metadata
license: mit
task_categories:
- question-answering
language:
- en
Question – Answer Dataset
The dataset contains 400 queries from two domains: Current Affairs and Creative Writing. It serves as a versatile resource for Natural Language Processing (NLP) tasks, including text classification, information retrieval, and model training.
Data attributes:
- Query: The user-generated question. Data type: string.
- Answer: The response provided by a team of writers and editors in markdown format, containing information related to the query.
- Citations: Up to 4 credible sources referenced by the writers to support and validate the information in the answers.
Use cases:
- Fine-tuning ML Models such as BERT, GPT-2, or RoBERTa for question-answering tasks.
- Train custom LLM from scratch for question-answering tasks.
- Model Evaluation for performance and accuracy.
- Develop models for open-domain question-answering.
- Create question-answering chatbots and virtual assistants.
- Build models for answering questions about documents.
The "Question – Answer Dataset" is a valuable resource for a wide range of NLP tasks and applications, from enhancing LLMs to developing chatbots and assisting with document question-answering.
