New BGE Vector Models Set SOTA in Code and Multimodal Retrieval – What Makes Them So Powerful?

Three newly released BGE vector models—BGE‑Code‑v1, BGE‑VL‑v1.5, and BGE‑VL‑Screenshot—deliver state‑of‑the‑art performance on code, multimodal, and visual document retrieval benchmarks, are open‑source on Hugging Face and GitHub, and aim to boost retrieval‑augmented applications across languages and modalities.

AI modelsBGEVector Retrieval

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New BGE Vector Models Set SOTA in Code and Multimodal Retrieval – What Makes Them So Powerful?

Zhihu Tech Column

Oct 10, 2024 · Artificial Intelligence

Massive Multi-Label Text Classification via Semantic Retrieval and Large AI Model

This article presents a method for massive multi-label text classification on Zhihu content by combining a semantic retrieval model with a proprietary large AI model, detailing the challenges of large label spaces, model architecture, loss optimization, and experimental results showing significant accuracy gains.

BGElarge language modelmulti-label classification

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Massive Multi-Label Text Classification via Semantic Retrieval and Large AI Model

AI Large Model Application Practice

Jun 17, 2024 · Artificial Intelligence

Boost Your RAG Pipeline with Cohere and BGE Rerank Models

This guide explains why post‑retrieval reranking is essential for Retrieval‑Augmented Generation, compares the commercial Cohere Rerank service with the open‑source bge‑reranker‑large model, and provides step‑by‑step code for integrating both into LlamaIndex pipelines, including a custom TEI‑based processor.

BGECohereLlamaIndex

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Boost Your RAG Pipeline with Cohere and BGE Rerank Models