system
HF staff
commited on
Commit
b3bdfeb
0 Parent(s):

Update files from the datasets library (from 1.2.0)

Browse files

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ *.7z filter=lfs diff=lfs merge=lfs -text
2
+ *.arrow filter=lfs diff=lfs merge=lfs -text
3
+ *.bin filter=lfs diff=lfs merge=lfs -text
4
+ *.bin.* filter=lfs diff=lfs merge=lfs -text
5
+ *.bz2 filter=lfs diff=lfs merge=lfs -text
6
+ *.ftz filter=lfs diff=lfs merge=lfs -text
7
+ *.gz filter=lfs diff=lfs merge=lfs -text
8
+ *.h5 filter=lfs diff=lfs merge=lfs -text
9
+ *.joblib filter=lfs diff=lfs merge=lfs -text
10
+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
11
+ *.model filter=lfs diff=lfs merge=lfs -text
12
+ *.msgpack filter=lfs diff=lfs merge=lfs -text
13
+ *.onnx filter=lfs diff=lfs merge=lfs -text
14
+ *.ot filter=lfs diff=lfs merge=lfs -text
15
+ *.parquet filter=lfs diff=lfs merge=lfs -text
16
+ *.pb filter=lfs diff=lfs merge=lfs -text
17
+ *.pt filter=lfs diff=lfs merge=lfs -text
18
+ *.pth filter=lfs diff=lfs merge=lfs -text
19
+ *.rar filter=lfs diff=lfs merge=lfs -text
20
+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
21
+ *.tar.* filter=lfs diff=lfs merge=lfs -text
22
+ *.tflite filter=lfs diff=lfs merge=lfs -text
23
+ *.tgz filter=lfs diff=lfs merge=lfs -text
24
+ *.xz filter=lfs diff=lfs merge=lfs -text
25
+ *.zip filter=lfs diff=lfs merge=lfs -text
26
+ *.zstandard filter=lfs diff=lfs merge=lfs -text
27
+ *tfevents* filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,173 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ annotations_creators:
3
+ - expert-generated
4
+ language_creators:
5
+ - found
6
+ languages:
7
+ - en
8
+ licenses:
9
+ - apache-2-0
10
+ multilinguality:
11
+ - monolingual
12
+ size_categories:
13
+ - 1K<n<10K
14
+ source_datasets:
15
+ - original
16
+ task_categories:
17
+ - question-answering
18
+ task_ids:
19
+ - open-domain-qa
20
+ - extractive-qa
21
+ ---
22
+
23
+
24
+ # Dataset Card for [covid_qa_castorini]
25
+
26
+ ## Table of Contents
27
+ - [Dataset Description](#dataset-description)
28
+ - [Dataset Summary](#dataset-summary)
29
+ - [Supported Tasks](#supported-tasks-and-leaderboards)
30
+ - [Languages](#languages)
31
+ - [Dataset Structure](#dataset-structure)
32
+ - [Data Instances](#data-instances)
33
+ - [Data Fields](#data-instances)
34
+ - [Data Splits](#data-instances)
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
+
49
+ ## Dataset Description
50
+
51
+ - **Homepage:** https://covidqa.ai
52
+ - **Repository:** https://github.com/castorini/pygaggle
53
+ - **Paper:** https://arxiv.org/abs/2004.11339
54
+ - **Point of Contact:** [Castorini research group @UWaterloo](https://github.com/castorini/)
55
+
56
+ ### Dataset Summary
57
+
58
+ CovidQA is a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered
59
+ from Kaggle’s COVID-19 Open Research Dataset Challenge.
60
+ The dataset comprises 156 question-article pairs with 27 questions (topics) and 85 unique articles.
61
+
62
+ ### Supported Tasks and Leaderboards
63
+
64
+ [More Information Needed]
65
+
66
+ ### Languages
67
+
68
+ The text in the dataset is in English.
69
+
70
+ ## Dataset Structure
71
+
72
+ ### Data Instances
73
+
74
+ **What do the instances that comprise the dataset represent?**
75
+ Each represents a question, a context (document passage from the CORD19 dataset) and an answer.
76
+
77
+ **How many instances are there in total?**
78
+
79
+ **What data does each instance consist of?**
80
+ Each instance is a query (natural language question and keyword-based), a set of answers, and a document id with its title associated with each answer.
81
+
82
+ [More Information Needed]
83
+
84
+ ### Data Fields
85
+
86
+ The data was annotated in SQuAD style fashion, where each row contains:
87
+
88
+ * **question_query**: Natural language question query
89
+ * **keyword_query**: Keyword-based query
90
+ * **category_name**: Category in which the queries are part of
91
+ * **answers**: List of answers
92
+ * **id**: The document ID the answer is found on
93
+ * **title**: Title of the document of the answer
94
+ * **exact_answer**: Text (string) of the exact answer
95
+
96
+ ### Data Splits
97
+
98
+ **data/kaggle-lit-review-0.2.json**: 156 question-article pairs with 27 questions (topics) and 85 unique articles from
99
+ CORD-19.
100
+
101
+ [More Information Needed]
102
+
103
+ ## Dataset Creation
104
+
105
+ The dataset aims to help for guiding research until more substantial evaluation resources become available. Being a smaller dataset,
106
+ it can be helpful for evaluating the zero-shot or transfer capabilities of existing models on topics specifically related to COVID-19.
107
+
108
+ ### Curation Rationale
109
+
110
+ [More Information Needed]
111
+
112
+ ### Source Data
113
+
114
+ #### Initial Data Collection and Normalization
115
+
116
+ #### Who are the source language producers?
117
+
118
+ [More Information Needed]
119
+
120
+ ### Annotations
121
+
122
+ Five of the co-authors participated in this annotation effort, applying the aforementioned approach, with one lead
123
+ annotator responsible for approving topics and answering technical questions from the other annotators. Two annotators are
124
+ undergraduate students majoring in computer science, one is a science alumna, another is a computer science professor,
125
+ and the lead annotator is a graduate student in computer science—all affiliated with the University of Waterloo.
126
+
127
+ #### Annotation process
128
+
129
+ #### Who are the annotators?
130
+
131
+ ### Personal and Sensitive Information
132
+
133
+ [More Information Needed]
134
+
135
+ ## Considerations for Using the Data
136
+
137
+ ### Social Impact of Dataset
138
+
139
+ The dataset was intended as a stopgap measure for guiding research until more substantial evaluation resources become available.
140
+
141
+ ### Discussion of Biases
142
+
143
+ [More Information Needed]
144
+
145
+ ### Other Known Limitations
146
+
147
+ While this dataset, comprising 124 question–article pairs as of the present version 0.1 release, does not have sufficient
148
+ examples for supervised machine learning, it can be helpful for evaluating the zero-shot or transfer capabilities
149
+ of existing models on topics specifically related to COVID-19.
150
+
151
+ ## Additional Information
152
+
153
+ The listed authors in the homepage are maintaining/supporting the dataset.
154
+
155
+ ### Dataset Curators
156
+
157
+ [More Information Needed]
158
+
159
+ The covidqa dataset is licensed under
160
+ the [Apache License 2.0](https://github.com/castorini/pygaggle/blob/master/LICENSE)
161
+
162
+ [More Information Needed]
163
+
164
+ ### Citation Information
165
+
166
+ ```
167
+ @article{tang2020rapidly,
168
+ title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19},
169
+ author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy},
170
+ journal={arXiv preprint arXiv:2004.11339},
171
+ year={2020}
172
+ }
173
+ ```
covid_qa_castorini.py ADDED
@@ -0,0 +1,117 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ """CovidQA, a question answering dataset specifically designed for COVID-19."""
16
+
17
+ from __future__ import absolute_import, division, print_function
18
+
19
+ import json
20
+ import logging
21
+
22
+ import datasets
23
+
24
+
25
+ _CITATION = """\
26
+ @article{tang2020rapidly,
27
+ title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19},
28
+ author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy},
29
+ journal={arXiv preprint arXiv:2004.11339},
30
+ year={2020}
31
+ }
32
+ """
33
+
34
+ _DESCRIPTION = """\
35
+ CovidQA is the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from \
36
+ knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge.
37
+ """
38
+
39
+ _HOMEPAGE = "http://covidqa.ai"
40
+
41
+ _LICENSE = "Apache License 2.0"
42
+
43
+ _URL = "https://raw.githubusercontent.com/castorini/pygaggle/master/data/"
44
+ _URLs = {"covid_qa_castorini": _URL + "kaggle-lit-review-0.2.json"}
45
+
46
+
47
+ class CovidQaCastorini(datasets.GeneratorBasedBuilder):
48
+ VERSION = datasets.Version("0.2.0")
49
+
50
+ BUILDER_CONFIGS = [
51
+ datasets.BuilderConfig(
52
+ name="covid_qa_castorini",
53
+ version=VERSION,
54
+ description="CovidQA, a question answering dataset specifically designed for COVID-19",
55
+ ),
56
+ ]
57
+
58
+ def _info(self):
59
+ features = datasets.Features(
60
+ {
61
+ "category_name": datasets.Value("string"),
62
+ "question_query": datasets.Value("string"),
63
+ "keyword_query": datasets.Value("string"),
64
+ "answers": datasets.features.Sequence(
65
+ {
66
+ "id": datasets.Value("string"),
67
+ "title": datasets.Value("string"),
68
+ "exact_answer": datasets.Value("string"),
69
+ }
70
+ ),
71
+ }
72
+ )
73
+ return datasets.DatasetInfo(
74
+ description=_DESCRIPTION,
75
+ features=features,
76
+ supervised_keys=None,
77
+ homepage=_HOMEPAGE,
78
+ license=_LICENSE,
79
+ citation=_CITATION,
80
+ )
81
+
82
+ def _split_generators(self, dl_manager):
83
+ url = _URLs[self.config.name]
84
+ downloaded_filepath = dl_manager.download_and_extract(url)
85
+
86
+ return [
87
+ datasets.SplitGenerator(
88
+ name=datasets.Split.TRAIN,
89
+ gen_kwargs={"filepath": downloaded_filepath},
90
+ ),
91
+ ]
92
+
93
+ def _generate_examples(self, filepath):
94
+ """This function returns the examples in the raw (text) form."""
95
+ logging.info("generating examples from = %s", filepath)
96
+ with open(filepath, encoding="utf-8") as f:
97
+ covid_qa = json.load(f)
98
+ for article in covid_qa["categories"]:
99
+ category_name = article["name"]
100
+ for idx, paragraph in enumerate(article["sub_categories"]):
101
+ question_query = paragraph["nq_name"]
102
+ keyword_query = paragraph["kq_name"]
103
+
104
+ ids = [answer["id"] for answer in paragraph["answers"]]
105
+ titles = [answer["title"] for answer in paragraph["answers"]]
106
+ exact_answers = [answer["exact_answer"] for answer in paragraph["answers"]]
107
+
108
+ yield idx, {
109
+ "category_name": category_name,
110
+ "question_query": question_query,
111
+ "keyword_query": keyword_query,
112
+ "answers": {
113
+ "id": ids,
114
+ "title": titles,
115
+ "exact_answer": exact_answers,
116
+ },
117
+ }
dataset_infos.json ADDED
@@ -0,0 +1 @@
 
1
+ {"covid_qa_deepset": {"description": "COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19.\n", "citation": "@inproceedings{moller2020covid,\n title={COVID-QA: A Question Answering Dataset for COVID-19},\n author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte},\n booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020},\n year={2020}\n}\n", "homepage": "https://github.com/deepset-ai/COVID-QA", "license": "Apache License 2.0", "features": {"document_id": {"dtype": "int32", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "is_impossible": {"dtype": "bool", "id": null, "_type": "Value"}, "id": {"dtype": "int32", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "covid_qa_deepset", "config_name": "covid_qa_deepset", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 65151262, "num_examples": 2019, "dataset_name": "covid_qa_deepset"}}, "download_checksums": {"https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/question-answering/COVID-QA.json": {"num_bytes": 4418117, "checksum": "291abf17f4bc2bd343838fd8ef5debb6278bbbb61b262db1f1bd58048fff76b9"}}, "download_size": 4418117, "post_processing_size": null, "dataset_size": 65151262, "size_in_bytes": 69569379}, "covidqa": {"description": "CovidQA is the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge.\n", "citation": "@article{tang2020rapidly,\n title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19},\n author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy},\n journal={arXiv preprint arXiv:2004.11339},\n year={2020}\n}\n", "homepage": "http://covidqa.ai", "license": "Apache License 2.0", "features": {"category_name": {"dtype": "string", "id": null, "_type": "Value"}, "question_query": {"dtype": "string", "id": null, "_type": "Value"}, "keyword_query": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "exact_answer": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "covid_qa_castorini", "config_name": "covidqa", "version": {"version_str": "0.2.0", "description": null, "major": 0, "minor": 2, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 33757, "num_examples": 27, "dataset_name": "covid_qa_castorini"}}, "download_checksums": {"https://raw.githubusercontent.com/castorini/pygaggle/master/data/kaggle-lit-review-0.2.json": {"num_bytes": 51438, "checksum": "b998dee956c4592a63828c628d1a369e6a81b8527e384a9d3448f417008080fb"}}, "download_size": 51438, "post_processing_size": null, "dataset_size": 33757, "size_in_bytes": 85195}, "covid_qa_castorini": {"description": "CovidQA is the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge.\n", "citation": "@article{tang2020rapidly,\n title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19},\n author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy},\n journal={arXiv preprint arXiv:2004.11339},\n year={2020}\n}\n", "homepage": "http://covidqa.ai", "license": "Apache License 2.0", "features": {"category_name": {"dtype": "string", "id": null, "_type": "Value"}, "question_query": {"dtype": "string", "id": null, "_type": "Value"}, "keyword_query": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "exact_answer": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "covid_qa_castorini", "config_name": "covid_qa_castorini", "version": {"version_str": "0.2.0", "description": null, "major": 0, "minor": 2, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 33757, "num_examples": 27, "dataset_name": "covid_qa_castorini"}}, "download_checksums": {"https://raw.githubusercontent.com/castorini/pygaggle/master/data/kaggle-lit-review-0.2.json": {"num_bytes": 51438, "checksum": "b998dee956c4592a63828c628d1a369e6a81b8527e384a9d3448f417008080fb"}}, "download_size": 51438, "post_processing_size": null, "dataset_size": 33757, "size_in_bytes": 85195}}
dummy/covid_qa_castorini/0.2.0/dummy_data.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:fd6ec0f0f3b2d87510390216557858020b12810f113b0e009f9eeb06223b9b65
3
+ size 7241