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Configuration Parameters Documentation

This README describes parameter categories and how to override these parameters at the various (finetuning) stages of the experiment.

Argument Categories

Parameters in each configuration yaml file are divided into six categories / groups. You can find more details in the respective dataclasses inside utils/arguments.py:

  1. training_arguments: see ModelTrainingArguments in utils/arguments.py.
  2. data_arguments: see DataTrainingArguments.
  3. model_arguments: see ModelArguments.
  4. experiment_arguments: see CommonExperimentArguments.
  5. define_experiment_arguments: see DefineExperimentDataArguments.
  6. numeric_experiment_arguments: see NumericExperimentDataArguments.

Argument Overrides

For each stage of finetuning, you can override the parameters above using the following parameter groups:

  1. first_stage_arguments: This is an overriding dictionary for stage one of model training/finetuning, accepting parameters from different argument groups.
  2. second_stage_arguments: This overriding dictionary is meant for stage two of finetuning.
  3. third_stage_arguments: This overriding dictionary is used for stage three.

For example, you can set the parameter num_train_epochs (normally part of training_arguments) to 20 for the first stage and 10 for the second stage (see how this is done in configs/current_experiment.yaml).

The number of finetuning stages (1, 2, or 3) can be set using the n_stages parameter in the experiment_arguments parameter group.