Training and Finetuning Embedding Models with Sentence Transformers

Training and Finetuning Sparse Embedding Models with Sentence Transformers

Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers

Train 400x faster Static Embedding Models with Sentence Transformers

Train and Fine-Tune Sentence Transformers Models

Training and Finetuning Reranker Models with Sentence Transformers

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

Train a Sentence Embedding Model with 1B Training Pairs

Multimodal Embedding & Reranker Models with Sentence Transformers

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.

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.

An expressivity analysis of hierarchical modelling in deep transformers via bounded-depth grammars

arXiv:2606. 17522v1 Announce Type: cross Abstract: Deep neural networks are widely believed to derive their expressive power from their ability to form \textbf{hierarchical representations}, capturing progressively more abstract and compositional features across layers.

An expressivity analysis of hierarchical modelling in deep transformers via bounded-depth grammars

Deep neural networks are widely believed to derive their expressive power from their ability to form \textbf{hierarchical representations}, capturing progressively more abstract and compositional features across layers. In language modeling, \textbf{transformers} have emerged as the dominant architecture, with early layers capturing local syntactic patterns and later layers encoding more complex clause-level dependencies.