update EL API
@@ -34,12 +34,12 @@ architectures and their arguments and hyperparameters.
> ```
| Setting | Type | Description | Default |
-| ---------------- | -------------------------------------------------------- | ------------------------------------------------------------------------- | ------------------------------------------------------ |
+| ---------------- | -------------------------------------------------------- | --------------------------------------------------------------------------- | ------------------------------------------------------ |
| `labels_discard` | `Iterable[str]` | NER labels that will automatically get a "NIL" prediction. | `[]` |
| `incl_prior` | bool | Whether or not to include prior probabilities from the KB in the model. | `True` |
| `incl_context` | bool | Whether or not to include the local context in the model. | `True` |
| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [EntityLinker](/api/architectures#EntityLinker) |
-| `kb_loader` | `Callable[[Vocab], KnowledgeBase]` | Function that creates a [`KnowledgeBase`](/api/kb) from a Vocab instance. | An empty KnowledgeBase with `entity_vector_length` 64. |
+| `kb_loader` | `Callable[[Vocab], KnowledgeBase]` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. | An empty KnowledgeBase with `entity_vector_length` 64. |
| `get_candidates` | `Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]` | Function that generates plausible candidates for a given `Span` object. | Built-in dictionary-lookup function. |
```python
@@ -55,7 +55,11 @@ https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/entity_linker.py
> entity_linker = nlp.add_pipe("entity_linker")
>
> # Construction via add_pipe with custom model
-> config = {"model": {"@architectures": "my_el"}}
+> config = {"model": {"@architectures": "my_el.v1"}}
+> entity_linker = nlp.add_pipe("entity_linker", config=config)
+>
+> # Construction via add_pipe with custom KB and candidate generation
+> config = {"kb_loader": {"@assets": "my_kb.v1"}, "get_candidates": {"@assets": "my_candidates.v1"},}
> entity_linker = nlp.add_pipe("entity_linker", config=config)
>
> # Construction from class
@@ -67,15 +71,17 @@ Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#add_pipe).
-<!-- TODO: finish API docs -->
+Note that both the internal KB as well as the Candidate generator can be
+customized by providing custom registered functions.
| Name | Type | Description |
-| ---------------- | --------------- | ------------------------------------------------------------------------------------------- |
+| ---------------- | -------------------------------------------------------- | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | `Model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
| _keyword-only_ | | |
-| `kb` | `KnowlegeBase` | The [`KnowledgeBase`](/api/kb) holding all entities and their aliases. |
+| `kb_loader` | `Callable[[Vocab], KnowledgeBase]` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. |
+| `get_candidates` | `Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]` | Function that generates plausible candidates for a given `Span` object. |
| `labels_discard` | `Iterable[str]` | NER labels that will automatically get a "NIL" prediction. |
| `incl_prior` | bool | Whether or not to include prior probabilities from the KB in the model. |
| `incl_context` | bool | Whether or not to include the local context in the model. |
