# Sentence similarity calculation

GIST-Embedding-v0 is a sentence embedding model based on sentence-transformers, mainly used for sentence similarity calculation and feature extraction tasks.

This model is used to compute embedding vectors for Russian and English sentences, obtained by distilling the embedding vectors from ai-forever/FRIDA. The model is of the uncased type, meaning it does not distinguish between uppercase and lowercase letters in the text.

A Romanian semantic understanding model fine-tuned based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2, compact in size yet outstanding in performance

BERTA is obtained by distilling the embedding vectors of the FRIDA model into LaBSE-ru-turbo, which is used to calculate the embedding vectors of Russian and English sentences and supports multiple prefix tasks.

Static quantized version of ColBERTv2.0, based on lightonai/colbertv2.0 model, offering multiple quantization options

This model is converted from BAAI/bge-m3 to GGUF format via GGUF-my-repo, primarily used for sentence similarity calculation and feature extraction.

This is a BERT-based sentence transformer model for calculating sentence similarity and semantic search tasks.

GTE-small is an efficient sentence embedding model based on the thenlper/gte-small foundation model, focusing on sentence similarity tasks.

This is a sentence transformer model based on ModernBERT-base, specifically designed for calculating sentence similarity and information retrieval tasks.

This is a SentenceTransformer model fine-tuned on jina-embeddings-v2-base-en for generating sentence embeddings and computing semantic similarity.

Nomic-embed-text-v1.5 is a text embedding model developed by Nomic AI, based on the sentence-transformers library, focusing on sentence similarity tasks.

This is a sentence-transformers model fine-tuned from microsoft/mpnet-base, designed for vector representations of sentences and paragraphs, supporting tasks like semantic textual similarity.

Snowflake Arctic Embed M v1.5 is an efficient sentence embedding model, focusing on sentence similarity calculation and feature extraction tasks.

A large language model with 7B parameters based on Qwen2 architecture, focusing on sentence similarity calculation and text embedding tasks.

A Norwegian sentence embedding model fine-tuned based on jina-embeddings-v2-base-en, focusing on keyword document search and sentence similarity tasks

Snowflake Arctic Embed XS is a lightweight sentence embedding model focused on sentence similarity and feature extraction tasks.

Snowflake Arctic M Long is a sentence-transformers-based sentence embedding model focused on sentence similarity and feature extraction tasks.

GGUF format file of the e5-base-v2 embedding model, used for tasks such as sentence similarity calculation, supporting a maximum context of 512 tokens.

bge_micro is a sentence embedding model based on sentence-transformers, focusing on sentence similarity calculation and feature extraction tasks.

GIST-all-MiniLM-L6-v2 is a Sentence-Transformers model focused on sentence similarity tasks, suitable for various natural language processing scenarios such as feature extraction, classification, retrieval, and clustering.

This is a sentence transformer model based on the LaBSE architecture, specifically fine-tuned for Russian-Mari language pairs, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space.

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

LaBSE is a multilingual sentence embedding model that maps sentences from 109 languages into a shared vector space.

A small Czech semantic embedding model fine-tuned with SimCSE objective based on RetroMAE-Small

The LLM-Embedder model fine-tuned on mathematical datasets, focusing on sentence similarity tasks.

bge_micro is a lightweight model focused on sentence similarity calculation, suitable for various natural language processing tasks.

Ember v1 is an embedding model based on sentence-transformers, primarily used for feature extraction and sentence similarity calculation.

bge_micro is a lightweight sentence similarity calculation model based on transformer architecture, specifically designed for efficient feature extraction and sentence similarity tasks.

This is an unsupervised SimCSE-based Japanese sentence embedding model, specifically designed for generating high-quality Japanese sentence embeddings.

This is a sentence-transformers based model for mapping Sinhala sentences into a 768-dimensional vector space, supporting tasks like sentence similarity calculation and semantic search.

This is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space, suitable for tasks like clustering or semantic search.

This is a vocabulary-pruned version of intfloat/multilingual-e5-small, retaining only English and Russian vocabulary, designed for sentence similarity tasks.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for text similarity calculation and semantic search tasks in the e-commerce domain.

BERTugues is a BERT model trained on Portuguese text, strictly following the original BERT paper's pre-training process, completing masked language modeling and next sentence prediction tasks through 1 million training steps.

Hungarian sentence embedding model fine-tuned based on the huBERT pre-trained model, specifically designed for sentence similarity tasks

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 clustering or semantic search.

LEALLA is a collection of lightweight language-agnostic sentence embedding models supporting 109 languages, distilled from LaBSE. Suitable for obtaining multilingual sentence embeddings and bilingual text retrieval.

A sentence similarity calculation model based on the CompCSE dedicated dataset, suitable for English text processing.

LaBSE is a multilingual sentence embedding model based on BERT, supporting 109 languages, suitable for sentence similarity calculation and bilingual text retrieval.

This is a sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 128-dimensional dense vector space, suitable for tasks such as clustering or semantic search.