Training and Finetuning Sparse Embedding Models with Sentence Transformers

Training and Finetuning Embedding Models with Sentence Transformers

Train 400x faster Static Embedding Models with Sentence Transformers

Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers

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

Train and Fine-Tune Sentence Transformers Models

Training and Finetuning Reranker Models with Sentence Transformers

Multimodal Embedding & Reranker Models with Sentence Transformers

Train a Sentence Embedding Model with 1B Training Pairs

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.

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.

Efficient training of language models to fill in the middle

BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning

arXiv:2608. 05104v1 Announce Type: new Abstract: Deep neural networks have shown impressive success in NLP tasks owing to their complex structure and huge number of edges.