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  The model is intended to be used for Q&A task, given the question & context, the model would attempt to infer the answer text, answer span & confidence score.<br>
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  Model is encoder-only (roberta-base) with QuestionAnswering LM Head, fine-tuned on SQUADx dataset with **exact_match:** 86.14 & **f1:** 92.330 performance scores.
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- [Live Demo: Question Answering Leveraging Encoders vs Generative](https://huggingface.co/spaces/anshoomehra/question_answering)
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  Please follow this link for [Generative Question Answering](https://huggingface.co/anshoomehra/question-answering-generative-t5-v1-base-s-q-c/)
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  The model is intended to be used for Q&A task, given the question & context, the model would attempt to infer the answer text, answer span & confidence score.<br>
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  Model is encoder-only (roberta-base) with QuestionAnswering LM Head, fine-tuned on SQUADx dataset with **exact_match:** 86.14 & **f1:** 92.330 performance scores.
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+ [Live Demo: Question Answering Encoders vs Generative](https://huggingface.co/spaces/anshoomehra/question_answering)
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  Please follow this link for [Generative Question Answering](https://huggingface.co/anshoomehra/question-answering-generative-t5-v1-base-s-q-c/)
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