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Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
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12
website/docs/api/entitylinker.mdx
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@ -55,13 +55,13 @@ architectures and their arguments and hyperparameters.
| Setting | Description |
| ------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `labels_discard` | NER labels that will automatically get a "NIL" prediction. Defaults to `[]`. ~~Iterable[str]~~ |
| `n_sents` | The number of neighbouring sentences to take into account. Defaults to 0. ~~int~~ |
| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. Defaults to `True`. ~~bool~~ |
| `incl_context` | Whether or not to include the local context in the model. Defaults to `True`. ~~bool~~ |
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [EntityLinker](/api/architectures#EntityLinker). ~~Model~~ |
| `labels_discard` | NER labels that will automatically get an "NIL" prediction. Defaults to `[]`. ~~Iterable[str]~~ |
| `n_sents` | The number of neighbouring sentences to take into account. Defaults to `0`. ~~int~~ |
| `incl_prior` | Whether prior probabilities from the KB are included in the model. Defaults to `True`. ~~bool~~ |
| `incl_context` | Whether the local context is included in the model. Defaults to `True`. ~~bool~~ |
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [`EntityLinker`](/api/architectures#EntityLinker). ~~Model~~ |
| `entity_vector_length` | Size of encoding vectors in the KB. Defaults to `64`. ~~int~~ |
| `use_gold_ents` | Whether to copy entities from the gold docs or not. Defaults to `True`. If `False`, entities must be set in the training data or by an annotating component in the pipeline. ~~int~~ |
| `use_gold_ents` | Whether entities are copied from the gold docs. Defaults to `True`. If `False`, entities must be set in the training data or by an annotating component in the pipeline. ~~int~~ |
| `get_candidates` | Function that retrieves plausible candidates per entity mention in a given `Iterator[SpanGroup]`. Defaults to [CandidateGenerator](/api/architectures#CandidateGenerator). ~~Callable[[KnowledgeBase, Iterator[SpanGroup]], Iterator[Iterable[Iterable[Candidate]]]]~~ |
| `generate_empty_kb` <Tag variant="new">3.6</Tag> | Function that generates an empty `KnowledgeBase` object. Defaults to [`spacy.EmptyKB.v2`](/api/architectures#EmptyKB), which generates an empty [`InMemoryLookupKB`](/api/inmemorylookupkb). ~~Callable[[Vocab, int], KnowledgeBase]~~ |
| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~ |

3
website/docs/api/inmemorylookupkb.mdx
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@ -158,8 +158,7 @@ Get a list of all aliases in the knowledge base.
Given textual mentions for an arbitrary number of documents as input, retrieve a
list of candidate entities of type [`InMemoryCandidate`](/api/kb#candidate) for
each mention. The [`EntityLinker`](/api/entitylinker) component passes a
generator yielding all mentions to retreive candidates for as
[`SpanGroup`](/api/spangroup)) per document. The decision of how to batch
generator that yields mentions as [`SpanGroup`](/api/spangroup))s per document. The decision of how to batch
candidate retrieval lookups over multiple documents is left up to the
implementation of `KnowledgeBase.get_candidates()`.

4
website/docs/api/kb.mdx
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@ -63,8 +63,8 @@ The length of the fixed-size entity vectors in the knowledge base.
Given textual mentions for an arbitrary number of documents as input, retrieve a
list of candidate entities of type [`Candidate`](/api/kb#candidate) for each
mention. The [`EntityLinker`](/api/entitylinker) component passes a generator
yielding all mentions to retreive candidates for as
[`SpanGroup`](/api/spangroup)) per document. The decision of how to batch
that yields mentions as [`SpanGroup`](/api/spangroup))s per document.
The decision of how to batch
candidate retrieval lookups over multiple documents is left up to the
implementation of `KnowledgeBase.get_candidates()`.