# Q&A system optimization

A CrossEncoder model based on the Transformer architecture, fine-tuned on the Quora question pair dataset, used to calculate scores for text pairs, suitable for semantic similarity and semantic search tasks.

This is a ColBERT model fine-tuned on EuroBERT-210m, specifically designed for semantic text similarity calculation in Spanish and English.

A binary classification model for determining the relevance between reference text and questions, optimized for RAG systems

all-MiniLM-L6-v2 is a lightweight sentence embedding model based on the MiniLM architecture, suitable for English text feature extraction and sentence similarity calculation.

This is a Hebrew sentence similarity calculation model based on sentence-transformers, which can map text to a 512-dimensional vector space for semantic search and clustering tasks

Silver Retriever is a neural retrieval model specifically designed for Polish language, focusing on sentence similarity and paragraph retrieval tasks.

A vectorizer developed by Sinequa capable of generating embedding vectors from paragraphs or queries for sentence similarity computation and feature extraction.

This is a Russian question classification model based on ruBert-tiny, designed to distinguish between precise and imprecise questions.

Provides Traditional Chinese transformers models and natural language processing tools

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

Italian sentence embedding model based on sentence-transformers, mapping text to a 768-dimensional vector space

A long-sequence processing model based on XLM-R extension, supporting sequences up to 4096 tokens, suitable for multilingual tasks

ParsBigBird is a Persian Bert model based on the BigBird framework, supporting text sequences up to 4096 tokens in length