# Sentence Similarity Calculation

Nomic Embed Text V2 GGUF is a multilingual text embedding model supporting over 70 languages, suitable for sentence similarity calculation and feature extraction tasks.

A pre-trained model for sentence similarity calculation, capable of converting sentences into embedded representations in a high-dimensional vector space and computing their semantic similarity.

Qwen2-VL-2B-Instruct is a vision-language model based on the Qwen2 architecture, supporting both English and Chinese, suitable for various natural language processing tasks.

Snowflake Arctic Embed v2.0 is a multilingual sentence embedding model that supports text feature extraction and sentence similarity calculation for over 100 languages.

Snowflake Arctic Embed M v2.0 is a multilingual sentence embedding model that supports over 50 languages, focusing on sentence similarity and feature extraction tasks.

Jina Embeddings V3 is a multilingual sentence embedding model supporting over 100 languages, specializing in sentence similarity calculation and feature extraction tasks.

An embedding model specifically designed for Vietnamese, optimized based on PhoBERT, capable of encoding Vietnamese sentences into a 768-dimensional vector space, suitable for various scenarios such as semantic search and text clustering.

A 7B-parameter sentence embedding model based on the Qwen1.5 architecture, focusing on sentence similarity calculation and multi-task evaluation

Snowflake Arctic Embed M is a sentence transformer model focused on sentence similarity tasks, capable of efficiently extracting text features and calculating similarity between sentences.

A sentence transformer model specifically designed for Italian Q&A tasks, capable of parsing and analyzing Italian text to predict the most likely context containing answers.

PromCSE is a supervised learning-based sentence embedding model specifically designed for calculating Chinese sentence similarity.

This is a sentence embedding model based on sentence-transformers, capable of mapping text to a 768-dimensional vector space, suitable for semantic search and text similarity calculation.

A sentence embedding model based on BioBERT, trained on multiple NLI datasets, suitable for sentence similarity calculation and semantic search tasks

A Czech-English semantic embedding model fine-tuned with SimCSE objective, based on Seznam/dist-mpnet-paracrawl-cs-en

This is a Czech-English bilingual BERT-small model distilled from the all-mpnet-base-v2 model, developed by Seznam.cz, specializing in semantic embedding tasks.

A multilingual text vectorization model focused on sentence similarity calculation and feature extraction tasks

GTE-small is a compact general-purpose text embedding model suitable for various natural language processing tasks, including sentence similarity calculation, text classification, and retrieval.

This dataset is used for training and evaluating the SimCSE (Simple Contrastive Learning of Sentence Embeddings) model, supporting sentence similarity tasks.

A multilingual sentence embedding model supporting Chinese, English, German, French, and other languages, focusing on sentence similarity calculation and feature extraction tasks.

This is a Nepali sentence similarity calculation model based on sentence-transformers, which maps sentences and paragraphs into a 768-dimensional vector space.

This model is designed to map sentences from biomedical literature abstracts into vector space and calculate sentence similarity, specifically trained for PubMed literature.

This model is used to predict the relevance of query-document pairs, suitable for information retrieval tasks.

This is a Japanese sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional vector space, suitable for tasks like clustering and semantic search.

German semantic similarity model, fine-tuned based on gBERT-large, used for generating high-quality German sentence embeddings

A BERT model trained on the CORD19 coronavirus research paper dataset, fine-tuned for natural language inference tasks to generate universal sentence embeddings

An improved model based on BioBERT, fine-tuned with SNLI and MultiNLI datasets for generating universal sentence embeddings

An extremely compact distilled version (45MB, 12M parameters) of the bert-base-multilingual-cased model for Russian and English, prioritizing speed and size over absolute accuracy

This is a sentence transformer model based on allenai/biomed_roberta_base, specifically fine-tuned for sentence similarity tasks, capable of mapping text to a 768-dimensional vector space.

This is a Japanese-specific sentence embedding model based on the BERT architecture, designed to generate semantic vector representations of sentences and calculate sentence similarity.

SciBERT-based scientific text sentence encoder, trained on 2 million scientific papers through self-supervised learning

This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.

SBERT-large-nli-v2 is a large-scale sentence transformer model based on BERT, specifically designed for sentence similarity calculation and feature extraction.

SGPT-1.3B is a sentence transformer model optimized with weighted mean and bitfit techniques, primarily used for sentence similarity and feature extraction tasks.