The Best 1357 Text Embedding Tools in 2025

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

A cross-encoder model trained on the MS Marco passage ranking task for query-passage relevance scoring in information retrieval

A sparse retrieval model based on distillation technology, optimized for OpenSearch, supporting inference-free document encoding with improved search relevance and efficiency over V1

A biomedical entity representation model based on PubMedBERT, optimized for semantic relation capture through self-aligned pre-training

GTE-Large is a powerful sentence transformer model focused on sentence similarity and text embedding tasks, excelling in multiple benchmark tests.

GTE-base-en-v1.5 is an English sentence transformer model focused on sentence similarity tasks, excelling in multiple text embedding benchmarks.

GTE Multilingual Base is a multilingual sentence embedding model supporting over 50 languages, suitable for tasks like sentence similarity calculation.

polyBERT is a chemical language model designed to achieve fully machine-driven ultrafast polymer informatics. It maps PSMILES strings into 600-dimensional dense fingerprints to numerically represent polymer chemical structures.

A sentence embedding model based on Turkish BERT, optimized for semantic similarity tasks

A text embedding model fine-tuned based on BAAI/bge-small-en-v1.5, trained with the MEDI dataset and MTEB classification task datasets, optimized for query encoding in retrieval tasks.

GTE-Large is a high-performance English text embedding model that excels in multiple text similarity and classification tasks.

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.

SPLADE model for passage retrieval, improving sparse neural information retrieval through knowledge distillation

A Chinese text embedding model based on the CoSENT (Cosine Sentence) model, which can map sentences to a 768-dimensional dense vector space and is suitable for tasks such as sentence embedding, text matching, or semantic search.

A compact BERT-based Russian encoder capable of generating high-quality sentence embeddings

A cross-encoder model trained on the MS Marco passage ranking task for query-passage relevance scoring in information retrieval.

Ruri is a universal text embedding model for Japanese, focusing on sentence similarity and feature extraction tasks.

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

GTE-small is a general text embedding model trained by Alibaba DAMO Academy, based on the BERT framework, suitable for tasks such as information retrieval and semantic text similarity.

A cross-encoder model trained on the MS Marco passage ranking task for relevance ranking in information retrieval.

This is an ONNX port of sentence-transformers/all-MiniLM-L6-v2, adjusted to return attention weights, specifically designed for BM42 search scenarios.

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 is a large Russian language model based on the BERT architecture, specifically designed for generating sentence embeddings with case-insensitive processing support.

A streamlined version of the LaBSE model specialized for English and Russian, significantly reducing model size while preserving original embedding quality

This is a Spanish sentence similarity calculation model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional vector space.

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

Russian universal sentence encoder, based on the sentence-transformers framework, specifically designed to extract 1024-dimensional dense vectors for Russian text

Quantized ONNX version of the BAAI/bge-small-en-v1.5 model for text classification and similarity search.

GTE-Base is a general-purpose text embedding model focused on sentence similarity and text retrieval tasks, performing well on multiple benchmarks.

This is the ONNX quantized version of the BAAI/bge-m3 model, supporting three functionalities: dense retrieval, multi-vector retrieval, and sparse retrieval, covering over 100 languages.

Supervised SimCSE model based on RoBERTa-large for sentence embedding and feature extraction tasks.

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

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

A lightweight cross-encoder trained on the MS Marco passage ranking task for query-passage relevance scoring in information retrieval

Biomedical entity representation model based on PubMedBERT, optimized for semantic relation capture through self-alignment pre-training

Nomic Embed v2 is a high-performance multilingual Mixture of Experts (MoE) text embedding model supporting approximately 100 languages, excelling in multilingual retrieval tasks.

A general-purpose text embedding model based on Qwen2-1.5B, supporting multilingual and long-text processing

The first multilingual reranking model in the GTE series, supporting 70+ languages with high performance and long text processing capabilities.

A Japanese-English bilingual sentence feature extraction model based on modernbert-ja-310m, supporting sentence similarity computation and text classification tasks

MMLW (I Must Get Better Messages) is a neural text encoder for Polish, optimized for information retrieval tasks.

A cross-encoder model trained on the MS Marco passage ranking task for scoring query-passage relevance in information retrieval

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