# Question Generation

This model is based on the t5-large architecture, fine-tuned on the SQuAD dataset for text generation, used to generate questions and corresponding answers from given contexts.

An end-to-end question generation model fine-tuned on T5-base, capable of automatically generating relevant questions from given contexts.

This model is based on the T5-base architecture and fine-tuned on the SQuAD v1.1 dataset, specifically designed for question generation tasks.

A T5-base fine-tuned joint model for question generation and answer extraction, supporting English text processing

A T5-small based question generation model for generating relevant questions from given text

This is a question generation model fine-tuned on the T5-Base architecture, specifically designed to generate relevant questions from given answers and contexts.

This model is a BART variant fine-tuned on the SQuAD dataset, specifically designed to generate corresponding questions based on text passages and answers.

This is a question generation model fine-tuned on the BART-base architecture, specifically designed to generate relevant questions from given text and answers.

A question generation model fine-tuned based on T5-Large, specifically designed to generate questions from the SQuAD dataset.

MixQG is a neural question generation model based on mixed answer types, capable of generating relevant questions based on given context and answers.