t5-large-generation-squad-QuestionAnswer
Maintainer: potsawee
44
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The t5-large-generation-squad-QuestionAnswer model is a T5 large model fine-tuned on the SQuAD dataset to perform question answering. It takes a context (e.g. a news article) as input and generates a question-answer pair as output. This model was developed by potsawee, and is similar to other fine-tuned T5 and DistilBERT models for question answering, such as t5-base-finetuned-question-generation-ap, distilbert-base-uncased-distilled-squad, and distilbert-base-cased-distilled-squad.
Model inputs and outputs
The t5-large-generation-squad-QuestionAnswer model takes a context or passage as input and generates a question-answer pair as output. The answers in the training data are highly extractive, so this model will generate extractive answers. If you would like to generate more abstractive questions and answers, the maintainer recommends using their model trained on the RACE dataset instead.
Inputs
- context: The input passage or text that the model will use to generate a question-answer pair.
Outputs
- question <sep> answer: The model will generate a question and answer separated by the
<sep>token.
Capabilities
The t5-large-generation-squad-QuestionAnswer model can be used to generate questions and answers based on a given context or passage. It is well-suited for tasks like question-answering, reading comprehension, and content summarization. The model's outputs are extractive, meaning the answers are drawn directly from the input text.
What can I use it for?
The t5-large-generation-squad-QuestionAnswer model can be useful for a variety of applications that involve question answering or content understanding, such as:
- Building chatbots or virtual assistants that can answer questions about a given topic or document
- Developing educational or tutoring applications that can generate questions to test a user's understanding of a text
- Enhancing search engine results by generating relevant questions and answers for a user's query
- Automating the process of creating practice questions or assessment materials for students
Things to try
One interesting thing to try with the t5-large-generation-squad-QuestionAnswer model is to experiment with the do_sample=True option when generating output. This can produce a variety of different question-answer pairs for the same input context, which could be useful for generating diverse practice materials or exploring different facets of the input text.
Another idea is to fine-tune the model further on domain-specific data, such as technical manuals or medical literature, to see if it can generate more specialized and accurate question-answer pairs for those types of content.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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