Introducing Command R+: Our new, most powerful model in the Command R family.
embeddings
For ML teams looking to build their own text analysis applications, Embeddings offers high-performance and accuracy in English and 100+ languages.
embeddings
For ML teams looking to build their own text analysis applications, Embeddings offers high-performance and accuracy in English and 100+ languages.
embeddings
For ML teams looking to build their own text analysis applications, Embeddings offers high-performance and accuracy in English and 100+ languages.
Semantic search
Topic modeling
Recommendations
Multilingual Embeddings
"It's next to impossible to gain access to Language AI and the experts building the technology. That’s why working with Cohere has been such a great experience. Anytime we have a new idea, their incredible team works with us to drive projects forward."
Cohere’s Embed model leads the industry in accuracy and performance, and works well with noisy datasets
Over 100 languages are supported, so the same topics, products and issues are identified the same way in each
Cohere Embed supports data compression, reducing storage and compute requirements
Cohere models can be accessed through a SaaS API, on cloud services (e.g. OCI, AWS SageMaker, Bedrock) and soon through private deployments (VPC and on-premise)
Cohere’s Embed model leads the industry in accuracy and performance, and works well with noisy datasets
Over 100 languages are supported, so the same topics, products and issues are identified the same way in each
Cohere Embed supports data compression, reducing storage and compute requirements
Cohere models can be accessed through a SaaS API, on cloud services (e.g. OCI, AWS SageMaker, Bedrock) and soon through private deployments (VPC and on-premise)
Simple APIs, powerful results
No matter your level of experience with ML/AI, the Cohere Platform makes it easy to classify text in your applications.
1import cohere
2co = cohere.Client('{apiKey}')
3
4faq_questions=[
5 "How much is a burger?",
6 "When do you close?",
7 "What are the hours",
8 "Do you have vegan options",
9 "What is the closest route"]
10
11response=co.embed(texts=faq_questions, input_type="search_query", model="embed-english-v3.0")
12print('Embeddings: {}'.format(response.embeddings))
Get started with Cohere today!
Reach out to us and let’s discuss your embedding needs.