question_generation
Questgen.ai
| question_generation | Questgen.ai | |
|---|---|---|
| 4 | 3 | |
| 1,070 | 872 | |
| - | - | |
| 0.0 | 6.3 | |
| 29 days ago | 5 months ago | |
| Jupyter Notebook | Python | |
| MIT License | MIT License |
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question_generation
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Yes/No style Question and Answer Generation
I have seen models which do something similar but the questions they ask are not in a Yes/No style such as this T5 - based Question Generator. Essentially, I was wondering how I would go about developing such a model.
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Best solution to make static chatbot for a minecraft server?
I've been able to get good domain-specific performance out of RoBERTa by training it on SQuAD (general QA) and synthetic domain-specific data created via https://github.com/patil-suraj/question_generation, took about 1000 domain-specific QA pairs.
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State of the art models for Question generation
Not sure if it's SOTA but this project and paper is really interesting and quite easy to use : https://github.com/patil-suraj/question_generation
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How could I go about turning a bunch of text into a series of questions with answers?
Take a look at https://github.com/patil-suraj/question_generation . It's a T5 model that tuned for generating questions for multiple formats (reading comprehension, multiple choice, cloze, etc) given an input text.
Questgen.ai
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Yes/No style Question and Answer Generation
I have tried to do some searching for models but there don't seem to be ones that do what I am looking for. The closest I found was Questgen, but it only generated the questions and they, more often than, not did not make sense - especially for the types of questions I was looking to generate.
- [D] How to create a question answering system with a (potentially very large) corpus of text?
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Creating a Wikipedia Question/Answer generator
This library might be of help https://github.com/ramsrigouthamg/Questgen.ai
What are some alternatives?
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
fastT5 - ⚡ boost inference speed of T5 models by 5x & reduce the model size by 3x.
mt5-M2M-comparison - Comparing M2M and mT5 on a rare language pairs, blog post: https://medium.com/@abdessalemboukil/comparing-facebooks-m2m-to-mt5-in-low-resources-translation-english-yoruba-ef56624d2b75
FARM - :house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.
gluon-nlp - NLP made easy
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.