Introduction to WizMap
WizMap is an innovative and powerful tool designed for exploring and interpreting large machine learning embeddings directly in your browser. Embeddings are numerical representations of data that are used in advanced machine learning, and understanding them can be complex. WizMap simplifies this task with its user-friendly, interactive platform, enabling users to delve into large data sets effortlessly.
Key Features of WizMap
-
Scalable Exploration: WizMap allows users to navigate through millions of embedding points, making it ideal for handling large-scale machine learning models.
-
Multi-resolution Summaries: Users can access summaries of embeddings at different resolutions, making it easy to glean insights at varying levels of detail.
-
Fast Embedding Search: Quickly find and identify specific embedding points with the tool's efficient search capabilities.
-
Support for Multimodal Data: WizMap can interpret data from multiple sources, including both text and images, providing a comprehensive view of complex data sets.
-
Animated Evolution: The tool offers animated representations of embedding changes over time, allowing users to visualize how data evolves.
-
Integration with Notebooks: It supports integration with various computational notebooks like Jupyter, Colab, and VS Code, which are popular among data scientists and researchers.
-
Sharable URLs: Users can easily share their embedding maps through unique URLs, facilitating collaboration and discussion.
Gallery and Examples
WizMap showcases several data sets in its gallery, which include diverse examples like:
-
DiffusionDB: Contains 1.8 million text entries and 1.8 million corresponding images, made accessible through CLIP embeddings.
-
ACL Paper Abstracts: Features summaries of 63k text abstracts, using the all-MiniLM-L6-v2 embedding model.
-
IMDB Review Comments: Offers insights into 25k text reviews via embedding interpretation.
Getting Started with WizMap
To use WizMap, you can clone the repository, install necessary dependencies, and run the application. Here's a quick guide to get started:
-
Clone the repository:
git clone [email protected]:poloclub/wizmap.git -
Install dependencies:
npm install -
Run WizMap:
npm run dev -
Access it by navigating to localhost:3000 in your web browser.
For those wanting to use their own embeddings, installation of the wizmap Python library and following a simple setup in computational notebooks will have them up and running in no time.
Sharing Your WizMap
WizMap makes it easy to share your visualized data with others by generating a unique URL for each embedding map. This feature supports seamless collaboration and data sharing, as seen with examples like IMDB embeddings provided in the project.
Credits and More Information
Created by Jay Wang, Fred Hohman, and Polo Chau, WizMap's development is backed by academic research, as detailed in their research paper. The project is open source and available under the MIT License.
For further queries or support, users are encouraged to reach out by opening an issue on their GitHub page or contacting the creators directly through the provided links.
WizMap is a groundbreaking tool that continues to make strides in simplifying the analysis of machine learning embeddings, empowering researchers and data enthusiasts alike.