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--- |
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dataset_info: |
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features: |
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- name: task_type |
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dtype: string |
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- name: instruction |
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dtype: string |
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- name: target |
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dtype: string |
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- name: source |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 93075676 |
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num_examples: 53264 |
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- name: validation |
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num_bytes: 12239164 |
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num_examples: 6850 |
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download_size: 45289649 |
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dataset_size: 105314840 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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language: |
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- ru |
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tags: |
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- qa |
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- qg |
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- aaqg |
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- instruct |
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- question-answering |
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- question-generation |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- question-answering |
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- text2text-generation |
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--- |
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### Description |
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This is a dataset created for training Russian-language Seq2Seq and CLM models primarily for tasks related to Closed-Domain QA. |
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The dataset includes 3 main tasks: |
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1. AAQG (Answer-Aware Question Answering) - generation of questions based on context, provided the answer is known |
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2. QG - generating questions based on context, without a known answer |
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3. QA - the standard task of answering a question based on context. |
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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. |
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List of datasets used to compile this dataset: |
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1. sberquad |
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2. russian_super_glue/muserc |
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3. russian_super_glue/danetqa |
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Prompts used for QA tasks: |
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```python |
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AAQG_PROMPT = "Сгенерируй вопрос по тексту, используя известный ответ. Текст: '{context}'. Ответ: '{answer}'." |
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QG_PROMPT = "Сгенерируй вопрос по тексту. Текст: '{context}'." |
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QA_PROMPT = "Сгенерируй ответ на вопрос по тексту. Текст: '{context}'. Вопрос: '{question}'." |
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``` |
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### Authors |
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- Sergei Bratchikov (https://t.me/nlpwanderer) |
