diff --git a/spacy/language.py b/spacy/language.py
index dab60a421..39d95c689 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -600,6 +600,19 @@ class Language(object):
def evaluate(
self, docs_golds, verbose=False, batch_size=256, scorer=None, component_cfg=None
):
+ """Evaluate a model's pipeline components.
+
+ docs_golds (iterable): Tuples of `Doc` and `GoldParse` objects.
+ verbose (bool): Print debugging information.
+ batch_size (int): Batch size to use.
+ scorer (Scorer): Optional `Scorer` to use. If not passed in, a new one
+ will be created.
+ component_cfg (dict): An optional dictionary with extra keyword
+ arguments for specific components.
+ RETURNS (Scorer): The scorer containing the evaluation results.
+
+ DOCS: https://spacy.io/api/language#evaluate
+ """
if scorer is None:
scorer = Scorer()
if component_cfg is None:
diff --git a/website/docs/api/language.md b/website/docs/api/language.md
index 47d747775..3245a165b 100644
--- a/website/docs/api/language.md
+++ b/website/docs/api/language.md
@@ -122,6 +122,25 @@ Update the models in the pipeline.
| `losses` | dict | Dictionary to update with the loss, keyed by pipeline component. |
| `component_cfg` 2.1 | dict | Config parameters for specific pipeline components, keyed by component name. |
+## Language.evaluate {#evaluate tag="method"}
+
+Evaluate a model's pipeline components.
+
+> #### Example
+>
+> ```python
+> scorer = nlp.evaluate(docs_golds, verbose=True)
+> print(scorer.scores)
+> ```
+
+| Name | Type | Description |
+| -------------------------------------------- | -------- | ------------------------------------------------------------------------------------- |
+| `docs_golds` | iterable | Tuples of `Doc` and `GoldParse` objects. |
+| `verbose` | bool | Print debugging information. |
+| `batch_size` | int | The batch size to use. |
+| `scorer` | `Scorer` | Optional [`Scorer`](/api/scorer) to use. If not passed in, a new one will be created. |
+| `component_cfg` 2.1 | dict | Config parameters for specific pipeline components, keyed by component name. |
+
## Language.begin_training {#begin_training tag="method"}
Allocate models, pre-process training data and acquire an optimizer.