# SQuAD Adaptation

ELECTRA-small model fine-tuned on SQuAD dataset for question answering tasks

This model is a fine-tuned version of Legal-BERT on the SQuAD dataset, specializing in legal domain question-answering tasks.

A question-answering model based on the BART-base architecture, fine-tuned on the SQuAD dataset, suitable for few-shot learning scenarios

This model is a fine-tuned version of facebook/bart-base on the SQuAD dataset, suitable for question-answering tasks.

BART-base model fine-tuned on SQuAD dataset, suitable for QA tasks

This model is a fine-tuned version of deepset/roberta-base-squad2, suitable for question answering tasks.

A BERT-based question answering model fine-tuned on the SQuAD dataset

A RoBERTa-base model fine-tuned for financial domain Q&A tasks, specifically designed for customized Q&A systems in banking, finance, and insurance sectors.

A QA model fine-tuned on the SQuAD dataset using few-shot learning based on RoBERTa-base

A question-answering model fine-tuned on the SQuAD dataset based on the RoBERTa-base model, suitable for reading comprehension tasks.

A question-answering model fine-tuned on the SQuAD dataset based on RoBERTa-base, suitable for reading comprehension tasks

A QA model fine-tuned on the SQuAD dataset based on RoBERTa-base, suitable for reading comprehension tasks

A question-answering model fine-tuned on the Squad V2 dataset based on the Mobile-Bert architecture, suitable for resource-limited devices

A Q&A model based on ALBERT XXLarge architecture, fine-tuned for the SQuAD V2 dataset

A Q&A model fine-tuned on the SQuAD dataset based on the BERT base model