Introduction to Inspectus
Inspectus is a powerful and user-friendly visualization tool designed specifically for machine learning enthusiasts and professionals. It allows users to visualize complex mechanisms such as attention processes in deep learning models effortlessly, directly within Jupyter notebooks, leveraging a straightforward Python API.
Key Features
Attention Visualization
Attention mechanisms are crucial in understanding how deep learning models focus on different parts of input sequences. Inspectus provides comprehensive visualization options to analyze these attention processes, which include:
- Attention Matrix: This component highlights the focus between tokens, showing the attention scores during model processing.
- Query and Key Token Heatmaps: These visualizations sum up attention scores for query-key token interactions, helping users to see which tokens are most attended to.
- Dimension Heatmap: Presents an overview of attention scores across different model dimensions, such as layers and heads, normalized for easy interpretation.
With these tools, users can select, deselect, and explore different tokens to better comprehend model behavior.
Token Visualization
Inspectus also offers token visualization capabilities, allowing users to explore various metrics associated with tokens. Users can choose different metrics and even add additional information for each token, all viewable through an intuitive interface.
Distribution Plot
This feature provides a way to visualize data distributions through comprehensive plots that reveal trends and identify outliers. The distribution plot is especially useful for examining data over time and helps in understanding the presence of anomalies or irregularities in the dataset.
How to Use Inspectus
The tool is easy to set up and use. Simply install it via pip:
pip install inspectus
For basic usage, users can import the library and call its visualization functions with their data. For example, to visualize an attention map:
import inspectus
inspectus.attention(attn, tokens)
Inspectus also supports more advanced scenarios, such as dealing with different query and key tokens.
Tutorials and Examples
Inspectus provides tutorials that guide users through its functions with real-life examples, such as using models from Huggingface or creating custom attention maps. These tutorials make it simple to get started with Inspectus and to explore its full potential.
Development and Citing
For those looking to delve deeper or contribute to the project, development setup documentation is readily available. If Inspectus is utilized for academic research, proper citation is possible with the provided BibTeX entry.
In summary, Inspectus is an invaluable tool for visualizing and analyzing machine learning processes, offering an intuitive interface and a broad range of capabilities suitable for both newcomers and seasoned data scientists.