Compare different Embeddings
OCR that extract text from image of hindi and english
Identify and extract key entities from text
Multimodal retrieval using llamaindex/vdr-2b-multi-v1
Ask questions about a document and get answers
Analyze documents to extract and structure text
Find information using text queries
Parse documents to extract structured information
Find relevant text chunks from documents based on queries
Extract handwritten text from images
Extract text from document images
Analyze legal PDFs and answer questions
Process text to extract entities and details
Embeddings Comparator is a tool designed to compare different embeddings, enabling users to analyze and understand how various models represent data. It is particularly useful for searching and summarizing documents using embeddings, making it an essential resource for tasks involving extracting text from scanned documents. By leveraging embeddings, the tool provides insights into how different models perform and represent textual information.
• Multi-Model Support: Compare embeddings from various models like BERT, RoBERTa, and more. • Visualization Tools: Generate plots to understand embedding distributions and clusters. • Distance Metrics: Calculate similarity using cosine, Euclidean, and other distance metrics. • Batch Processing: Analyze multiple embeddings at once for efficient comparison. • Custom Filters: Apply filters to focus on specific parts of the data. • Export Results: Save comparisons in formats like CSV, JSON, or PDF for further analysis.
What formats does Embeddings Comparator support for input?
Embeddings Comparator supports JSON, CSV, and numpy array formats for input.
Can I customize the distance metrics used for comparison?
Yes, you can customize the distance metrics by selecting from predefined options or defining your own.
What are typical use cases for Embeddings Comparator?
Common use cases include comparing model performance, analyzing document similarity, and optimizing embedding models for specific tasks.