10 actions in 10 pieces of software · ranked by words, meaning and measured trust · 576 ms
modelrecord40,596 downloads/month
- sentence-similarity
Task: sentence-similarity
- sentence-similarity
modelrecord76,484 downloads/month
- feature-extraction
Task: feature-extraction
- feature-extraction
modelrecord43,919 downloads/month
- visual-document-retrieval
Task: visual-document-retrieval
- visual-document-retrieval
modelrecord
- beautyyuyanli/multilingual-e5-small
multilingual-e5-small: A multi-language text embedding model
- beautyyuyanli/multilingual-e5-small
modelrecord918,560 downloads/month
- text-ranking
Task: text-ranking
- text-ranking
modelrecord
- ibm-granite/granite-embedding-small-english-r2
Granite-embedding-small-english-r2 is a 47M parameter dense biencoder embedding model from the Granite Embeddings collection that can be used to generate high quality text embeddings.
- ibm-granite/granite-embedding-small-english-r2
- 7Jina AI MCP Serverfound in code, not yet checkedno passport yet
- embed_text
Generate vector embeddings for text or images. Powered by Jina Embeddings API. Useful for semantic search, clustering, and retrieval.
- embed_text
MCP serverrecord
- classify_text
Classify texts into user-defined labels using Jina embeddings. Use this when you need to categorize, tag, or sort text content into predefined categories. Perfect for sentiment analysis, topic classification, content moderation, or any text categorization task.
- classify_text
- 9jina-localfound in code, not yet checkedno passport yet
MCP serverlorenzozanee/jina-local
- embeddings
- 10Jina AI MCP Serverfound in code, not yet checkedno passport yet
- embeddings
Creates an embedding vector representing the input text.
- embeddings
Labels are the instruments' verdict and are never changed by ranking. Agents and robots get this same answer as JSON at /api/v1/find?q=…&answer=1 and through the MCP server.