diff --git a/weave/flow/eval.py b/weave/flow/eval.py index a94759d93cff..d38efdf10a14 100644 --- a/weave/flow/eval.py +++ b/weave/flow/eval.py @@ -380,11 +380,9 @@ async def predict_and_score( message += textwrap.dedent( f""" - When using the `Scorer` weave class, you can set the `scorer.column_map` - attribute to map scorer argument names to dataset columns. + When using the `Scorer` weave class, you can set the `scorer.column_map` attribute to map scorer argument names to dataset columns. - For example, if the `score` expects "output", "input" and "ground_truth" and we have a dataset - with columns "question" and "answer", `column_map` can be used to map the non-output parameter like so: + For example, if the `score` expects "output", "input" and "ground_truth" and we have a dataset with columns "question" and "answer", `column_map` can be used to map the non-output parameter like so: {{"input": "question", "ground_truth": "answer"}} scorer argument names: {score_arg_names} @@ -393,7 +391,7 @@ async def predict_and_score( Options for resolving: a. if using the `Scorer` weave class, you can set the `scorer.column_map` attribute to map scorer argument names to dataset column names or - b. change the argument names the in the scoring function of `{scorer_name}` to match a subset of dataset column names: ({dataset_column_names_str}) or + b. change the argument names the in the scoring function of `{scorer_name}` to match a subset of dataset column names: ({dataset_column_names_str}) or c. change dataset column names to match expected `{scorer_name}` argument names: {scorer_required_arg_names} """ )