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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
+ - crowdsourced
4
+ language_creators:
5
+ - crowdsourced
6
+ languages:
7
+ - en
8
+ licenses:
9
+ - cc-by-4-0
10
+ multilinguality:
11
+ - monolingual
12
+ size_categories:
13
+ - 1K<n<10K
14
+ source_datasets:
15
+ - extended|other-xsum
16
+ task_categories:
17
+ - conditional-text-generation
18
+ task_ids:
19
+ - summarization
20
+ ---
21
+
22
+ # Dataset Card for XSum Hallucination Annotations
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://research.google/tools/datasets/xsum-hallucination-annotations/
50
+ - **Repository:** https://github.com/google-research-datasets/xsum_hallucination_annotations
51
+ - **Paper:** https://www.aclweb.org/anthology/2020.acl-main.173.pdf
52
+ - **Leaderboard:** NA
53
+ - **Point of Contact:** [[email protected]](mailto:[email protected])
54
+
55
+ ### Dataset Summary
56
+
57
+ Neural abstractive summarization models are highly prone to hallucinate content that is unfaithful to the input document. The popular metric such as ROUGE fails to show the severity of the problem. The dataset consists of faithfulness and factuality annotations of abstractive summaries for the XSum dataset. The dataset has crowdsourced 3 judgements for each of 500 x 5 document-system pairs. This will be a valuable resource to the abstractive summarization community.
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
+ ##### Faithfulness annotations dataset
72
+
73
+ ```
74
+ {
75
+ 'bbcid': 34687720,
76
+ 'hallucinated_span_end': 114,
77
+ 'hallucinated_span_start': 1,
78
+ 'hallucination_type': 1,
79
+ 'summary': 'rory mcilroy will take a one-shot lead into the final round of the wgc-hsbc champions after carding a three-under',
80
+ 'system': 'BERTS2S',
81
+ 'worker_id': 'wid_0'
82
+ }
83
+ ```
84
+
85
+ ##### Factuality annotations dataset
86
+
87
+ ```
88
+ {
89
+ 'bbcid': 29911712,
90
+ 'is_factual': 0,
91
+ 'summary': 'more than 50 pupils at a bristol academy have been sent home from school because of a lack of uniform.',
92
+ 'system': 'BERTS2S',
93
+ 'worker_id': 'wid_0'
94
+ }
95
+ ```
96
+
97
+ ### Data Fields
98
+
99
+ ##### Faithfulness annotations dataset
100
+
101
+ Raters are shown the news article and the system summary, and are tasked with identifying and annotating the spans that aren't supported by the input article. The file contains the following columns:
102
+
103
+
104
+ - bbcid: Document id in the XSum corpus.
105
+ - system: Name of neural summarizer.
106
+ - summary: Summary generated by ‘system’.
107
+ - hallucination_type: Type of hallucination: intrinsic (0) or extrinsic (1)
108
+ - hallucinated_span: Hallucinated span in the ‘summary’.
109
+ - hallucinated_span_start: Index of the start of the hallucinated span.
110
+ - hallucinated_span_end: Index of the end of the hallucinated span.
111
+ - worker_id: 'wid_0', 'wid_1', 'wid_2'
112
+
113
+
114
+ The `hallucination_type` column has NULL value for some entries which have been replaced iwth `-1`.
115
+
116
+ ##### Factuality annotations dataset
117
+
118
+ Raters are shown the news article and the hallucinated system summary, and are tasked with assessing the summary whether it is factual or not. The file contains the following columns:
119
+
120
+
121
+ - bbcid: Document id in the XSum corpus.
122
+ - system: Name of neural summarizer.
123
+ - summary: Summary generated by ‘system’.
124
+ - is_factual: yes (1) or no (0)
125
+ - worker_id: 'wid_0', 'wid_1', 'wid_2'
126
+
127
+
128
+ The `is_factual` column has NULL value for some entries which have been replaced iwth `-1`.
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
+ [More Information Needed]
183
+
184
+ ### Licensing Information
185
+
186
+ [More Information Needed]
187
+
188
+ ### Citation Information
189
+
190
+ [More Information Needed]
dataset_infos.json ADDED
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1
+ {"xsum_factuality": {"description": "Neural abstractive summarization models are highly prone to hallucinate content that is unfaithful to the input\ndocument. The popular metric such as ROUGE fails to show the severity of the problem. The dataset consists of\nfaithfulness and factuality annotations of abstractive summaries for the XSum dataset. We have crowdsourced 3 judgements\n for each of 500 x 5 document-system pairs. This will be a valuable resource to the abstractive summarization community.\n", "citation": "@InProceedings{maynez_acl20,\n author = \"Joshua Maynez and Shashi Narayan and Bernd Bohnet and Ryan Thomas Mcdonald\",\n title = \"On Faithfulness and Factuality in Abstractive Summarization\",\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n year = \"2020\",\n pages = \"1906--1919\",\n address = \"Online\",\n}\n", "homepage": "https://research.google/tools/datasets/xsum-hallucination-annotations/", "license": "https://creativecommons.org/licenses/by/4.0/", "features": {"bbcid": {"dtype": "int32", "id": null, "_type": "Value"}, "system": {"dtype": "string", "id": null, "_type": "Value"}, "summary": {"dtype": "string", "id": null, "_type": "Value"}, "is_factual": {"num_classes": 2, "names": ["no", "yes"], "names_file": null, "id": null, "_type": "ClassLabel"}, "worker_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "xsum_factuality", "config_name": "xsum_factuality", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 800027, "num_examples": 5597, "dataset_name": "xsum_factuality"}}, "download_checksums": {"https://raw.githubusercontent.com/google-research-datasets/xsum_hallucination_annotations/master/factuality_annotations_xsum_summaries.csv": {"num_bytes": 759614, "checksum": "f0ace0a9b52cacaa632ded3d07a355b7991383ce28fdd9fcbbf08a8523695ecb"}, "https://raw.githubusercontent.com/google-research-datasets/xsum_hallucination_annotations/master/hallucination_annotations_xsum_summaries.csv": {"num_bytes": 2105145, "checksum": "fa7fb66a36cc0f32ede4135985d0d65591dc2a8d21103a0bacd0583d77d4c8ea"}}, "download_size": 2864759, "post_processing_size": null, "dataset_size": 800027, "size_in_bytes": 3664786}, "xsum_faithfulness": {"description": "Neural abstractive summarization models are highly prone to hallucinate content that is unfaithful to the input\ndocument. The popular metric such as ROUGE fails to show the severity of the problem. The dataset consists of\nfaithfulness and factuality annotations of abstractive summaries for the XSum dataset. We have crowdsourced 3 judgements\n for each of 500 x 5 document-system pairs. This will be a valuable resource to the abstractive summarization community.\n", "citation": "@InProceedings{maynez_acl20,\n author = \"Joshua Maynez and Shashi Narayan and Bernd Bohnet and Ryan Thomas Mcdonald\",\n title = \"On Faithfulness and Factuality in Abstractive Summarization\",\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n year = \"2020\",\n pages = \"1906--1919\",\n address = \"Online\",\n}\n", "homepage": "https://research.google/tools/datasets/xsum-hallucination-annotations/", "license": "https://creativecommons.org/licenses/by/4.0/", "features": {"bbcid": {"dtype": "int32", "id": null, "_type": "Value"}, "system": {"dtype": "string", "id": null, "_type": "Value"}, "summary": {"dtype": "string", "id": null, "_type": "Value"}, "hallucination_type": {"num_classes": 2, "names": ["intrinsic", "extrinsic"], "names_file": null, "id": null, "_type": "ClassLabel"}, "hallucinated_span_start": {"dtype": "int32", "id": null, "_type": "Value"}, "hallucinated_span_end": {"dtype": "int32", "id": null, "_type": "Value"}, "worker_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "xsum_factuality", "config_name": "xsum_faithfulness", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1750325, "num_examples": 11185, "dataset_name": "xsum_factuality"}}, "download_checksums": {"https://raw.githubusercontent.com/google-research-datasets/xsum_hallucination_annotations/master/factuality_annotations_xsum_summaries.csv": {"num_bytes": 759614, "checksum": "f0ace0a9b52cacaa632ded3d07a355b7991383ce28fdd9fcbbf08a8523695ecb"}, "https://raw.githubusercontent.com/google-research-datasets/xsum_hallucination_annotations/master/hallucination_annotations_xsum_summaries.csv": {"num_bytes": 2105145, "checksum": "fa7fb66a36cc0f32ede4135985d0d65591dc2a8d21103a0bacd0583d77d4c8ea"}}, "download_size": 2864759, "post_processing_size": null, "dataset_size": 1750325, "size_in_bytes": 4615084}}
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xsum_factuality.py ADDED
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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
+ """XSum Hallucination Annotations: Faithfulness and factuality annotations of XSum summaries"""
16
+
17
+ from __future__ import absolute_import, division, print_function
18
+
19
+ import csv
20
+ import os
21
+
22
+ import datasets
23
+
24
+
25
+ _CITATION = """\
26
+ @InProceedings{maynez_acl20,
27
+ author = "Joshua Maynez and Shashi Narayan and Bernd Bohnet and Ryan Thomas Mcdonald",
28
+ title = "On Faithfulness and Factuality in Abstractive Summarization",
29
+ booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
30
+ year = "2020",
31
+ pages = "1906--1919",
32
+ address = "Online",
33
+ }
34
+ """
35
+
36
+ _DESCRIPTION = """\
37
+ Neural abstractive summarization models are highly prone to hallucinate content that is unfaithful to the input
38
+ document. The popular metric such as ROUGE fails to show the severity of the problem. The dataset consists of
39
+ faithfulness and factuality annotations of abstractive summaries for the XSum dataset. We have crowdsourced 3 judgements
40
+ for each of 500 x 5 document-system pairs. This will be a valuable resource to the abstractive summarization community.
41
+ """
42
+
43
+ _HOMEPAGE = "https://research.google/tools/datasets/xsum-hallucination-annotations/"
44
+
45
+ _LICENSE = "https://creativecommons.org/licenses/by/4.0/"
46
+
47
+ _URL = "https://raw.githubusercontent.com/google-research-datasets/xsum_hallucination_annotations/master/"
48
+ _URLs = {
49
+ "factuality": _URL + "factuality_annotations_xsum_summaries.csv",
50
+ "hallucination": _URL + "hallucination_annotations_xsum_summaries.csv",
51
+ }
52
+
53
+
54
+ class XsumFactualityConfig(datasets.BuilderConfig):
55
+ """BuilderConfig for XsumFactuality"""
56
+
57
+ def __init__(self, **kwargs):
58
+ """BuilderConfig for XsumFactuality.
59
+ Args:
60
+ **kwargs: keyword arguments forwarded to super.
61
+ """
62
+ super(XsumFactualityConfig, self).__init__(**kwargs)
63
+
64
+
65
+ class XsumFactuality(datasets.GeneratorBasedBuilder):
66
+ """XSum Hallucination Annotations: Faithfulness and factuality annotations of XSum summaries"""
67
+
68
+ VERSION = datasets.Version("1.1.0")
69
+
70
+ BUILDER_CONFIGS = [
71
+ XsumFactualityConfig(
72
+ name="xsum_factuality",
73
+ version=datasets.Version("1.1.0"),
74
+ description="Raters are shown the news article and the system summary, and are tasked with "
75
+ "identifying and annotating the spans that aren't supported by the input article.",
76
+ ),
77
+ XsumFactualityConfig(
78
+ name="xsum_faithfulness",
79
+ version=datasets.Version("1.1.0"),
80
+ description="Raters are shown the news article and the hallucinated system summary, and are "
81
+ "tasked with assessing the summary whether it is factual or not.",
82
+ ),
83
+ ]
84
+
85
+ DEFAULT_CONFIG_NAME = "xsum_factuality"
86
+
87
+ def _info(self):
88
+ if self.config.name == "xsum_factuality":
89
+ features = datasets.Features(
90
+ {
91
+ "bbcid": datasets.Value("int32"),
92
+ "system": datasets.Value("string"),
93
+ "summary": datasets.Value("string"),
94
+ "is_factual": datasets.ClassLabel(names=["no", "yes"]),
95
+ "worker_id": datasets.Value("string"),
96
+ }
97
+ )
98
+ else:
99
+ features = datasets.Features(
100
+ {
101
+ "bbcid": datasets.Value("int32"),
102
+ "system": datasets.Value("string"),
103
+ "summary": datasets.Value("string"),
104
+ "hallucination_type": datasets.ClassLabel(names=["intrinsic", "extrinsic"]),
105
+ "hallucinated_span_start": datasets.Value("int32"),
106
+ "hallucinated_span_end": datasets.Value("int32"),
107
+ "worker_id": datasets.Value("string"),
108
+ }
109
+ )
110
+
111
+ return datasets.DatasetInfo(
112
+ description=_DESCRIPTION,
113
+ features=features,
114
+ supervised_keys=None,
115
+ homepage=_HOMEPAGE,
116
+ license=_LICENSE,
117
+ citation=_CITATION,
118
+ )
119
+
120
+ def _split_generators(self, dl_manager):
121
+ """Returns SplitGenerators."""
122
+
123
+ data_dir = dl_manager.download_and_extract(_URLs)
124
+ if self.config.name == "xsum_factuality":
125
+ return [
126
+ datasets.SplitGenerator(
127
+ name=datasets.Split.TRAIN,
128
+ gen_kwargs={
129
+ "filepath": os.path.join(data_dir["factuality"]),
130
+ "split": "factuality",
131
+ },
132
+ ),
133
+ ]
134
+ else:
135
+ return [
136
+ datasets.SplitGenerator(
137
+ name=datasets.Split.TRAIN,
138
+ gen_kwargs={
139
+ "filepath": os.path.join(data_dir["hallucination"]),
140
+ "split": "hallucination",
141
+ },
142
+ ),
143
+ ]
144
+
145
+ def _generate_examples(self, filepath, split):
146
+ """ Yields examples. """
147
+
148
+ with open(filepath, encoding="utf-8") as f:
149
+ f_csv = csv.reader(f, delimiter=",", quotechar='"')
150
+
151
+ next(f_csv)
152
+ for id_, data in enumerate(f_csv):
153
+
154
+ if self.config.name == "xsum_factuality":
155
+ bbcid, system, summary, is_factual, worker_id = data
156
+
157
+ is_factual = -1 if is_factual == "NULL" else is_factual
158
+
159
+ yield id_, {
160
+ "bbcid": bbcid,
161
+ "system": system,
162
+ "summary": summary,
163
+ "is_factual": is_factual,
164
+ "worker_id": worker_id,
165
+ }
166
+ else:
167
+ (
168
+ bbcid,
169
+ system,
170
+ summary,
171
+ hallucination_type,
172
+ hallucinated_span,
173
+ hallucinated_span_start,
174
+ hallucinated_span_end,
175
+ worker_id,
176
+ ) = data
177
+
178
+ hallucination_type = -1 if hallucination_type == "NULL" else hallucination_type
179
+
180
+ yield id_, {
181
+ "bbcid": bbcid,
182
+ "system": system,
183
+ "summary": summary,
184
+ "hallucination_type": hallucination_type,
185
+ "hallucinated_span_start": hallucinated_span_start,
186
+ "hallucinated_span_end": hallucinated_span_end,
187
+ "worker_id": worker_id,
188
+ }