Choose completion function for evaluation of modelgraded evals

[LoryPack]

The documentation for modelgraded evals says:

In general, the evaluation model and the model being evaluated don't have to be the same, though we will assume that they are here for ease of explanation.

However, the current code did not allow to do this. Indeed, classify.py:ModelBasedClassify had the following line:

evals/evals/elsuite/modelgraded/classify.py

Line 30 in 7400b0e

self.eval_completion_fn = self.completion_fns[-1]

I have now added an additional optional parameter to ModelBasedClassify which allows to pick a different model for the evaluation from the evaluated one. The model for the evaluation has to be specified from the .yaml file, for instance with:

<my_eval>:
  id: <my_eval>.test.v1
  metrics:
  - accuracy
<my_eval>.test.v1:
  args:
    eval_type: cot_classify
    modelgraded_spec: fact
    samples_jsonl: ../registry/data/<my_eval>/samples.jsonl
    eval_completion_fn: gpt-3.5-turbo
  class: evals.elsuite.modelgraded.classify:ModelBasedClassify

Final checklist 👀

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