README.md

metadata

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:

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)