Model overview
Cohere-embed-multilingual-v3.0 converts text into numerical embeddings that machines can understand and compare. This model handles text across 100+ languages, making it useful for applications that work with international content. The model was trained on nearly 1 billion English training pairs and approximately 500 million non-English training pairs. You can access it through the Cohere API, deploy it on AWS SageMaker, or run it privately on your own infrastructure. The Cohere creator profile provides additional information about the developers.
Model inputs and outputs
The model takes text as input and produces vector embeddings as output. Different input types trigger different optimization paths—documents receive one type of encoding while queries receive another, which improves search relevance.
Inputs
- Text documents - any passage or document you want to embed
- Search queries - questions or search terms you want to match against documents
- Input type specification - parameter indicating whether input is a document or query
Outputs
- Embedding vectors - numerical representations of your text that capture semantic meaning
- Similarity scores - numerical values showing how closely related different texts are
Capabilities
The model performs semantic search by comparing the meaning of queries against document collections. It ranks documents by relevance to find the most similar results. The model maintains quality across diverse languages, supporting everything from major languages like Spanish and Mandarin to less common languages. Query encoding runs with latencies as low as 5 milliseconds when deployed on AWS SageMaker, enabling real-time applications.
What can I use it for?
Build search systems that understand meaning rather than just matching keywords. Use it to power recommendation engines that suggest related content to users. Create chatbot systems that retrieve relevant documents to answer questions. Implement content moderation by comparing user submissions against flagged examples. Organizations can use embeddings to cluster similar documents, detect duplicates, or find anomalies in large text collections. The multilingual support means you can build global search applications without maintaining separate models for each language.
Things to try
Experiment with the input type parameter—changing between search_document and search_query modes produces different embeddings optimized for each task, potentially improving your results. Test the model on your specific domain to see how well it handles technical terms or specialized vocabulary in your industry. Try combining embeddings with simple dot product similarity calculations to implement semantic search with just a few lines of code. Deploy the model privately if you handle sensitive information, avoiding the need to send data to external APIs. Compare results across different languages to understand how the multilingual training translates to performance in your target markets.
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