Hugging Face's official blog announced that Cohere, the well-known enterprise AI research and development company, has officially joined Hugging Face's…
When building RAG (Retrieval-Augmented Generation) systems, relying solely on vector embeddings for semantic search is often not precise enough. To improve…
### What Are Static Embeddings? In today's NLP landscape, Transformer-based embedding models (such as BERT and mE5) have become the mainstream, as they…
Hugging Face has recently released a new Visual Document Retrieval (VDR) model — **VDR-2B-multilingual**. This technology marks a formal transition in document…
Despite the recent dominance of generative decoder models (such as GPT and Llama), encoder-only models (such as BERT) remain indispensable behind the scenes…
This case study provides a detailed account of how non-profit organization Digital Green, with support from Hugging Face's Expert Support team, optimized its…
XLSCOUT, an intellectual property (IP) and patent analysis platform, has announced the launch of its next-generation patent-specific embedding model…
Hugging Face has announced the launch of a new Hugging Face Embedding container (Deep Learning Container, DLC) designed specifically for Amazon SageMaker. This…
The official Hugging Face blog introduces a major update to the Sentence Transformers library (v3.0), centered on the launch of the new…
As enterprise demand for Retrieval-Augmented Generation (RAG) technology surges, how to maintain high performance while controlling hardware costs has become…
As RAG (Retrieval-Augmented Generation) and semantic search have become widespread, the maintenance costs of vector databases — especially RAM overhead — have…
When building Retrieval-Augmented Generation (RAG) systems, converting large volumes of text into embeddings (vectors) is an indispensable and computationally…
This article provides an in-depth introduction to Matryoshka Representation Learning (MRL), also known as Matryoshka embedding models. Traditional embedding…
Hugging Face has partnered with Patronus AI — a startup focused on LLM evaluation and defense — to officially launch the **Enterprise Scenarios Leaderboard**…
While large language models (LLMs) have demonstrated remarkable generative capabilities across many domains, "hallucination" — where a model confidently…
In the open-source AI community, the Hugging Face Open LLM Leaderboard serves as an important benchmark for evaluating model capabilities. However, many…
This technical blog post from Replicate provides a detailed introduction to using the open-source BGE (BAAI General Embedding) model for efficient, low-cost…
As large language models (LLMs) and Retrieval-Augmented Generation (RAG) technology become increasingly widespread, embedding models have become an…
This technical blog post from Replicate provides a detailed walkthrough of how to build a basic Retrieval-Augmented Generation (RAG) application from scratch…
In the field of natural language processing (NLP), text embeddings — the technique of converting text into real-valued vectors — are a foundational technology…
This is a practical guide authored by Hugging Face, aimed at teaching developers how to train and fine-tune Sentence Transformers models to generate…
This classic blog post from Hugging Face is an essential introductory guide for understanding vector embeddings — the foundational technology underlying modern…
This classic Hugging Face blog post (co-authored by Sentence-Transformers creator Nils Reimers and others) provides a detailed account of how to train…
Retrieval-Augmented Generation (RAG) is a powerful architecture that combines a "retriever" with a "generator." It enables language models to dynamically…