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Move Language.evaluate scorer config to new arg
Move `Language.evaluate` scorer config from `component_cfg` to separate argument `scorer_cfg`.
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@ -1099,6 +1099,7 @@ class Language:
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batch_size: int = 256,
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scorer: Optional[Scorer] = None,
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component_cfg: Optional[Dict[str, Dict[str, Any]]] = None,
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scorer_cfg: Optional[Dict[str, Any]] = None,
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) -> Dict[str, Union[float, dict]]:
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"""Evaluate a model's pipeline components.
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@ -1109,6 +1110,8 @@ class Language:
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will be created.
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component_cfg (dict): An optional dictionary with extra keyword
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arguments for specific components.
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scorer_cfg (dict): An optional dictionary with extra keyword arguments
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for the scorer.
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RETURNS (Scorer): The scorer containing the evaluation results.
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DOCS: https://spacy.io/api/language#evaluate
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@ -1126,8 +1129,10 @@ class Language:
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raise TypeError(err)
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if component_cfg is None:
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component_cfg = {}
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if scorer_cfg is None:
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scorer_cfg = {}
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if scorer is None:
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kwargs = component_cfg.get("scorer", {})
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kwargs = dict(scorer_cfg)
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kwargs.setdefault("verbose", verbose)
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kwargs.setdefault("nlp", self)
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scorer = Scorer(**kwargs)
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@ -118,7 +118,7 @@ def test_overfitting_IO():
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# Test scoring
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scores = nlp.evaluate(
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train_examples, component_cfg={"scorer": {"positive_label": "POSITIVE"}}
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train_examples, scorer_cfg={"positive_label": "POSITIVE"}
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)
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assert scores["cats_f"] == 1.0
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assert scores["cats_score"] == 1.0
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@ -302,6 +302,7 @@ Evaluate a model's pipeline components.
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| `batch_size` | int | The batch size to use. |
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| `scorer` | `Scorer` | Optional [`Scorer`](/api/scorer) to use. If not passed in, a new one will be created. |
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| `component_cfg` | `Dict[str, dict]` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. |
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| `scorer_cfg` | `Dict[str, Any]` | Optional dictionary of keyword arguments for the `Scorer`. Defaults to `None`. |
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| **RETURNS** | `Dict[str, Union[float, dict]]` | A dictionary of evaluation scores. |
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## Language.use_params {#use_params tag="contextmanager, method"}
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