Datasets:

Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Languages:
Russian
ArXiv:
Libraries:
Datasets
pandas
License:
File size: 5,159 Bytes
358bdfe
 
 
 
 
 
64f4dc3
358bdfe
64f4dc3
358bdfe
 
 
 
 
 
 
 
 
 
 
 
92d74b2
5dcac8c
92d74b2
5dcac8c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
92d74b2
5dcac8c
 
92d74b2
5dcac8c
589aebc
92d74b2
589aebc
deb870e
92d74b2
deb870e
 
 
 
 
 
 
 
 
 
358bdfe
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
59515e9
 
 
 
 
 
 
 
 
 
 
358bdfe
 
 
 
 
5dcac8c
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
---
annotations_creators:
- crowdsourced
language_creators:
- found
- crowdsourced
language:
- ru
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: sberquad
pretty_name: SberQuAD
dataset_info:
  config_name: sberquad
  features:
  - name: id
    dtype: int32
  - name: title
    dtype: string
  - name: context
    dtype: string
  - name: question
    dtype: string
  - name: answers
    sequence:
    - name: text
      dtype: string
    - name: answer_start
      dtype: int32
  splits:
  - name: train
    num_bytes: 71631541
    num_examples: 45328
  - name: validation
    num_bytes: 7972953
    num_examples: 5036
  - name: test
    num_bytes: 36397776
    num_examples: 23936
  download_size: 19770316
  dataset_size: 116002270
configs:
- config_name: sberquad
  data_files:
  - split: train
    path: sberquad/train-*
  - split: validation
    path: sberquad/validation-*
  - split: test
    path: sberquad/test-*
  default: true
---


# Dataset Card for sberquad

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-instances)
  - [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/sberbank-ai/data-science-journey-2017
- **Paper:** https://arxiv.org/abs/1912.09723
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]

### Dataset Summary

Sber Question Answering Dataset (SberQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
Russian original analogue presented in Sberbank Data Science Journey 2017.

### Supported Tasks and Leaderboards

[Needs More Information]

### Languages

Russian

## Dataset Structure

### Data Instances
```
{
    "context": "Первые упоминания о строении человеческого тела встречаются в Древнем Египте...",
    "id": 14754,
    "qas": [
        {
            "id": 60544,
            "question": "Где встречаются первые упоминания о строении человеческого тела?",
            "answers": [{"answer_start": 60, "text": "в Древнем Египте"}],
        }
    ]
}
```

### Data Fields

- id: a int32 feature
- title: a string feature
- context: a string feature
- question: a string feature
- answers: a dictionary feature containing:
   - text: a string feature
   - answer_start: a int32 feature

### Data Splits

|   name   |train |validation|test |
|----------|-----:|---------:|-----|
|plain_text|45328 | 5036     |23936|

## Dataset Creation

### Curation Rationale

[Needs More Information]

### Source Data

#### Initial Data Collection and Normalization

[Needs More Information]

#### Who are the source language producers?

[Needs More Information]

### Annotations

#### Annotation process

[Needs More Information]

#### Who are the annotators?

[Needs More Information]

### Personal and Sensitive Information

[Needs More Information]

## Considerations for Using the Data

### Social Impact of Dataset

[Needs More Information]

### Discussion of Biases

[Needs More Information]

### Other Known Limitations

[Needs More Information]

## Additional Information

### Dataset Curators

[Needs More Information]

### Licensing Information

[Needs More Information]

### Citation Information

```
@InProceedings{sberquad,
doi       = {10.1007/978-3-030-58219-7_1},
author    = {Pavel Efimov and
             Andrey Chertok and
             Leonid Boytsov and
             Pavel Braslavski},
title     = {SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis},
booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction},
year      = {2020},
publisher = {Springer International Publishing},
pages     = {3--15}
}
```

### Contributions

Thanks to [@alenusch](https://github.com/Alenush) for adding this dataset.