Compare different Embeddings
Extract text from multilingual invoices
Extract text from documents or images
Search documents and retrieve relevant chunks
Extract text from images with OCR
Find similar sentences in text using search query
Traditional OCR 1.0 on PDF/image files returning text/PDF
Find relevant legal documents for your query
Extract handwritten text from images
Convert images with text to searchable documents
Find information using text queries
Query deep learning documents to get answers
Parse and extract information from documents
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.