patil-suraj/question_generation

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1141 stars · 347 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

An open-source study and library for neural question generation using pre-trained seq2seq transformer models like T5 via Hugging Face transformers. It provides pipelines for answer-aware, multitask QA-QG, and end-to-end answer-agnostic question generation, plus simplified training and evaluation scripts.

Use cases

• generate questions from a text passage
• create quiz questions from articles automatically
• generate questions for a given answer and context
• build QA datasets by generating questions from documents
• fine-tune a transformer model for question generation
• extract answers from text and generate questions about them

When to choose

• you need to generate questions from text using pre-trained transformer models
• you want simple pipelines for answer-aware or answer-agnostic question generation
• you want to fine-tune seq2seq models like T5 on question generation tasks
• you need to bootstrap QA training data from raw passages

When to avoid

• you need production-grade, actively maintained NLP pipelines with long-term support
• you want rule-based or template-based question generation without deep learning
• you need question answering rather than question generation
• you cannot run GPU inference or fine-tuning for transformer models

Facets

library · maturity maintenance

nlp machine-learning deep-learning data-generation machine-learning deep-learning python question-generation transformers t5 seq2seq squad nlg natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="patil-suraj/question_generation")