[bugfix] fix reward_model

[Jintao-Huang]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @Jintao-Huang, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses a bug by refining the handling of the problem_type parameter, particularly for reward models and single-label classification tasks. The change ensures that the problem_type is correctly inferred as 'regression' when appropriate, improving the accuracy of model configuration. The corresponding documentation has also been updated to clearly explain this new behavior.

Highlights

• Default problem_type: The default value for the problem_type parameter has been changed from 'single_label_classification' to None in the argument definitions, allowing for dynamic assignment.
• Dynamic problem_type Assignment: The system now automatically sets problem_type to 'regression' for sequence classification tasks if the model is identified as a reward model (model_meta.is_reward) or if num_labels is 1. Otherwise, it defaults to 'single_label_classification' when problem_type is not explicitly provided.
• Documentation Updates: The documentation for problem_type in both Chinese and English has been updated to reflect the new default behavior and the logic for automatic assignment based on reward models or num_labels.

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[gemini-code-assist]

Code Review

This pull request correctly adjusts the default problem_type for reward models to 'regression'. The changes involve updating the default value in argument classes to None and adding logic to infer the correct problem_type during model initialization. The documentation has also been updated accordingly. The logic is sound and fixes the intended issue. I've added one minor suggestion to improve code clarity.

[gemini-code-assist]

For better readability and to make the logic more direct, you could refactor this block to avoid reassigning the problem_type variable and instead directly assign to model_info.config.problem_type within the conditional branches. This makes the flow of control clearer.

Suggested change

	if problem_type is None:
	if model_info.num_labels == 1 or model_meta.is_reward:
	problem_type = 'regression'
	else:
	problem_type = 'single_label_classification'
	model_info.config.problem_type = problem_type
	if problem_type is None:
	if model_info.num_labels == 1 or model_meta.is_reward:
	model_info.config.problem_type = 'regression'
	else:
	model_info.config.problem_type = 'single_label_classification'
	else:
	model_info.config.problem_type = problem_type