Training and Finetuning Reranker Models with Sentence Transformers

Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers

Training and Finetuning Embedding Models with Sentence Transformers

Multimodal Embedding & Reranker Models with Sentence Transformers

Training and Finetuning Sparse Embedding Models with Sentence Transformers

Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

Train and Fine-Tune Sentence Transformers 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.

Train 400x faster Static Embedding Models with Sentence Transformers

A Reranker for Orchestrating Heterogeneous Speech and Text Retrievers

The paper introduces STeReO, a reranker that orchestrates speech and text retrievers to aggregate evidence from heterogeneous databases. It addresses the scarcity of training data by curating a dataset of queries, mixed-modality evidence, and relevance rankings, then trains and evaluates the reranker in both single- and mixed-modality settings. Results show that STeReO effectively selects the most relevant evidence, leading to significant improvements in downstream question‑answering performance.

RankLLM: Weighted Ranking of LLMs by Quantifying Question Difficulty

arXiv:2602. 12424v2 Announce Type: replace-cross Abstract: Benchmarks establish a standardized evaluation framework to systematically assess the performance of large language models (LLMs), facilitating objective comparisons and driving advancements in the field.

Train a Sentence Embedding Model with 1B Training Pairs

A Self Consistency Based Reranking for Narrative Question Answering

arXiv:2606. 15741v1 Announce Type: cross Abstract: Narrative question answering (NQA) is a challenging task in natural language processing that requires models to understand long textual contexts, capture relationships across events, and generate coherent responses.