sentence-transformers

The tweet notes that Sentence Transformers models for search have gained popularity on Hugging Face, and expresses hope for a resurgence of encoders with zero-shot capabilities.

The tweet expresses pride in Sentence Transformers' all-MiniLM-L6-v2 trending on Hugging Face, but advises against using it because it's outdated and better performance is available.

This blog post introduces how to train and finetune multi-vector embedding models using the Sentence Transformers library, showcasing its v6.0 update with a new MultiVectorEncoder type and demonstrating superior performance on medical retrieval tasks.

Sentence Transformers v6.0 introduces MultiVectorEncoder for ColBERT-style late interaction retrieval, enhancing support for multi-vector embedding models with a familiar API.

This paper proposes domain adaptation of Sentence Transformer models to automate the mapping between cloud security controls and technical metrics, achieving significant performance gains over zero-shot baselines on control-to-metric and cross-standard association tasks.

Introducing the Ettin Reranker family: six new state-of-the-art CrossEncoder rerankers at various sizes, built on ModernBERT encoders, with open-source data and training recipe.

This article introduces ProtSent, a contrastive fine-tuning framework for protein language models that improves embedding quality for downstream tasks like remote homology detection and structural retrieval.

Developer seeks advice on handling English-Hindi code-mixed text classification without heavy LLMs, as sentence transformers fail on Romanized Hindi.

This article provides a technical guide on training and fine-tuning multimodal embedding and reranker models using the Sentence Transformers library, demonstrating performance improvements on Visual Document Retrieval tasks with Qwen3-VL.

Sentence Transformers v5.4 introduces support for multimodal embedding and reranking, allowing users to encode and compare text, images, audio, and video using a unified API.

This model maps sentences to 384-dimensional vectors for tasks like clustering and semantic search, fine-tuned on 1B sentence pairs using contrastive learning. It is part of the sentence-transformers library and can be used with Hugging Face Transformers.

A domain name valuation AI model using neural networks with sentence transformers and domain tokenization, providing instant price estimates across auction, marketplace, and brokerage channels.