Train 400x faster Static Embedding Models with Sentence Transformers

Training and Finetuning Embedding Models with Sentence Transformers

Training and Finetuning Sparse Embedding Models with Sentence Transformers

Train a Sentence Embedding Model with 1B Training Pairs

Train and Fine-Tune Sentence Transformers Models

Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers

Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers

Multimodal Embedding & Reranker Models with Sentence Transformers

Training and Finetuning Reranker Models with Sentence Transformers

SkMTEB: Slovak Massive Text Embedding Benchmark and Model Adaptation

arXiv:2606. 13647v1 Announce Type: cross Abstract: We introduce SkMTEB, the first comprehensive MTEB-style text embedding benchmark for Slovak, a low-resource West Slavic language, comprising 31 datasets across 7 task types -- nearly 4$\times$ the depth of existing multilingual benchmark coverage for Slovak.

MTEB: Massive Text Embedding Benchmark

BitNet Text Embeddings

LLM-based text embedders have substantially improved retrieval and semantic representation quality, but their deployment remains costly: large backbone models slow down embedding inference, while high-dimensional full-precision embeddings impose substantial storage and bandwidth overhead on large-scale indexes. In this paper, we present BITEMBED, an extreme low-bit framework for LLM-based text embedding that jointly targets encoding efficiency and vector storage.

New and improved embedding model

We are excited to announce a new embedding model which is significantly more capable, cost effective, and simpler to use.