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Merge pull request #6242 from svlandeg/feature/nel-docs
update NEL docs after latest refactor
This commit is contained in:
Ines Montani
GitHub
commit
63f39d3ce7
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@ -1,3 +1,4 @@
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from pathlib import Path
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from typing import Optional, Callable, Iterable
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from thinc.api import chain, clone, list2ragged, reduce_mean, residual
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from thinc.api import Model, Maxout, Linear
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@ -25,7 +26,7 @@ def build_nel_encoder(tok2vec: Model, nO: Optional[int] = None) -> Model:
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@registry.misc.register("spacy.KBFromFile.v1")
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def load_kb(kb_path: str) -> Callable[[Vocab], KnowledgeBase]:
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def load_kb(kb_path: Path) -> Callable[[Vocab], KnowledgeBase]:
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def kb_from_file(vocab):
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kb = KnowledgeBase(vocab, entity_vector_length=1)
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kb.from_disk(kb_path)
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@ -637,13 +637,6 @@ into the "real world". This requires 3 main components:
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> window_size = 1
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> maxout_pieces = 3
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> subword_features = true
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>
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> [kb_loader]
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> @misc = "spacy.EmptyKB.v1"
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> entity_vector_length = 64
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>
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> [get_candidates]
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> @misc = "spacy.CandidateGenerator.v1"
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> ```
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The `EntityLinker` model architecture is a Thinc `Model` with a
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@ -657,13 +650,21 @@ The `EntityLinker` model architecture is a Thinc `Model` with a
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### spacy.EmptyKB.v1 {#EmptyKB}
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A function that creates a default, empty `KnowledgeBase` from a
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[`Vocab`](/api/vocab) instance.
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A function that creates an empty `KnowledgeBase` from a [`Vocab`](/api/vocab)
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instance. This is the default when a new entity linker component is created.
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| Name | Description |
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| ---------------------- | ----------------------------------------------------------------------------------- |
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| `entity_vector_length` | The length of the vectors encoding each entity in the KB. Defaults to `64`. ~~int~~ |
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### spacy.KBFromFile.v1 {#KBFromFile}
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A function that reads an existing `KnowledgeBase` from file.
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| Name | Description |
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| --------- | -------------------------------------------------------- |
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| `kb_path` | The location of the KB that was stored to file. ~~Path~~ |
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### spacy.CandidateGenerator.v1 {#CandidateGenerator}
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A function that takes as input a [`KnowledgeBase`](/api/kb) and a
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@ -34,20 +34,20 @@ architectures and their arguments and hyperparameters.
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> "incl_prior": True,
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> "incl_context": True,
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> "model": DEFAULT_NEL_MODEL,
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> "kb_loader": {'@misc': 'spacy.EmptyKB.v1', 'entity_vector_length': 64},
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> "entity_vector_length": 64,
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> "get_candidates": {'@misc': 'spacy.CandidateGenerator.v1'},
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> }
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> nlp.add_pipe("entity_linker", config=config)
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> ```
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| Setting | Description |
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| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `labels_discard` | NER labels that will automatically get a "NIL" prediction. Defaults to `[]`. ~~Iterable[str]~~ |
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| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. Defaults to `True`. ~~bool~~ |
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| `incl_context` | Whether or not to include the local context in the model. Defaults to `True`. ~~bool~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [EntityLinker](/api/architectures#EntityLinker). ~~Model~~ |
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| `kb_loader` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. Defaults to [EmptyKB](/api/architectures#EmptyKB), a function returning an empty `KnowledgeBase` with an `entity_vector_length` of `64`. ~~Callable[[Vocab], KnowledgeBase]~~ |
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| `get_candidates` | Function that generates plausible candidates for a given `Span` object. Defaults to [CandidateGenerator](/api/architectures#CandidateGenerator), a function looking up exact, case-dependent aliases in the KB. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
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| Setting | Description |
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| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `labels_discard` | NER labels that will automatically get a "NIL" prediction. Defaults to `[]`. ~~Iterable[str]~~ |
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| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. Defaults to `True`. ~~bool~~ |
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| `incl_context` | Whether or not to include the local context in the model. Defaults to `True`. ~~bool~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [EntityLinker](/api/architectures#EntityLinker). ~~Model~~ |
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| `entity_vector_length` | Size of encoding vectors in the KB. Defaults to 64. ~~int~~ |
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| `get_candidates` | Function that generates plausible candidates for a given `Span` object. Defaults to [CandidateGenerator](/api/architectures#CandidateGenerator), a function looking up exact, case-dependent aliases in the KB. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/entity_linker.py
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@ -65,10 +65,6 @@ architectures and their arguments and hyperparameters.
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> config = {"model": {"@architectures": "my_el.v1"}}
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> entity_linker = nlp.add_pipe("entity_linker", config=config)
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>
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> # Construction via add_pipe with custom KB and candidate generation
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> config = {"kb": {"@misc": "my_kb.v1"}}
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> entity_linker = nlp.add_pipe("entity_linker", config=config)
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>
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> # Construction from class
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> from spacy.pipeline import EntityLinker
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> entity_linker = EntityLinker(nlp.vocab, model)
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@ -76,21 +72,25 @@ architectures and their arguments and hyperparameters.
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Create a new pipeline instance. In your application, you would normally use a
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shortcut for this and instantiate the component using its string name and
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[`nlp.add_pipe`](/api/language#add_pipe). Note that both the internal
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`KnowledgeBase` as well as the Candidate generator can be customized by
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providing custom registered functions.
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[`nlp.add_pipe`](/api/language#add_pipe).
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| Name | Description |
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| ---------------- | -------------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model~~ |
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| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
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| _keyword-only_ | |
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| `kb_loader` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. ~~Callable[[Vocab], KnowledgeBase]~~ |
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| `get_candidates` | Function that generates plausible candidates for a given `Span` object. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
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| `labels_discard` | NER labels that will automatically get a `"NIL"` prediction. ~~Iterable[str]~~ |
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| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. ~~bool~~ |
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| `incl_context` | Whether or not to include the local context in the model. ~~bool~~ |
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Upon construction of the entity linker component, an empty knowledge base is
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constructed with the provided `entity_vector_length`. If you want to use a
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custom knowledge base, you should either call
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[`set_kb`](/api/entitylinker#set_kb) or provide a `kb_loader` in the
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[`initialize`](/api/entitylinker#initialize) call.
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| Name | Description |
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| ---------------------- | -------------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model~~ |
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| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
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| _keyword-only_ | |
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| `entity_vector_length` | Size of encoding vectors in the KB. ~~int~~ |
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| `get_candidates` | Function that generates plausible candidates for a given `Span` object. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
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| `labels_discard` | NER labels that will automatically get a `"NIL"` prediction. ~~Iterable[str]~~ |
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| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. ~~bool~~ |
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| `incl_context` | Whether or not to include the local context in the model. ~~bool~~ |
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## EntityLinker.\_\_call\_\_ {#call tag="method"}
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@ -139,6 +139,28 @@ applied to the `Doc` in order. Both [`__call__`](/api/entitylinker#call) and
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| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
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| **YIELDS** | The processed documents in order. ~~Doc~~ |
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## EntityLinker.set_kb {#initialize tag="method" new="3"}
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The `kb_loader` should be a function that takes a `Vocab` instance and creates
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the `KnowledgeBase`, ensuring that the strings of the knowledge base are synced
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with the current vocab.
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> #### Example
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>
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> ```python
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> def create_kb(vocab):
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> kb = KnowledgeBase(vocab, entity_vector_length=128)
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> kb.add_entity(...)
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> kb.add_alias(...)
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> return kb
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> entity_linker = nlp.add_pipe("entity_linker")
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> entity_linker.set_kb(lambda: [], nlp=nlp, kb_loader=create_kb)
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> ```
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| Name | Description |
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| ----------- | ---------------------------------------------------------------------------------------------------------------- |
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| `kb_loader` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. ~~Callable[[Vocab], KnowledgeBase]~~ |
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## EntityLinker.initialize {#initialize tag="method" new="3"}
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Initialize the component for training. `get_examples` should be a function that
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@ -150,6 +172,11 @@ network,
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setting up the label scheme based on the data. This method is typically called
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by [`Language.initialize`](/api/language#initialize).
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Optionally, a `kb_loader` argument may be specified to change the internal
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knowledge base. This argument should be a function that takes a `Vocab` instance
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and creates the `KnowledgeBase`, ensuring that the strings of the knowledge base
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are synced with the current vocab.
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<Infobox variant="warning" title="Changed in v3.0" id="begin_training">
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This method was previously called `begin_training`.
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@ -160,7 +187,7 @@ This method was previously called `begin_training`.
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>
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> ```python
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> entity_linker = nlp.add_pipe("entity_linker")
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> entity_linker.initialize(lambda: [], nlp=nlp)
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> entity_linker.initialize(lambda: [], nlp=nlp, kb_loader=my_kb)
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> ```
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| Name | Description |
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@ -168,6 +195,7 @@ This method was previously called `begin_training`.
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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| `kb_loader` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. ~~Callable[[Vocab], KnowledgeBase]~~ |
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## EntityLinker.predict {#predict tag="method"}
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