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Enhance Multilingual Search with Cohere's Rerank-Multilingual-v2.0 Model

Tal Peretz

Sep 28, 2024 — 1 min read

In today's globalized world, offering accurate and efficient search results in multiple languages is crucial. Cohere's Rerank-Multilingual-v2.0 model is designed to address this need, providing advanced reranking capabilities for documents in various languages.

Model Description

The rerank-multilingual-v2.0 model focuses on reranking non-English documents, supporting the same languages as the embed-multilingual-v3.0 model. It leverages a cross-encoder approach to assess the semantic relevance between a query and each document, reordering them based on relevance scores.

Technical Details

With a context length of 512 tokens, the model can handle longer documents through automatic chunking. It supports reranking for both semi-structured data (JSON) and full strings, making it versatile for various data formats.

Multilingual Support

The rerank-multilingual-v2.0 model provides robust support across multiple languages, helping global organizations enhance search experiences in diverse regions.

Integration and Usage

This model can be seamlessly integrated into systems like OpenSearch, Weaviate, and Amazon SageMaker JumpStart. For instance, in OpenSearch, a reranking pipeline can be created to reorder search results based on their relevance to the query.

In Weaviate, you can configure a collection to utilize the rerank-multilingual-v2.0 model for reranking search results, ensuring the most relevant results are presented to users.

Performance

While not as fast as the newer Cohere Rerank 3 Nimble models, the rerank-multilingual-v2.0 model delivers high accuracy in reranking documents, making it a reliable choice for enhancing search accuracy in multilingual contexts.

Parameters and Configuration

Users can specify parameters such as the query, the list of documents, and the number of top-ranked results to return (top_n). Additionally, users can configure which fields of the documents should be considered for reranking.

Overall, the rerank-multilingual-v2.0 model is a powerful tool for improving search accuracy in multilingual environments, even if it doesn’t match the speed of the latest models in the series.

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