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Release 1.00

November 3rd, 2025

This major release introduces image embedding support, improved data streaming efficiency, and enhanced caching performance monitoring, alongside multiple stability and documentation updates.

With the introduction of Image Embeddings, multiple core refactors, and the publication of the API Reference, BaseModel officially reaches version 1.00.

New features

  • Image embedding support
    Users can now add images, and BaseModel automatically generates image embeddings that integrate seamlessly with behavioral, text, and tabular data for complete multimodal modeling. Learn more in the Data & Feature Types guide.

  • Unix timestamp support
    Users can now use Unix timestamps directly in time-related functions for greater flexibility in data processing.

Improvements

  • Improved data streaming efficiency
    Reduced memory usage and increased performance for large datasets, resulting in smoother and faster data handling.

  • Revamped timezone handling
    Enhanced timestamp alignment across multiple data sources for consistent temporal comparisons.

  • Robust handling of missing numerical data
    More stable aggregation and event computations when numerical values are partially missing.

  • Optimized data transformations
    Improved efficiency when processing large data structures within pipelines.

  • Simplified async stream handling
    Streamlined background data operations for greater reliability and maintainability.

  • Improved query consistency
    More predictable and stable query behavior across data modules, enhancing reliability in data access.

  • Additional caching performance benchmarks
    Improved cache performance benchmarking across supported databases, enabling further optimization to achieve faster data retrieval and more consistent caching efficiency.

Fixes

  • Correct trainer loss logging
    Fixed an issue where train_loss_epoch could log as NaN during certain training configurations.

  • Revised time series handling
    Corrected how time series model manages series of counts, ensuring accurate scaling and alignment.

  • Improved checkpoint reliability
    Fixed a checkpointing issue that could prevent model state from saving correctly during long training sessions.

  • Minor bugs and code maintenance
    Fixed various small issues and improved overall code stability and maintainability.

Documentation

  • Comprehensive API Reference
    Users can now access a complete API Reference section describing all classes and functions available in BaseModel.

  • New guides and FAQ
    Added a new FAQ section in the About BaseModel block and a detailed guide on Data Types & Features, explaining how different data types are transformed into model features and how users can influence this process, including text, image, and time-series features.