Yalda Embedding
Yaldat · Kit
Turns Persian text into 1,024-number embeddings for semantic search and retrieval. Built for developers who need to match Persian questions to the right passag…
Yaldat · Kit
Turns Persian text into 1,024-number embeddings for semantic search and retrieval. Built for developers who need to match Persian questions to the right passag…
unsloth · Kit
Turns English text into embeddings for search and retrieval, in a small GGUF file of 33.2M parameters. For developers who build search or retrieval apps on mod…
perplexity-ai · Kit
Turns text, images and document pages into vectors for search, and can embed each chunk with its surrounding document in view. For developers who build retriev…
nomic-ai · Kit
Turns sentences into vectors on your own machine through llama.cpp, using compact GGUF files, for search and RAG questions. For developers who want local text…
lightonai · Kit
Turns queries and documents into sparse vectors you can search with an inverted index, across several languages and long text. A 339M multilingual retrieval mo…
DataScience-UIBK · Kit
Finds the passages that best match a query, using one model that combines token level and whole text matching. An English retrieval model from the University o…
BAAI · Kit
A small English text embedding model from BAAI that turns sentences into vectors for search and retrieval. About 33M parameters, and part of the FlagEmbedding…
BAAI · Kit
Turns English text into number vectors for search and retrieval. A 109M-parameter embedding model from BAAI, for developers who build semantic search or retrie…
perplexity-ai · Kit
Scores queries against text, images and document pages with a ColBERT-style model that keeps one small vector per token. For developers who build search over m…
Linkup-Platform · Kit
A 188M-parameter English sparse retrieval model from Linkup that turns queries and documents into weighted word lists for search. It scores 56.4 on BEIR, the b…
intfloat · Kit
Turns text in about 100 languages into 1024-number vectors for search, matching and retrieval. A 560M-parameter embedding model from intfloat, built on xlm-rob…
hindsight-ai · Kit
Turns text into compact vectors for search and matching in eight languages, with inputs up to 8,192 tokens. A small 112M embedding model built for Hindsight, r…
flowercomputers · Kit
Turns text into 1024-dimension vectors with a static embedding model, trimmed to 25 languages. Fast sentence similarity and retrieval, without a transformer, i…
sentence-transformers · Kit
Turns sentences and paragraphs into 384-number vectors for semantic search, clustering and sentence similarity, with a multilingual model from the sentence-tra…
lightonai · Kit
An English sparse retrieval model from LightOn that turns queries and documents into sparse vectors for inverted-index search. It suits search engineers and de…
nomic-ai · Kit
Turns English text into embedding vectors for search, clustering and classification, and the vectors can be cut to a smaller size. It suits developers building…
Qwen · Kit
Turns text and code in over 100 languages into vectors for search, retrieval, classification and clustering, with adjustable vector size and task instructions,…
MineAITechnology · Kit
Scores how close two short English texts are in meaning with a small 57.8M embedding model trained from scratch, an early experiment for people who study or ba…
unsloth · Kit
Turns text into 768-dimensional vectors for on-device search, retrieval and clustering. The base model also covers images, video and audio. Packaged as GGUF by…
Qwen · Kit
Turns text into vectors for search, code retrieval, classification and clustering, in over 100 languages. The 8B model in Qwen's embedding family, with adjusta…