Introducing Cohere's Embed-English-Light-v3.0: Efficient and Powerful Text Embeddings

The Cohere Embed-English-Light-v3.0 model is a recent addition to Cohere's suite of embedding models, engineered for efficient text embeddings. It's a leaner, faster option tailored for English text, making it an ideal choice for applications where speed and lower-dimensional embeddings are paramount.

Dimensions and Performance

Boasting 384 dimensions, this model is more lightweight compared to the full-size Cohere Embed-English-v3.0 model, which has 1024 dimensions. Despite its smaller size, it maintains close performance, making it a viable option for many applications.

Usage

The model can be seamlessly integrated via the Cohere API, AWS Bedrock, or private deployments:

• API Integration: Use the Cohere SDK or AWS SDKs to incorporate the model into your applications.
• Input Types: Specify input_type="search_document" for embedding passages/documents and input_type="search_query" for embedding queries.

Technical Details

Here are some critical technical aspects of the model:

• Max Input Tokens: Supports up to 512 input tokens.
• Similarity Metric: Utilizes cosine or dot product similarity metrics.
• Evaluation Performance: Performs well on benchmarks like MTEB and BEIR, though slightly lower than the full-size model.

Deployment

The model can be deployed in several ways:

• Cohere API: Interact with the model using the Cohere SDK. Install the SDK via pip install -U cohere and obtain an API key from Cohere.
• AWS Bedrock: Available through Amazon Bedrock, allowing integration via the Bedrock API or AWS SDKs.
• Private Deployment: Deploy using AWS SageMaker or on your own hardware.

Applications

The Cohere Embed-English-Light-v3.0 model is perfect for applications requiring text embeddings, such as semantic search, where short queries need to return medium-length passages of text (1-2 paragraphs). It can generate vector embeddings, which can be stored in vector databases like Zilliz Cloud or Pinecone for efficient similarity searches.

In summary, this model provides a balance of performance and efficiency, making it a valuable tool for developers and data scientists looking to implement fast and effective text embedding solutions.