# Question answering system

A CoSENT framework model designed specifically for Chinese text semantic understanding, suitable for Retrieval Augmented Generation (RAG) tasks

This is an Italian sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.

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

Long T5 is a text-to-text Transformer model extended from T5, which supports efficient processing of long sequence inputs and is particularly suitable for text generation tasks.