Embeddings Comparator

What is Embeddings Comparator ?

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.

Features

• 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.

How to use Embeddings Comparator ?

1. Prepare Your Data: Extract text from scanned documents or input your own text data.
2. Generate Embeddings: Use a preferred model to create embeddings for your data.
3. Upload Embeddings: Load the embeddings into the comparator tool.
4. Select Comparison Criteria: Choose models, metrics, and filters for your analysis.
5. Run Comparison: Execute the comparison and visualize the results.
6. Analyze and Export: Review the findings and export them for further use.

Frequently Asked Questions

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.