Cohere-embed-multilingual-v3.0
| Property | Value |
|---|---|
| Author | Cohere |
| Training Data | ~1B English pairs, ~0.5B Non-English pairs |
| Language Support | 100+ languages |
| Deployment Options | Cohere API, AWS SageMaker, Private Deployment |
What is Cohere-embed-multilingual-v3.0?
Cohere-embed-multilingual-v3.0 is a state-of-the-art multilingual embedding model designed for semantic search, clustering, and classification tasks. It represents the third generation of Cohere's embedding technology, trained on an extensive dataset of nearly 1.5 billion training pairs across multiple languages.
Implementation Details
The model can be accessed through multiple deployment options, including the Cohere API, AWS SageMaker for private cloud deployment, and custom private deployments. It features specialized encoding modes for search queries and documents, optimizing performance for specific use cases.
- Comprehensive multilingual support with training across 100+ languages
- Specialized encoding types for search optimization (search_query and search_document)
- Low-latency performance (as low as 5ms for query encoding on AWS SageMaker)
- Extensive benchmark results across multiple tasks including STS, clustering, and retrieval
Core Capabilities
- Semantic Search with strong NDCG@10 scores (up to 88.9 on QuoraRetrieval)
- Text Classification with high accuracy (95.6% on AmazonPolarity)
- Clustering with robust v_measure scores (up to 68.1 on StackExchangeClustering)
- Cross-lingual Understanding demonstrated through various benchmarks
Frequently Asked Questions
Q: What makes this model unique?
The model's extensive multilingual training (100+ languages) combined with specialized encoding modes for search makes it particularly effective for cross-lingual applications and semantic search tasks. It demonstrates strong performance across a wide range of benchmarks in the MTEB suite.
Q: What are the recommended use cases?
The model excels in semantic search, document clustering, text classification, and similarity scoring tasks. It's particularly well-suited for multilingual applications and can be deployed in various environments from API calls to private cloud deployments.
