# Q&A System Optimization

A cross-encoder model trained on the msmarco dataset using the sentence-transformers library, designed for text re-ranking and semantic search

This is a preview version of a Japanese general-purpose reranking model, trained on the cl-nagoya/ruri-v3-pt-310m base model, specifically designed for Japanese text relevance ranking tasks.

This is a cross-encoder model fine-tuned from ModernBERT-base, designed for calculating scores of text pairs, suitable for text re-ranking and semantic search tasks.

This is a cross-encoder model fine-tuned on MiniLM-L12-H384-uncased for text re-ranking and semantic search tasks.

Ruri-Reranker is a Japanese text re-ranking model based on sentence-transformers, specifically designed to optimize the relevance ranking between queries and documents.

A model fine-tuned based on deepset/roberta-base-squad2, specific purpose not explicitly stated

A Q&A model fine-tuned on the SQuADv1 dataset based on BERT-large, optimized with second-order pruning technology

This model is a fine-tuned version of distilbert-base-cased-distilled-squad, suitable for question-answering tasks

A Q&A model fine-tuned on SQuAD1, AQA, and NewsQA datasets based on the DeBERTa-base model

This model is a fine-tuned version of distilbert-base-uncased on the SQuAD adversarial dataset, suitable for question-answering tasks.

This model is obtained by fine-tuning a pre-trained 80% 1x4 block sparse Prune OFA BERT-Large model through knowledge distillation, demonstrating excellent performance on the SQuADv1.1 Q&A task.

FiD is a Q&A system model based on knowledge distillation, improving the efficiency of Q&A systems by distilling the knowledge from the reader model into the retriever.

A model fine-tuned on SQuAD2.0 and custom Q&A datasets based on Distilbert_Base, focusing on efficient Q&A tasks

This is a Q&A model optimized via nn_pruning library, retaining 50% of original weights, fine-tuned on SQuAD v1 with F1 score reaching 88.72

This model is based on the DistilBERT architecture, fine-tuned on the SQuAD2 and MIT Restaurant datasets, supporting both question answering and named entity recognition tasks.

This model is a fine-tuned version of bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and mit_restaurant datasets, supporting token classification tasks.

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

This model is based on the BERT-mini architecture, fine-tuned on the SQuAD 2.0 dataset using the M-FAC second-order optimizer for question answering tasks.