Cohere Embed Multilingual 3
The cohere.embed-multilingual-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 These Regions
- Brazil East (Sao Paulo)
- Germany Central (Frankfurt)
- Japan Central (Osaka)
- UAE East (Dubai)
- UK South (London)
- US Midwest (Chicago)
Key Features
- Works for both English and multilingual.
- Model creates a 1,024-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 |
|---|---|---|---|---|
|
Not available for fine-tuning |
|
|
|
-
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-countlimit to increase by 1.
Release and Retirement Dates
| Model | Release Date | On-Demand Retirement Date | Dedicated Mode Retirement Date |
|---|---|---|---|
cohere.embed-multilingual-v3.0
|
2024-02-07 | At least one month after the release of the 1st replacement model. | At least 6 months after the release of the 1st replacement model. |
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.