return_all_scores

weave/scorers/relevance_scorer.py

@@ -230,7 +230,6 @@ class RelevanceScorer(Scorer):
         base_url (Optional[str]): Optional URL for external API scoring instead of local model
         device (str): Device to run model on, defaults to "cpu"
         threshold (float): Threshold for relevance classification, defaults to 0.7
-        return_all_scores (bool): Whether to return detailed span-level scores, defaults to False
         debug (bool): Enable debug logging, defaults to False
 
     Returns:
@@ -262,7 +261,6 @@ class RelevanceScorer(Scorer):
     base_url: Optional[str] = None
     device: str = "cpu"
     threshold: float = 0.7
-    return_all_scores: bool = False
     _model: Any = PrivateAttr()
     _tokenizer: Any = PrivateAttr()
 
@@ -325,7 +323,7 @@ def _score_document(
                 span_prob = positive_probs[start:end].mean()
                 spans_with_probs.append({
                     "text": span_text,
-                    "scores": float(span_prob)
+                    "score": float(span_prob)
                 })
 
             return spans_with_probs, int(label_mask.sum()), int(len(label_mask))
@@ -334,7 +332,9 @@ def _score_document(
     def score(
         self, 
         query: str, 
-        documents: list[str]) -> tuple[list[dict[str, Any]], float]:
+        documents: list[str],
+        return_all_scores: bool = False
+        ) -> tuple[list[dict[str, Any]], float]:
         """Score multiple documents and compute weighted average relevance."""
         all_spans = []
         total_weighted_score = 0.0
@@ -354,6 +354,6 @@ def score(
         final_score = total_weighted_score / total_length if total_length > 0 else 0.0
         output = {"flagged": final_score > self.threshold}
         output['extras'] = {'score': final_score}
-        if self.return_all_scores:
+        if return_all_scores:
             output['extras']['all_spans'] = all_spans
         return output