Hi,I'd like to work on the following issues:

[Sushen Yadav]

Adding the issues I want to work on and some initial doubts:

• issue
  • Doubt/line of thought

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• Add a HugginFace dataloader to apply IReRa to any multi-label classification task
  - Add dataloader such that loader.py logic is changed to accomodate HuggingFace dataloader
• Add a cli argument parsing class
  - Make the argparse stuff into a class.
• Create a config for the Optimizer class
  - use dataclasses for defining config.
• Make the Optimizer class agnostic of the implementation details of the program that gets optimized, so it can define a high-level strategy which gets compiled across many programs
  - generalise methods and abstract away methods so that strategy logic can be implemented at a high level.
• Track amount of student and teacher calls during optimization so system-cost can be compared.
  - simply track calls to llm
• Make src/programs/retriever.py more efficient
  - work on logic to optimise retrieving of dataset
• Track intermediate pipeline steps and log these traces for debugging
  - track each step in IReRa and log it.
• Use logging instead of print-statements, and dump logs to experiment file
  - Define a logger in loader and experiments to log the states
• Control seeds in LM calls and data loaders
  - Add seed to optimiser.
  I hope that i am thinking in the right direction, I am intrigued by the IReRa style and want to help the project get better.