---
dataset_info:
  features:
  - name: task_type
    dtype: string
  - name: instruction
    dtype: string
  - name: target
    dtype: string
  - name: source
    dtype: string
  splits:
  - name: train
    num_bytes: 93075676
    num_examples: 53264
  - name: validation
    num_bytes: 12239164
    num_examples: 6850
  download_size: 45289649
  dataset_size: 105314840
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
language:
- ru
tags:
- qa
- qg
- aaqg
- instruct
- question-answering
- question-generation
size_categories:
- 10K<n<100K
task_categories:
- question-answering
- text2text-generation
---

### Description

This is a dataset created for training Russian-language Seq2Seq and CLM models primarily for tasks related to Closed-Domain QA.

The dataset includes 3 main tasks:
1. AAQG (Answer-Aware Question Answering) - generation of questions based on context, provided the answer is known
2. QG - generating questions based on context, without a known answer
3. QA - the standard task of answering a question based on context.

AAQG, QG, QA tasks are generated based on regular datasets for which the context, question and correct answer are known. They are generated in a ratio of 0.4, 0.3 and 0.3, respectively.

List of datasets used to compile this dataset:
1. sberquad
2. russian_super_glue/muserc
3. russian_super_glue/danetqa

Prompts used for QA tasks:
```python
AAQG_PROMPT = "Сгенерируй вопрос по тексту, используя известный ответ. Текст: '{context}'. Ответ: '{answer}'."
QG_PROMPT = "Сгенерируй вопрос по тексту. Текст: '{context}'."
QA_PROMPT = "Сгенерируй ответ на вопрос по тексту. Текст: '{context}'. Вопрос: '{question}'."
```

### Authors
- Sergei Bratchikov (https://t.me/nlpwanderer)