# SQuAD2.0 Adaptation

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

A question answering system model based on the DeBERTa-v3-xsmall architecture, specifically fine-tuned for the SQuAD2.0 dataset

This model is a question-answering model based on the ALBERT architecture, fine-tuned on the SQuAD 2.0 dataset for reading comprehension tasks.

This is a RoBERTa-based extractive question answering model, specifically trained on the SQuAD 2.0 dataset, suitable for English Q&A tasks.

A bilingual (English + German) QA model fine-tuned on the bert-base-multilingual-cased model, supporting SQuAD2.0 and deQuAD2.0 datasets

SpanBERT is a pre-trained model developed by Facebook Research, fine-tuned on the SQuAD 2.0 dataset for question answering tasks.

This model is fine-tuned from BERT-Tiny on the SQuAD 2.0 dataset for question-answering tasks, with a compact size of only 24.33MB, making it suitable for resource-constrained environments.

BERT-Small is a compact BERT model developed by Google Research, fine-tuned on the SQuAD 2.0 dataset, suitable for Q&A tasks in resource-constrained environments.