Datasets:

Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
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Update files from the datasets library (from 1.11.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.11.0

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  1. .gitattributes +27 -0
  2. README.md +179 -0
  3. dataset_infos.json +1 -0
  4. disfl_qa.py +199 -0
  5. dummy/1.1.0/dummy_data.zip +3 -0
.gitattributes ADDED
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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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+ *.ftz 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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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot 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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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.xz 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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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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1
+ ---
2
+ annotations_creators:
3
+ - expert-generated
4
+ language_creators:
5
+ - found
6
+ languages:
7
+ - en
8
+ licenses:
9
+ - cc-by-4-0
10
+ multilinguality:
11
+ - monolingual
12
+ pretty_name: 'DISFL-QA: A Benchmark Dataset for Understanding Disfluencies in Question
13
+ Answering'
14
+ size_categories:
15
+ - 10K<n<100K
16
+ source_datasets:
17
+ - original
18
+ task_categories:
19
+ - question-answering
20
+ task_ids:
21
+ - extractive-qa
22
+ - open-domain-qa
23
+ ---
24
+
25
+ # Dataset Card for DISFL-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering
26
+
27
+ ## Table of Contents
28
+ - [Table of Contents](#table-of-contents)
29
+ - [Dataset Description](#dataset-description)
30
+ - [Dataset Summary](#dataset-summary)
31
+ - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
32
+ - [Languages](#languages)
33
+ - [Dataset Structure](#dataset-structure)
34
+ - [Data Instances](#data-instances)
35
+ - [Data Fields](#data-fields)
36
+ - [Data Splits](#data-splits)
37
+ - [Dataset Creation](#dataset-creation)
38
+ - [Curation Rationale](#curation-rationale)
39
+ - [Source Data](#source-data)
40
+ - [Annotations](#annotations)
41
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
42
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
43
+ - [Social Impact of Dataset](#social-impact-of-dataset)
44
+ - [Discussion of Biases](#discussion-of-biases)
45
+ - [Other Known Limitations](#other-known-limitations)
46
+ - [Additional Information](#additional-information)
47
+ - [Dataset Curators](#dataset-curators)
48
+ - [Licensing Information](#licensing-information)
49
+ - [Citation Information](#citation-information)
50
+ - [Contributions](#contributions)
51
+
52
+ ## Dataset Description
53
+
54
+ - **Homepage:** [Disfl-QA](https://github.com/google-research-datasets/disfl-qa)
55
+ - **Paper:** [Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering](https://arxiv.org/pdf/2106.04016.pdf)
56
+ - **Point of Contact:** [disfl-qa team](disfl-qa@google.com)
57
+
58
+ ### Dataset Summary
59
+
60
+ Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting, namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 ([Rajpurkar et al., 2018](https://www.aclweb.org/anthology/P18-2124/)) dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as a source of distractors.
61
+
62
+ The final dataset consists of ~12k (disfluent question, answer) pairs. Over 90\% of the disfluencies are corrections or restarts, making it a much harder test set for disfluency correction. Disfl-QA aims to fill a major gap between speech and NLP research community. The authors hope the dataset can serve as a benchmark dataset for testing robustness of models against disfluent inputs.
63
+
64
+ The expriments reveal that the state-of-the-art models are brittle when subjected to disfluent inputs from Disfl-QA. Detailed experiments and analyses can be found in the [paper](https://arxiv.org/pdf/2106.04016.pdf).
65
+
66
+ ### Supported Tasks and Leaderboards
67
+
68
+ [More Information Needed]
69
+
70
+ ### Languages
71
+
72
+ The dataset is in English only.
73
+
74
+ ## Dataset Structure
75
+
76
+ ### Data Instances
77
+
78
+ This example was too long and was cropped:
79
+ ```
80
+ {
81
+ "answers": {
82
+ "answer_start": [94, 87, 94, 94],
83
+ "text": ["10th and 11th centuries", "in the 10th and 11th centuries", "10th and 11th centuries", "10th and 11th centuries"]
84
+ },
85
+ "context": "\"The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave thei...",
86
+ "id": "56ddde6b9a695914005b9629",
87
+ "original question": "When were the Normans in Normandy?",
88
+ "disfluent question": "From which countries no tell me when were the Normans in Normandy?"
89
+ "title": "Normans"
90
+ }
91
+ ```
92
+ ### Data Fields
93
+
94
+ - `id`: a `string` feature.
95
+ - `title`: a `string` feature.
96
+ - `context`: a `string` feature.
97
+ - `original question`: Original question from SQuAD-v2 (a `string` feature)
98
+ - `disfluent question`: Disfluent question from Disfl-QA (a `string` feature)
99
+ - `answers`: a dictionary feature containing:
100
+ - `text`: a `string` feature.
101
+ - `answer_start`: a `int32` feature.
102
+
103
+ ### Data Splits
104
+
105
+ Disfl-QA consists of ~12k disfluent questions with the following train/dev/test splits:
106
+ | File | Questions |
107
+ |-----|-----|
108
+ |train.json | 7182 |
109
+ |dev.json | 1000 |
110
+ |test.json | 3643 |
111
+
112
+ ## Dataset Creation
113
+
114
+ ### Curation Rationale
115
+
116
+ The research in NLP and speech community has been impeded by the lack of curated datasets containing such disfluencies. The datasets available today are mostly conversational in nature, and span a limited number of very specific domains (e.g., telephone conversations, court proceedings). Furthermore, only a small fraction of the utterances in these datasets contain disfluencies, with a limited and skewed distribution of disfluencies types. In the most popular dataset in the literature, the SWITCHBOARD corpus (Godfrey et al., 1992), only 5.9% of the words are disfluencies (Charniak and Johnson, 2001), of which > 50% are repetitions (Shriberg, 1996), which has been shown to be the relatively simpler form of disfluencies (Zayats et al., 2014; Jamshid Lou et al., 2018; Zayats et al., 2019). To fill this gap, the authors presented DISFL-QA, the first dataset containing contextual disfluencies in an information seeking setting, namely question answering over Wikipedia passages.
117
+
118
+ ### Source Data
119
+
120
+ #### Initial Data Collection and Normalization
121
+
122
+ DISFL-QA is constructed by asking human raters to insert disfluencies in questions from SQUAD-v2, a popular question answering dataset, using the passage and remaining questions as context. These contextual disfluencies lend naturalness to DISFL-QA, and challenge models relying on shallow matching between question and context to predict an answer.
123
+
124
+ #### Who are the source language producers?
125
+
126
+ [More Information Needed]
127
+
128
+ ### Annotations
129
+
130
+ #### Annotation process
131
+
132
+ Each question associated with the paragraph is sent for a human annotation task to add a contextual disfluency using the paragraph as a source of distractors. Finally, to ensure the quality of the dataset, a subsequent round of human evaluation with an option to re-annotate is conducted.
133
+
134
+ #### Who are the annotators?
135
+
136
+ [More Information Needed]
137
+
138
+ ### Personal and Sensitive Information
139
+
140
+ [More Information Needed]
141
+
142
+ ## Considerations for Using the Data
143
+
144
+ ### Social Impact of Dataset
145
+
146
+ [More Information Needed]
147
+
148
+ ### Discussion of Biases
149
+
150
+ [More Information Needed]
151
+
152
+ ### Other Known Limitations
153
+
154
+ [More Information Needed]
155
+
156
+ ## Additional Information
157
+
158
+ ### Dataset Curators
159
+
160
+ [More Information Needed]
161
+
162
+ ### Licensing Information
163
+
164
+ Disfl-QA dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
165
+
166
+ ### Citation Information
167
+
168
+ ```
169
+ @inproceedings{gupta-etal-2021-disflqa,
170
+ title = "{Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering}",
171
+ author = "Gupta, Aditya and Xu, Jiacheng and Upadhyay, Shyam and Yang, Diyi and Faruqui, Manaal",
172
+ booktitle = "Findings of ACL",
173
+ year = "2021"
174
+ }
175
+ ```
176
+
177
+ ### Contributions
178
+
179
+ Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.
dataset_infos.json ADDED
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1
+ {"default": {"description": "Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting,\nnamely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 (Rajpurkar et al., 2018)\ndataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as\na source of distractors.\n\nThe final dataset consists of ~12k (disfluent question, answer) pairs. Over 90% of the disfluencies are\ncorrections or restarts, making it a much harder test set for disfluency correction. Disfl-QA aims to fill a\nmajor gap between speech and NLP research community. We hope the dataset can serve as a benchmark dataset for\ntesting robustness of models against disfluent inputs.\n\nOur expriments reveal that the state-of-the-art models are brittle when subjected to disfluent inputs from\nDisfl-QA. Detailed experiments and analyses can be found in our paper.\n", "citation": "@inproceedings{gupta-etal-2021-disflqa,\n title = \"{Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering}\",\n author = \"Gupta, Aditya and Xu, Jiacheng and Upadhyay, Shyam and Yang, Diyi and Faruqui, Manaal\",\n booktitle = \"Findings of ACL\",\n year = \"2021\"\n}\n\n", "homepage": "https://github.com/google-research-datasets/disfl-qa", "license": "Disfl-QA dataset is licensed under CC BY 4.0", "features": {"squad_v2_id": {"dtype": "string", "id": null, "_type": "Value"}, "original question": {"dtype": "string", "id": null, "_type": "Value"}, "disfluent question": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "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, "task_templates": [{"task": "question-answering-extractive", "question_column": "disfluent question", "context_column": "context", "answers_column": "answers"}], "builder_name": "disfl_qa", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7712523, "num_examples": 7182, "dataset_name": "disfl_qa"}, "test": {"name": "test", "num_bytes": 3865097, "num_examples": 3643, "dataset_name": "disfl_qa"}, "validation": {"name": "validation", "num_bytes": 1072731, "num_examples": 1000, "dataset_name": "disfl_qa"}}, "download_checksums": {"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json": {"num_bytes": 42123633, "checksum": "68dcfbb971bd3e96d5b46c7177b16c1a4e7d4bdef19fb204502738552dede002"}, "https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json": {"num_bytes": 4370528, "checksum": "80a5225e94905956a6446d296ca1093975c4d3b3260f1d6c8f68bc2ab77182d8"}, "https://raw.githubusercontent.com/google-research-datasets/Disfl-QA/main/train.json": {"num_bytes": 1467771, "checksum": "5407199d0c039de5b50cfc16d1ed4a3299c9127cb549da4e4a650b30f4e000eb"}, "https://raw.githubusercontent.com/google-research-datasets/Disfl-QA/main/test.json": {"num_bytes": 771364, "checksum": "404801de916ebcb2caa82661dfd189c0520e2766db6838f6ff548088650e565e"}, "https://raw.githubusercontent.com/google-research-datasets/Disfl-QA/main/dev.json": {"num_bytes": 201742, "checksum": "b60e075b810b27a5130fd0aa2cfbc85753b71a0b30dcd2585f540f0a6afe6492"}}, "download_size": 48935038, "post_processing_size": null, "dataset_size": 12650351, "size_in_bytes": 61585389}}
disfl_qa.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
+ """A Benchmark Dataset for Understanding Disfluencies in Question Answering"""
16
+
17
+
18
+ import json
19
+
20
+ import datasets
21
+ from datasets.tasks import QuestionAnsweringExtractive
22
+
23
+
24
+ _CITATION = """\
25
+ @inproceedings{gupta-etal-2021-disflqa,
26
+ title = "{Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering}",
27
+ author = "Gupta, Aditya and Xu, Jiacheng and Upadhyay, Shyam and Yang, Diyi and Faruqui, Manaal",
28
+ booktitle = "Findings of ACL",
29
+ year = "2021"
30
+ }
31
+
32
+ """
33
+
34
+ _DESCRIPTION = """\
35
+ Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting,
36
+ namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 (Rajpurkar et al., 2018)
37
+ dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as
38
+ a source of distractors.
39
+
40
+ The final dataset consists of ~12k (disfluent question, answer) pairs. Over 90% of the disfluencies are
41
+ corrections or restarts, making it a much harder test set for disfluency correction. Disfl-QA aims to fill a
42
+ major gap between speech and NLP research community. We hope the dataset can serve as a benchmark dataset for
43
+ testing robustness of models against disfluent inputs.
44
+
45
+ Our expriments reveal that the state-of-the-art models are brittle when subjected to disfluent inputs from
46
+ Disfl-QA. Detailed experiments and analyses can be found in our paper.
47
+ """
48
+
49
+ _HOMEPAGE = "https://github.com/google-research-datasets/disfl-qa"
50
+
51
+ _LICENSE = "Disfl-QA dataset is licensed under CC BY 4.0"
52
+
53
+ _URL = "https://raw.githubusercontent.com/google-research-datasets/Disfl-QA/main/"
54
+
55
+ _URLS_squad_v2 = {
56
+ "train": "https://rajpurkar.github.io/SQuAD-explorer/dataset/" + "train-v2.0.json",
57
+ "dev": "https://rajpurkar.github.io/SQuAD-explorer/dataset/" + "dev-v2.0.json",
58
+ }
59
+
60
+
61
+ class DisflQA(datasets.GeneratorBasedBuilder):
62
+ """A Benchmark Dataset for Understanding Disfluencies in Question Answering"""
63
+
64
+ VERSION = datasets.Version("1.1.0")
65
+
66
+ def _info(self):
67
+ features = datasets.Features(
68
+ {
69
+ "squad_v2_id": datasets.Value("string"),
70
+ "original question": datasets.Value("string"),
71
+ "disfluent question": datasets.Value("string"),
72
+ "title": datasets.Value("string"),
73
+ "context": datasets.Value("string"),
74
+ "answers": datasets.features.Sequence(
75
+ {
76
+ "text": datasets.Value("string"),
77
+ "answer_start": datasets.Value("int32"),
78
+ }
79
+ ),
80
+ }
81
+ )
82
+ return datasets.DatasetInfo(
83
+ # This is the description that will appear on the datasets page.
84
+ description=_DESCRIPTION,
85
+ # This defines the different columns of the dataset and their types
86
+ features=features, # Here we define them above because they are different between the two configurations
87
+ # If there's a common (input, target) tuple from the features,
88
+ # specify them here. They'll be used if as_supervised=True in
89
+ # builder.as_dataset.
90
+ supervised_keys=None,
91
+ # Homepage of the dataset for documentation
92
+ homepage=_HOMEPAGE,
93
+ # License for the dataset if available
94
+ license=_LICENSE,
95
+ # Citation for the dataset
96
+ citation=_CITATION,
97
+ task_templates=[
98
+ QuestionAnsweringExtractive(
99
+ question_column="disfluent question", context_column="context", answers_column="answers"
100
+ )
101
+ ],
102
+ )
103
+
104
+ def _split_generators(self, dl_manager):
105
+ """Returns SplitGenerators."""
106
+
107
+ squad_v2_downloaded_files = dl_manager.download_and_extract(_URLS_squad_v2)
108
+
109
+ return [
110
+ datasets.SplitGenerator(
111
+ name=datasets.Split.TRAIN,
112
+ # These kwargs will be passed to _generate_examples
113
+ gen_kwargs={
114
+ "filepath": dl_manager.download_and_extract(_URL + "train.json"),
115
+ "split": "train",
116
+ "squad_v2_data": squad_v2_downloaded_files,
117
+ },
118
+ ),
119
+ datasets.SplitGenerator(
120
+ name=datasets.Split.TEST,
121
+ # These kwargs will be passed to _generate_examples
122
+ gen_kwargs={
123
+ "filepath": dl_manager.download_and_extract(_URL + "test.json"),
124
+ "split": "test",
125
+ "squad_v2_data": squad_v2_downloaded_files,
126
+ },
127
+ ),
128
+ datasets.SplitGenerator(
129
+ name=datasets.Split.VALIDATION,
130
+ # These kwargs will be passed to _generate_examples
131
+ gen_kwargs={
132
+ "filepath": dl_manager.download_and_extract(_URL + "dev.json"),
133
+ "split": "dev",
134
+ "squad_v2_data": squad_v2_downloaded_files,
135
+ },
136
+ ),
137
+ ]
138
+
139
+ def _generate_examples(
140
+ self,
141
+ filepath,
142
+ split,
143
+ squad_v2_data, # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
144
+ ):
145
+ """Yields examples as (key, example) tuples."""
146
+
147
+ merge_squad_v2_json = {}
148
+
149
+ for file in squad_v2_data:
150
+ with open(squad_v2_data[file], encoding="utf-8") as f:
151
+ merge_squad_v2_json.update(json.load(f))
152
+
153
+ squad_v2_dict = _helper_dict(merge_squad_v2_json) # contains all squad_v2 data in a dict with id as key
154
+
155
+ with open(filepath, encoding="utf-8") as f:
156
+ glob_id = 0
157
+ for id_, row in enumerate(f):
158
+ data = json.loads(row)
159
+ for i in data:
160
+ yield glob_id, {
161
+ "squad_v2_id": i,
162
+ "disfluent question": data[i]["disfluent"],
163
+ "title": squad_v2_dict[i]["title"],
164
+ "context": squad_v2_dict[i]["context"],
165
+ "original question": squad_v2_dict[i]["question"],
166
+ "answers": {
167
+ "answer_start": squad_v2_dict[i]["answers"]["answer_start"],
168
+ "text": squad_v2_dict[i]["answers"]["text"],
169
+ },
170
+ }
171
+ glob_id += 1
172
+
173
+
174
+ def _helper_dict(row_squad_v2: dict): # creates dict with id as key for combined squad_v2
175
+
176
+ squad_v2_dict = {}
177
+
178
+ for example in row_squad_v2["data"]:
179
+ title = example.get("title", "").strip()
180
+ for paragraph in example["paragraphs"]:
181
+ context = paragraph["context"].strip()
182
+ for qa in paragraph["qas"]:
183
+ question = qa["question"].strip()
184
+ id_ = qa["id"]
185
+
186
+ answer_starts = [answer["answer_start"] for answer in qa["answers"]]
187
+ answers = [answer["text"].strip() for answer in qa["answers"]]
188
+
189
+ squad_v2_dict[id_] = {
190
+ "title": title,
191
+ "context": context,
192
+ "question": question,
193
+ "id": id_,
194
+ "answers": {
195
+ "answer_start": answer_starts,
196
+ "text": answers,
197
+ },
198
+ }
199
+ return squad_v2_dict
dummy/1.1.0/dummy_data.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:77dbcd571d08b1e4abe2267aec2712cd516703c3126124e6de940a669e6cd189
3
+ size 5707