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Paraphrase Any Question with T5 (Text-To-Text Transformer)
Given a question, generate paraphrased versions of the question with T5 transformer. Pretrained model and training script provided.
t5 transformers huggingface pytorch
Generate Boolean (Yes/No) Questions From Any Content
Question generation algorithm trained on the BoolQ dataset using T5 text-to-text transformer model.
question-generation transformers huggingface t5
Automatically Generate Multiple Choice Questions (MCQs)
Automatically Generate Multiple Choice Questions (MCQs) from any content with BERT Summarizer, Wordnet, and Conceptnet
question-generation bert wordnet conceptnet
TeachEasy: Web app for Text Summarization & Q/A generation
An intuitive Streamlit based web app for Text Summarization and Question Answer generation so as to reduce the work for School teachers.
text-summarization question-generation question-answering paraphrase-identification
Q*BERT
Agents that build knowledge graphs and explore textual worlds by asking questions.
bert transformers knowledge-graphs question-generation
Paraphrase Generation Using T5 model
Simple application using T5 base model fine tuned in Quora Question Pairs to generate paraphrased questions.
t5 transformers question-generation paraphrase-generation
Generate True or False questions from any content
Automatically generate “True or False” questions like the ones you see in school textbooks using OpenAI GPT2, Sentence BERT, and Berkley parser
gpt2 bert constituency-parsing transformers
Unsupervised Toolbox
Unsupervised learning Tool box : A micro framework for State of the Art Methods and models for unsupervised learning for NLU / NLG
question-generation question-answering question-similarity autoencoders
Semantic Graphs for Generating Deep Questions
Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information of the input passage.
question-generation semantic-graphs attention natural-language-processing
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