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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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  1. .gitattributes +27 -0
  2. README.md +165 -0
  3. dataset_infos.json +1 -0
  4. dummy/0.0.0/dummy_data.zip +3 -0
  5. msr_sqa.py +167 -0
.gitattributes ADDED
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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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+ *.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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+ *.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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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zstandard filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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1
+ ---
2
+ annotations_creators:
3
+ - crowdsourced
4
+ language_creators:
5
+ - found
6
+ languages:
7
+ - en
8
+ licenses:
9
+ - ms-pl
10
+ multilinguality:
11
+ - monolingual
12
+ size_categories:
13
+ - 10K<n<100K
14
+ source_datasets:
15
+ - original
16
+ task_categories:
17
+ - question-answering
18
+ task_ids:
19
+ - extractive-qa
20
+ ---
21
+
22
+ # Dataset Card for Microsoft Research Sequential Question Answering
23
+
24
+ ## Table of Contents
25
+
26
+ - [Dataset Card for Microsoft Research Sequential Question Answering](#dataset-card-for-microsoft-research-sequential-question-answering)
27
+ - [Table of Contents](#table-of-contents)
28
+ - [Dataset Description](#dataset-description)
29
+ - [Dataset Summary](#dataset-summary)
30
+ - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
31
+ - [Languages](#languages)
32
+ - [Dataset Structure](#dataset-structure)
33
+ - [Data Instances](#data-instances)
34
+ - [Data Fields](#data-fields)
35
+ - [Data Splits](#data-splits)
36
+ - [Dataset Creation](#dataset-creation)
37
+ - [Curation Rationale](#curation-rationale)
38
+ - [Source Data](#source-data)
39
+ - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
40
+ - [Who are the source language producers?](#who-are-the-source-language-producers)
41
+ - [Annotations](#annotations)
42
+ - [Annotation process](#annotation-process)
43
+ - [Who are the annotators?](#who-are-the-annotators)
44
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
45
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
46
+ - [Social Impact of Dataset](#social-impact-of-dataset)
47
+ - [Discussion of Biases](#discussion-of-biases)
48
+ - [Other Known Limitations](#other-known-limitations)
49
+ - [Additional Information](#additional-information)
50
+ - [Dataset Curators](#dataset-curators)
51
+ - [Licensing Information](#licensing-information)
52
+ - [Citation Information](#citation-information)
53
+
54
+ ## Dataset Description
55
+
56
+ - **Homepage:[Microsoft Research Sequential Question Answering (SQA) Dataset](https://msropendata.com/datasets/b25190ed-0f59-47b1-9211-5962858142c2)**
57
+ - **Repository:**
58
+ - **Paper:[https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/acl17-dynsp.pdf](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/acl17-dynsp.pdf)**
59
+ - **Leaderboard:**
60
+ - **Point of Contact:**
61
+ - Scott Wen-tau Yih scottyih@microsoft.com
62
+ - Mohit Iyyer m.iyyer@gmail.com
63
+ - Ming-Wei Chang minchang@microsoft.com
64
+
65
+ ### Dataset Summary
66
+
67
+ Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions.
68
+
69
+ We created SQA by asking crowdsourced workers to decompose 2,022 questions from WikiTableQuestions (WTQ)*, which contains highly-compositional questions about tables from Wikipedia. We had three workers decompose each WTQ question, resulting in a dataset of 6,066 sequences that contain 17,553 questions in total. Each question is also associated with answers in the form of cell locations in the tables.
70
+
71
+ - Panupong Pasupat, Percy Liang. "Compositional Semantic Parsing on Semi-Structured Tables" ACL-2015.
72
+ [http://www-nlp.stanford.edu/software/sempre/wikitable/](http://www-nlp.stanford.edu/software/sempre/wikitable/)
73
+
74
+ ### Supported Tasks and Leaderboards
75
+
76
+ [More Information Needed]
77
+
78
+ ### Languages
79
+
80
+ English
81
+
82
+ ## Dataset Structure
83
+
84
+ ### Data Instances
85
+
86
+ [More Information Needed]
87
+
88
+ ### Data Fields
89
+
90
+ - `id` (`str`): question sequence id (the id is consistent with those in WTQ)
91
+ - `annotator` (`int`): `0`, `1`, `2` (the 3 annotators who annotated the question intent)
92
+ - `position` (`int`): the position of the question in the sequence
93
+ - `question` (`str`): the question given by the annotator
94
+ - `table_file` (`str`): the associated table
95
+ - `table_header` (`List[str]`): a list of headers in the table
96
+ - `table_data` (`List[List[str]]`): 2d array of data in the table
97
+ - `answer_coordinates` (`List[Dict]`): the table cell coordinates of the answers (0-based, where 0 is the first row after the table header)
98
+ - `row_index`
99
+ - `column_index`
100
+ - `answer_text` (`List[str]`): the content of the answer cells
101
+
102
+ Note that some text fields may contain Tab or LF characters and thus start with quotes.
103
+ It is recommended to use a CSV parser like the Python CSV package to process the data.
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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+ {"default": {"description": "Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We created SQA by asking crowdsourced workers to decompose 2,022 questions from WikiTableQuestions (WTQ), which contains highly-compositional questions about tables from Wikipedia. We had three workers decompose each WTQ question, resulting in a dataset of 6,066 sequences that contain 17,553 questions in total. Each question is also associated with answers in the form of cell locations in the tables.\n", "citation": "@inproceedings{iyyer2017search,\n title={Search-based neural structured learning for sequential question answering},\n author={Iyyer, Mohit and Yih, Wen-tau and Chang, Ming-Wei},\n booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n pages={1821--1831},\n year={2017}\n}\n", "homepage": "https://msropendata.com/datasets/b25190ed-0f59-47b1-9211-5962858142c2", "license": "Microsoft Research Data License Agreement", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "annotator": {"dtype": "int32", "id": null, "_type": "Value"}, "position": {"dtype": "int32", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "table_file": {"dtype": "string", "id": null, "_type": "Value"}, "table_header": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "table_data": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "answer_coordinates": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answer_text": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "msr_sqa", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 22605449, "num_examples": 14541, "dataset_name": "msr_sqa"}, "test": {"name": "test", "num_bytes": 4924516, "num_examples": 3012, "dataset_name": "msr_sqa"}}, "download_checksums": {"https://download.microsoft.com/download/1/D/C/1DC270D2-1B53-4A61-A2E3-88AB3E4E6E1F/SQA%20Release%201.0.zip": {"num_bytes": 4796932, "checksum": "791a07ef90d6e736c186b25009d3c10cb38624b879bb668033445a3ab8892f64"}}, "download_size": 4796932, "post_processing_size": null, "dataset_size": 27529965, "size_in_bytes": 32326897}}
dummy/0.0.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:7e46d5f939a1049a45c605ba21355084b0043e84b5dc6c7dec2717e0aa326510
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+ size 2732
msr_sqa.py ADDED
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1
+ # coding=utf-8
2
+ # Copyright 2020 The HuggingFace Datasets Authors, The Google AI Language Team 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
+ """Microsoft Research Sequential Question Answering (SQA) Dataset"""
16
+
17
+ from __future__ import absolute_import, division, print_function
18
+
19
+ import ast
20
+ import csv
21
+ import os
22
+
23
+ import datasets
24
+
25
+
26
+ # TODO: Add BibTeX citation
27
+ # Find for instance the citation on arxiv or on the dataset repo/website
28
+ _CITATION = """\
29
+ @inproceedings{iyyer2017search,
30
+ title={Search-based neural structured learning for sequential question answering},
31
+ author={Iyyer, Mohit and Yih, Wen-tau and Chang, Ming-Wei},
32
+ booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
33
+ pages={1821--1831},
34
+ year={2017}
35
+ }
36
+ """
37
+
38
+ _DESCRIPTION = """\
39
+ Recent work in semantic parsing for question answering has focused on long and complicated questions, \
40
+ many of which would seem unnatural if asked in a normal conversation between two humans. \
41
+ In an effort to explore a conversational QA setting, we present a more realistic task: \
42
+ answering sequences of simple but inter-related questions. We created SQA by asking crowdsourced workers \
43
+ to decompose 2,022 questions from WikiTableQuestions (WTQ), which contains highly-compositional questions about \
44
+ tables from Wikipedia. We had three workers decompose each WTQ question, resulting in a dataset of 6,066 sequences \
45
+ that contain 17,553 questions in total. Each question is also associated with answers in the form of cell \
46
+ locations in the tables.
47
+ """
48
+
49
+ _HOMEPAGE = "https://msropendata.com/datasets/b25190ed-0f59-47b1-9211-5962858142c2"
50
+
51
+ _LICENSE = "Microsoft Research Data License Agreement"
52
+
53
+ _URL = "https://download.microsoft.com/download/1/D/C/1DC270D2-1B53-4A61-A2E3-88AB3E4E6E1F/SQA%20Release%201.0.zip"
54
+
55
+
56
+ def _load_table_data(table_file):
57
+ """Load additional data from a csv table file.
58
+
59
+ Args:
60
+ table_file: Path to the csv file.
61
+
62
+ Returns:
63
+ header: a list of headers in the table.
64
+ data: 2d array of data in the table.
65
+ """
66
+ with open(table_file, encoding="utf-8") as f:
67
+ lines = f.readlines()
68
+ header = lines[0].strip().split(",")
69
+ data = [line.strip().split(",") for line in lines[1:]]
70
+ return header, data
71
+
72
+
73
+ def _parse_answer_coordinates(answer_coordinate_str):
74
+ """Parsing answer_coordinates field to a list of answer coordinates.
75
+ The original code is from https://github.com/google-research/tapas.
76
+
77
+ Args:
78
+ answer_coordinate_str: A string representation of a Python list of tuple
79
+ strings.
80
+ For example: "['(1, 4)','(1, 3)', ...]"
81
+
82
+ Returns:
83
+ answer_coordinates: A list of answer cordinates.
84
+ """
85
+ try:
86
+ answer_coordinates = []
87
+ coords = ast.literal_eval(answer_coordinate_str)
88
+ for row_index, column_index in sorted(ast.literal_eval(coord) for coord in coords):
89
+ answer_coordinates.append({"row_index": row_index, "column_index": column_index})
90
+ return answer_coordinates
91
+ except SyntaxError:
92
+ raise ValueError("Unable to evaluate %s" % answer_coordinate_str)
93
+
94
+
95
+ def _parse_answer_text(answer_text_str):
96
+ """Parsing `answer_text` field to list of answers.
97
+ The original code is from https://github.com/google-research/tapas.
98
+ Args:
99
+ answer_text_str: A string representation of a Python list of strings.
100
+ For example: "[u'test', u'hello', ...]"
101
+
102
+ Returns:
103
+ answer_texts: A list of answers.
104
+ """
105
+ try:
106
+ answer_texts = []
107
+ for value in ast.literal_eval(answer_text_str):
108
+ answer_texts.append(value)
109
+ return answer_texts
110
+ except SyntaxError:
111
+ raise ValueError("Unable to evaluate %s" % answer_text_str)
112
+
113
+
114
+ class MsrSQA(datasets.GeneratorBasedBuilder):
115
+ """Microsoft Research Sequential Question Answering (SQA) Dataset"""
116
+
117
+ def _info(self):
118
+ return datasets.DatasetInfo(
119
+ description=_DESCRIPTION,
120
+ features=datasets.Features(
121
+ {
122
+ "id": datasets.Value("string"),
123
+ "annotator": datasets.Value("int32"),
124
+ "position": datasets.Value("int32"),
125
+ "question": datasets.Value("string"),
126
+ "table_file": datasets.Value("string"),
127
+ "table_header": datasets.features.Sequence(datasets.Value("string")),
128
+ "table_data": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
129
+ "answer_coordinates": datasets.features.Sequence(
130
+ {"row_index": datasets.Value("int32"), "column_index": datasets.Value("int32")}
131
+ ),
132
+ "answer_text": datasets.features.Sequence(datasets.Value("string")),
133
+ }
134
+ ),
135
+ supervised_keys=None,
136
+ homepage=_HOMEPAGE,
137
+ license=_LICENSE,
138
+ citation=_CITATION,
139
+ )
140
+
141
+ def _split_generators(self, dl_manager):
142
+ """Returns SplitGenerators."""
143
+ data_dir = os.path.join(dl_manager.download_and_extract(_URL), "SQA Release 1.0")
144
+ return [
145
+ datasets.SplitGenerator(
146
+ name=datasets.Split.TRAIN,
147
+ gen_kwargs={"filepath": os.path.join(data_dir, "train.tsv"), "data_dir": data_dir},
148
+ ),
149
+ datasets.SplitGenerator(
150
+ name=datasets.Split.TEST,
151
+ gen_kwargs={"filepath": os.path.join(data_dir, "test.tsv"), "data_dir": data_dir},
152
+ ),
153
+ ]
154
+
155
+ def _generate_examples(self, filepath, data_dir):
156
+ """ Yields examples. """
157
+
158
+ with open(filepath, encoding="utf-8") as f:
159
+ reader = csv.DictReader(f, delimiter="\t")
160
+ for row in reader:
161
+ item = dict(row)
162
+ item["answer_text"] = _parse_answer_text(item["answer_text"])
163
+ item["answer_coordinates"] = _parse_answer_coordinates(item["answer_coordinates"])
164
+ header, table_data = _load_table_data(os.path.join(data_dir, item["table_file"]))
165
+ item["table_header"] = header
166
+ item["table_data"] = table_data
167
+ yield item["id"], item