Sentence Transformers
AssessLanguages & Frameworks
A framework for training and using transformer-based sentence embedding and cross-encoder models.
Why it's here
Placed in Assess: 4 article(s) of evidence from 1 source(s), led by model releases, with 0 in the last 30 days. Confidence 41%.
Evidence (4)
- 7Hugging Face Blog·5/19/2026model_releaseHugging Face releases Ettin reranker family
Hugging Face announced six new Sentence Transformers CrossEncoder rerankers built on Ettin ModernBERT encoders, ranging from 17M to 1B parameters. The release includes the models, training data, and full training recipe, and positions them for retrieve-then-rerank pipelines alongside embedding models.
- 8Hugging Face Blog·5/14/2026model_releaseIBM Granite Releases Multilingual Embedding R2 Models
IBM has released two Apache 2.0 multilingual embedding models based on ModernBERT: a 97M-parameter compact model and a 311M full-size model. The models support 200+ languages, 32K-token context, and code retrieval across nine programming languages, with strong retrieval benchmark results and Matryoshka support for the larger model.
- 6Hugging Face Blog·4/16/2026researchSentence Transformers guide to finetuning multimodal embedding and reranker models
Hugging Face's Sentence Transformers blog explains how to train and finetune multimodal embedding and reranker models on custom data. The article uses visual document retrieval as an example, showing that finetuning Qwen/Qwen3-VL-Embedding-2B can improve retrieval quality over the base model and outperform larger competing models in the author's evaluation.
- 7Hugging Face Blog·4/9/2026framework_updateSentence Transformers Adds Multimodal Embedding and Reranking
Sentence Transformers v5.4 adds support for encoding and comparing text, images, audio, and video through the same API. The update enables multimodal embedding and reranker workflows for use cases such as cross-modal search, visual document retrieval, and multimodal RAG.