langchain.evaluation.exact_match.base.ExactMatchStringEvaluator¶
class langchain.evaluation.exact_match.base.ExactMatchStringEvaluator(*, ignore_case: bool = False, ignore_punctuation: bool = False, ignore_numbers: bool = False, **kwargs: Any)[source]¶
Compute an exact match between the prediction and the reference.
Examples
>>> evaluator = ExactMatchChain()
>>> evaluator.evaluate_strings(
        prediction="Mindy is the CTO",
        reference="Mindy is the CTO",
    )  # This will return {'score': 1.0}
>>> evaluator.evaluate_strings(
        prediction="Mindy is the CTO",
        reference="Mindy is the CEO",
    )  # This will return {'score': 0.0}
Attributes
evaluation_name
Get the evaluation name.
input_keys
Get the input keys.
requires_input
This evaluator does not require input.
requires_reference
This evaluator requires a reference.
Methods
__init__(*[, ignore_case, ...])
aevaluate_strings(*, prediction[, ...])
Asynchronously evaluate Chain or LLM output, based on optional input and label.
evaluate_strings(*, prediction[, reference, ...])
Evaluate Chain or LLM output, based on optional input and label.
__init__(*, ignore_case: bool = False, ignore_punctuation: bool = False, ignore_numbers: bool = False, **kwargs: Any)[source]¶
async aevaluate_strings(*, prediction: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict¶
Asynchronously evaluate Chain or LLM output, based on optional input and label.
Parameters
prediction (str) – The LLM or chain prediction to evaluate.