langtest.transform.fairness.MinGenderLLMEval#
- class MinGenderLLMEval#
Bases:
BaseFairness
Class for evaluating fairness based on minimum gender performance in question-answering tasks using a Language Model.
- alias_name#
Alias names for the evaluation method.
- Type:
List[str]
- supported_tasks#
Supported tasks for this evaluation method.
- Type:
List[str]
- transform(cls, test
str, data: List[Sample], params: Dict) -> List[MaxScoreSample]: Transforms data for evaluation.
- run(sample_list
List[MaxScoreSample], grouped_label: Dict[str, Tuple[List, List]], **kwargs) -> List[MaxScoreSample]: Runs the evaluation process.
- __init__()#
Methods
__init__
()async_run
(sample_list, model, **kwargs)Creates a task for the run method.
run
(sample_list, grouped_label, **kwargs)Runs the evaluation process using Language Model.
transform
(test, data, params)Transforms the data for evaluation.
Attributes
eval_model
test_types
- async classmethod async_run(sample_list: List[Sample], model: ModelAPI, **kwargs)#
Creates a task for the run method.
- Parameters:
sample_list (List[Sample]) – The input data to be transformed.
model (ModelAPI) – The model to be used for the computation.
- Returns:
The task for the run method.
- Return type:
asyncio.Task
- async static run(sample_list: List[MaxScoreSample], grouped_label, **kwargs) List[MaxScoreSample] #
Runs the evaluation process using Language Model.
- Parameters:
sample_list – The input data samples.
grouped_label – A dictionary containing grouped labels where each key corresponds to a test case and the value is a tuple containing true labels and predicted labels.
**kwargs – Additional keyword arguments.
- Returns:
The evaluated data samples.
- classmethod transform(test: str, data: List[Sample], params: Dict) List[MaxScoreSample] #
Transforms the data for evaluation.
- Parameters:
test (str) – The test alias name.
data (List[Sample]) – The data to be transformed.
params (Dict) – Parameters for transformation.
- Returns:
The transformed data samples.
- Return type:
List[MaxScoreSample]