FFM-Embedding-v2

FFM-Embedding is a vector embedding model that enhances semantic search. It supports Traditional Chinese and eight other languages. v2 has been fully upgraded in terms of text length and vector dimensions, making information retrieval faster and semantically more accurate. v2.1 strengthens legal texts and enhances the recognition of legal terms and context.

FFM-Embedding-v2

★ A text vectorization tool that converts complex text data into vectors. ★
★ Used for information retrieval, RAG (Rapid Augmentation) vector construction and searching ★
★ Focuses on Traditional Chinese vector graphics and supports 8 other languages (English/German/French/Italian/Portuguese/Hindu/Spanish/Thai) ★
★ Significantly upgraded to 8K text length, improving search efficiency and enabling rapid processing of large amounts of information★
★ Customizable vector dimensions, up to 2048, flexibly adjustable for more accurate semantic search ★
★ OpenAI compatible; use OpenAI's Embedding API for quick model switching. ★
★ New v2.1: Enhanced legal texts, improved recognition of Traditional Chinese legal terminology and context ★

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The benchmark surpasses OpenAI's best Traditional Chinese vector model.

Applicable Scenarios

Complete complex natural language tasks

Embeddings technology can capture the meaning between these languages and is applicable to text classification, question-answering systems, semantic retrieval, anomaly detection, and reordering.

Supports Traditional Chinese and multiple languages

FFM-Embedding-v2 focuses on Traditional Chinese vectors while supporting 8 other languages, making vector creation and searching between Traditional Chinese and multiple languages more accurate.

RAG (Extract Enhancement Generation)

Vector models are crucial for RAG applications. Embedding-v2 supports 8K text length and 2048 vector dimensions, making vector construction and information retrieval faster and more accurate!

Legal document processing

Automating legal document processing, such as contract review, summarization, and Q&A, saves labor costs, improves efficiency, and reduces legal risks faced by enterprises.

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