Cohere Embed Multilingual Light 3

The cohere.embed-multilingual-light-v3.0 model transforms each phrase, sentence, or paragraph that you input, into an array.

You can use the embedding models for finding similarity in phrases that are similar in context or category. Embeddings are typically stored in a vector database. Embeddings are mostly used for semantic searches where the search function focuses on the meaning of the text that it's searching through rather than finding results based on keywords.

Available in This Region

US Midwest (Chicago)

Key Features

Light models are smaller and faster than the original models.
English or multilingual.
Model creates a 384-dimensional vector for each embedding.
Maximum 96 sentences per run.
Maximum 512 tokens for each input.
Best for use cases when:
- Instead of English, the documents are written in one of the supported languages.
- The documents are written in more than one language and those languages are one of the supported languages.

Dedicated AI Cluster for the Model

To reach a model through a dedicated AI cluster in any listed region, you must create an endpoint for that model on a dedicated AI cluster. For the cluster unit size that matches this model, see the following table.


Base Model	Fine-Tuning Cluster	Hosting Cluster	Pricing Page Information	Request Cluster Limit Increase
Model Name: Cohere Embed Multilingual Light 3 OCI Model Name: `cohere.embed-multilingual-light-v3.0`	Not available for fine-tuning	Unit Size: Embed Cohere Required Units: 1	Pricing Page Product Name: Embed Cohere - Dedicated For Hosting, Multiply the Unit Price: x1	Limit Name: `dedicated-unit-embed-cohere-count` For Hosting, Request Limit Increase by: 1

Tip

The Cohere Embed Multilingual Light 3 model has both on-demand and dedicated AI cluster options. For the on-demand option, you don't need clusters and you can reach the model in the Console playground or through the API.
If you don't have enough cluster limits in your tenancy for hosting an Embed model on a dedicated AI cluster, request the dedicated-unit-embed-cohere-count limit to increase by 1.

Release and Retirement Dates


Model	Release Date	On-Demand Retirement Date	Dedicated Mode Retirement Date
`cohere.embed-multilingual-light-v3.0`	2024-02-07	At least one month after the release of the 1^st replacement model.	At least 6 months after the release of the 1^st replacement model.

Important

For a list of all model time lines and retirement details, see Retiring the Models.

Embedding Model Parameter

When using the embedding models, you can get a different output by changing the following parameter.

Truncate: Whether to truncate the start or end tokens in a sentence, when that sentence exceeds the maximum number of allowed tokens. For example, a sentence has 516 tokens, but the maximum token size is 512. If you select to truncate the end, the last 4 tokens of that sentence are cut off.