# No-answer Detection

An extractive QA model fine-tuned on the SQuAD2.0 dataset based on flan-t5-large, capable of handling both answerable and unanswerable questions.

The xlarge version v2 model based on ALBERT architecture, fine-tuned on the SQuAD V2 dataset for Q&A tasks

This is a large-scale discriminator model based on the ELECTRA architecture, specifically fine-tuned for Q&A tasks on the SQuAD2.0 dataset, capable of handling both answerable and unanswerable question scenarios.

A question answering system based on the ALBERT-xlarge-v2 model fine-tuned on the SQuAD V2 dataset

XLNet is a pre-trained language model jointly developed by Google and Carnegie Mellon University, fine-tuned on the SQuAD 2.0 question answering dataset

A BERT-based pre-trained language model for the scientific domain, trained using a scientific literature vocabulary

RoBERTa-based English extractive question answering model, trained on the SQuAD 2.0 dataset, capable of handling both answerable and unanswerable questions