Dev

[Oufattole]

Summary by CodeRabbit

• New Features

  • Introduced new configuration files for various models (e.g., mimiciv.yaml, ebcl.yaml, ocp.yaml, value_forecasting.yaml) enhancing functionality and adaptability.
  • Added support for text and triplet encoding with new encoder configurations.
  • Implemented a range of new modules for token forecasting and value forecasting tasks.

• Improvements

  • Updated the README.md to reflect the current status of project tasks.
  • Enhanced dataset handling and processing in data modules with dynamic references and improved collation methods.
  • Improved clarity and usability in configuration files with updated parameters and structured setup processes.

• Bug Fixes

  • Addressed issues with dataset naming conventions and improved data integrity checks during processing.

• Chores

  • Cleaned up dependency management in pyproject.toml to streamline the project's requirements.

[coderabbitai[bot]]

[!CAUTION]

Review failed

The pull request is closed.

Walkthrough

This update introduces significant enhancements across various configuration files and model components in the meds_torch framework. Key changes include refined dataset naming conventions, the introduction of new models and input encoders, adjustments to dependency management, and improvements in data processing functionalities. These updates aim to streamline configurations, enhance modularity, and improve the overall architecture for better performance and maintainability.

Changes

Files	Change Summary
.gitignore, README.md	Added log file ignore rule and updated project task statuses for clarity.
configs/data/*.yaml, configs/model/*.yaml	Modified dataset names, added new parameters, and refined model configurations for flexibility.
pyproject.toml	Updated dependencies, removing and adding packages for streamlined management.
src/meds_torch/data/components/*.py	Enhanced data handling methods, including new tokenization and collation functionalities.
src/meds_torch/models/*.py	Refactored models to inherit from BaseModule, updated keys for embeddings, and streamlined logic.
src/meds_torch/input_encoder/*.py	Introduced new encoders and refined embedding functionalities for better text and code processing.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant Config
    participant Model
    participant DataLoader

    User->>+Config: Update configuration
    Config->>Model: Load new model settings
    Model->>DataLoader: Request data
    DataLoader->>Model: Provide processed data
    Model->>User: Output results

🐰 In the meadow, hopping with glee,
A tweak in the code, oh how happy we’ll be!
With models and datasets, all tidy and bright,
We’ll train and we’ll learn, from morning till night.
So here’s to the changes, fresh paths we will roam,
In the world of data, we’ve found a new home! 🌱✨

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