Add vectors to Hub datasets and do in memory vector search.
Search for movie/show reviews
Visual QA
Create visual diagrams and flowcharts easily
Ask questions about images
Ask questions about images to get answers
Display a loading spinner while preparing a space
Monitor floods in West Bengal in real-time
demo of batch processing with moondream
Rank images based on text similarity
Try PaliGemma on document understanding tasks
Transcribe manga chapters with character names
Select a city to view its map
Vectorsearch Hub Datasets is a tool designed to enhance datasets on Hugging Face Hub by enabling vector-based search capabilities. It allows users to add vector embeddings to their datasets and perform in-memory vector similarity searches, making it easier to find relevant data points within large datasets. This tool is particularly useful for applications that require efficient and accurate visual question answering (Visual QA) tasks.
What does vectorization mean in this context?
Vectorization refers to converting data (e.g., text, images) into numerical vector representations, enabling similarity-based searches.
What types of datasets are supported?
Vectorsearch Hub Datasets primarily supports text-based datasets but can be extended to other data types with appropriate vectorization.
How do I ensure data privacy?
Data remains on Hugging Face Hub, and Vectorsearch Hub Datasets only processes data in-memory during search operations.