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

Training and Finetuning Embedding Models with Sentence Transformers

Training and Finetuning Sparse Embedding Models with Sentence Transformers

Multimodal Embedding & Reranker Models with Sentence Transformers

Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers

Train 400x faster Static Embedding Models with Sentence Transformers

Train a Sentence Embedding Model with 1B Training Pairs

Train and Fine-Tune Sentence Transformers Models

How Far Do Simple Transformations Translate Across Text Embedding Models?

arXiv:2608. 05980v1 Announce Type: new Abstract: We investigate whether simple transformations can translate representations across heterogeneous text embedding models.

Introduction to Transformers: an NLP Perspective

arXiv:2311. 17633v2 Announce Type: replace-cross Abstract: Transformers have dominated empirical machine learning models of natural language processing.

Multiple Choice Learning of Low-Rank Adapters for Language Modeling

arXiv:2507. 10419v3 Announce Type: replace-cross Abstract: We propose LoRA-MCL, a training scheme that extends next-token prediction in language models with a method designed to decode diverse, plausible sentence continuations at inference time.

Training and Finetuning Reranker Models with Sentence Transformers

Aligning Sentence Embeddings to Human Concepts via Sparse Autoencoders

arXiv:2607. 00023v1 Announce Type: cross Abstract: Dense sentence embeddings are fundamental to modern Retrieval-Augmented Generation (RAG) systems but suffer from a lack of interpretability due to feature superposition.