets.Value("string")
83
+
84
+ return datasets.DatasetInfo(
85
+ description=_DESCRIPTION,
86
+ features=datasets.Features(features),
87
+ supervised_keys=None,
88
+ homepage=_HOMEPAGE,
89
+ citation=_CITATION,
90
+ )
91
+
92
+ def _split_generators(self, dl_manager):
93
+ extracted_path = dl_manager.download_and_extract(_GIT_ARCHIVE_URL)
94
+
95
+ repo_path = os.path.join(extracted_path, "Table-Fact-Checking-948b5560e2f7f8c9139bd91c7f093346a2bb56a8")
96
+ all_csv_path = os.path.join(repo_path, "data", "all_csv")
97
+
98
+ if self.config.name == "blind_test":
99
+ test_file_path = os.path.join(repo_path, "challenge", "blind_test.json")
100
+ return [
101
+ datasets.SplitGenerator(
102
+ name=datasets.Split.TEST,
103
+ gen_kwargs={"statements_file": test_file_path, "all_csv_path": all_csv_path},
104
+ ),
105
+ ]
106
+
107
+ train_statements_file = os.path.join(repo_path, "tokenized_data", "train_examples.json")
108
+ val_statements_file = os.path.join(repo_path, "tokenized_data", "val_examples.json")
109
+ test_statements_file = os.path.join(repo_path, "tokenized_data", "test_examples.json")
110
+
111
+ return [
112
+ datasets.SplitGenerator(
113
+ name=datasets.Split.TRAIN,
114
+ gen_kwargs={"statements_file": train_statements_file, "all_csv_path": all_csv_path},
115
+ ),
116
+ datasets.SplitGenerator(
117
+ name=datasets.Split.VALIDATION,
118
+ gen_kwargs={"statements_file": val_statements_file, "all_csv_path": all_csv_path},
119
+ ),
120
+ datasets.SplitGenerator(
121
+ name=datasets.Split.TEST,
122
+ gen_kwargs={"statements_file": test_statements_file, "all_csv_path": all_csv_path},
123
+ ),
124
+ ]
125
+
126
+ def _generate_examples(self, statements_file, all_csv_path):
127
+ with open(statements_file, encoding="utf-8") as f:
128
+ examples = json.load(f)
129
+
130
+ if self.config.name == "blind_test":
131
+ test_examples = self._generate_blind_test_examples(examples, all_csv_path)
132
+ for idx, example in test_examples:
133
+ yield idx, example
134
+ else:
135
+ for i, (table_id, example) in enumerate(examples.items()):
136
+ table_file_path = os.path.join(all_csv_path, table_id)
137
+ with open(table_file_path, encoding="utf-8") as f:
138
+ tabel_text = f.read()
139
+
140
+ statements, labels, caption = example
141
+
142
+ for statement, label in zip(statements, labels):
143
+ yield i, {
144
+ "id": i,
145
+ "table_id": table_id,
146
+ "table_text": tabel_text,
147
+ "table_caption": caption,
148
+ "statement": statement,
149
+ "label": label,
150
+ }
151
+
152
+ def _generate_blind_test_examples(self, examples, all_csv_path):
153
+ for i, (test_id, example) in enumerate(examples.items()):
154
+ statement, table_id, caption = example
155
+ table_file_path = os.path.join(all_csv_path, table_id)
156
+ with open(table_file_path, encoding="utf-8") as f:
157
+ tabel_text = f.read()
158
+
159
+ yield i, {
160
+ "id": i,
161
+ "test_id": test_id,
162
+ "table_id": table_id,
163
+ "table_text": tabel_text,
164
+ "table_caption": caption,
165
+ "statement": statement,
166
+ }