Add vectors to Hub datasets and do in memory vector search.
Answer questions about images in natural language
Ask questions about images
Ask questions about images and get detailed answers
Ask questions about text or images
Monitor floods in West Bengal in real-time
Answer questions based on images and text
Explore interactive maps of textual data
Generate animated Voronoi patterns as cloth
Fetch and display crawler health data
Generate answers by combining image and text inputs
Browse and compare language model leaderboards
PaliGemma2 LoRA finetuned on VQAv2
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.