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efdbb722c5
spaCy/website/docs/api/entitylinker.md
Daniël de Kok efdbb722c5
Store activations in Docs when save_activations is enabled (#11002) 
* Store activations in Doc when `store_activations` is enabled

This change adds the new `activations` attribute to `Doc`. This
attribute can be used by trainable pipes to store their activations,
probabilities, and guesses for downstream users.

As an example, this change modifies the `tagger` and `senter` pipes to
add an `store_activations` option. When this option is enabled, the
probabilities and guesses are stored in `set_annotations`.

* Change type of `store_activations` to `Union[bool, List[str]]`

When the value is:

- A bool: all activations are stored when set to `True`.
- A List[str]: the activations named in the list are stored

* Formatting fixes in Tagger

* Support store_activations in spancat and morphologizer

* Make Doc.activations type visible to MyPy

* textcat/textcat_multilabel: add store_activations option

* trainable_lemmatizer/entity_linker: add store_activations option

* parser/ner: do not currently support returning activations

* Extend tagger and senter tests

So that they, like the other tests, also check that we get no
activations if no activations were requested.

* Document `Doc.activations` and `store_activations` in the relevant pipes

* Start errors/warnings at higher numbers to avoid merge conflicts

Between the master and v4 branches.

* Add `store_activations` to docstrings.

* Replace store_activations setter by set_store_activations method

Setters that take a different type than what the getter returns are still
problematic for MyPy. Replace the setter by a method, so that type inference
works everywhere.

* Use dict comprehension suggested by @svlandeg

* Revert "Use dict comprehension suggested by @svlandeg"

This reverts commit 6e7b958f70.

* EntityLinker: add type annotations to _add_activations

* _store_activations: make kwarg-only, remove doc_scores_lens arg

* set_annotations: add type annotations

* Apply suggestions from code review

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* TextCat.predict: return dict

* Make the `TrainablePipe.store_activations` property a bool

This means that we can also bring back `store_activations` setter.

* Remove `TrainablePipe.activations`

We do not need to enumerate the activations anymore since `store_activations` is
`bool`.

* Add type annotations for activations in predict/set_annotations

* Rename `TrainablePipe.store_activations` to `save_activations`

* Error E1400 is not used anymore

This error was used when activations were still `Union[bool, List[str]]`.

* Change wording in API docs after store -> save change

* docs: tag (save_)activations as new in spaCy 4.0

* Fix copied line in morphologizer activations test

* Don't train in any test_save_activations test

* Rename activations

- "probs" -> "probabilities"
- "guesses" -> "label_ids", except in the edit tree lemmatizer, where
  "guesses" -> "tree_ids".

* Remove unused W400 warning.

This warning was used when we still allowed the user to specify
which activations to save.

* Formatting fixes

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Replace "kb_ids" by a constant

* spancat: replace a cast by an assertion

* Fix EOF spacing

* Fix comments in test_save_activations tests

* Do not set RNG seed in activation saving tests

* Revert "spancat: replace a cast by an assertion"

This reverts commit 0bd5730d16.

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
2022-09-13 09:51:12 +02:00

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---
title: EntityLinker
tag: class
source: spacy/pipeline/entity_linker.py
new: 2.2
teaser: 'Pipeline component for named entity linking and disambiguation'
api_base_class: /api/pipe
api_string_name: entity_linker
api_trainable: true
---
An `EntityLinker` component disambiguates textual mentions (tagged as named
entities) to unique identifiers, grounding the named entities into the "real
world". It requires a `KnowledgeBase`, as well as a function to generate
plausible candidates from that `KnowledgeBase` given a certain textual mention,
and a machine learning model to pick the right candidate, given the local
context of the mention.
## Assigned Attributes {#assigned-attributes}
Predictions, in the form of knowledge base IDs, will be assigned to
`Token.ent_kb_id_`.
| Location | Value |
| ------------------ | --------------------------------- |
| `Token.ent_kb_id` | Knowledge base ID (hash). ~~int~~ |
| `Token.ent_kb_id_` | Knowledge base ID. ~~str~~ |
## Config and implementation {#config}
The default config is defined by the pipeline component factory and describes
how the component should be configured. You can override its settings via the
`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
[`config.cfg` for training](/usage/training#config). See the
[model architectures](/api/architectures) documentation for details on the
architectures and their arguments and hyperparameters.
> #### Example
>
> ```python
> from spacy.pipeline.entity_linker import DEFAULT_NEL_MODEL
> config = {
> "labels_discard": [],
> "n_sents": 0,
> "incl_prior": True,
> "incl_context": True,
> "model": DEFAULT_NEL_MODEL,
> "entity_vector_length": 64,
> "get_candidates": {'@misc': 'spacy.CandidateGenerator.v1'},
> "threshold": None,
> }
> nlp.add_pipe("entity_linker", config=config)
> ```
| 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~~ |
| `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~~ |
| `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]]~~ |
| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~ |
| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~ |
| `save_activations` <Tag variant="new">4.0</Tag> | Save activations in `Doc` when annotating. Saved activations are `"ents"` and `"scores"`. ~~Union[bool, list[str]]~~ |
| `threshold` <Tag variant="new">3.4</Tag> | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the treshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~ |
```python
%%GITHUB_SPACY/spacy/pipeline/entity_linker.py
```
## EntityLinker.\_\_init\_\_ {#init tag="method"}
> #### Example
>
> ```python
> # Construction via add_pipe with default model
> entity_linker = nlp.add_pipe("entity_linker")
>
> # Construction via add_pipe with custom model
> config = {"model": {"@architectures": "my_el.v1"}}
> entity_linker = nlp.add_pipe("entity_linker", config=config)
>
> # Construction from class
> from spacy.pipeline import EntityLinker
> entity_linker = EntityLinker(nlp.vocab, model)
> ```
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).
Upon construction of the entity linker component, an empty knowledge base is
constructed with the provided `entity_vector_length`. If you want to use a
custom knowledge base, you should either call
[`set_kb`](/api/entitylinker#set_kb) or provide a `kb_loader` in the
[`initialize`](/api/entitylinker#initialize) call.
| Name | Description |
| ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| _keyword-only_ | |
| `entity_vector_length` | Size of encoding vectors in the KB. ~~int~~ |
| `get_candidates` | Function that generates plausible candidates for a given `Span` object. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
| `labels_discard` | NER labels that will automatically get a `"NIL"` prediction. ~~Iterable[str]~~ |
| `n_sents` | The number of neighbouring sentences to take into account. ~~int~~ |
| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. ~~bool~~ |
| `incl_context` | Whether or not to include the local context in the model. ~~bool~~ |
| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~ |
| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~ |
| `threshold` <Tag variant="new">3.4</Tag> | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the treshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~ |
## EntityLinker.\_\_call\_\_ {#call tag="method"}
Apply the pipe to one document. The document is modified in place and returned.
This usually happens under the hood when the `nlp` object is called on a text
and all pipeline components are applied to the `Doc` in order. Both
[`__call__`](/api/entitylinker#call) and [`pipe`](/api/entitylinker#pipe)
delegate to the [`predict`](/api/entitylinker#predict) and
[`set_annotations`](/api/entitylinker#set_annotations) methods.
> #### Example
>
> ```python
> doc = nlp("This is a sentence.")
> entity_linker = nlp.add_pipe("entity_linker")
> # This usually happens under the hood
> processed = entity_linker(doc)
> ```
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## EntityLinker.pipe {#pipe tag="method"}
Apply the pipe to a stream of documents. This usually happens under the hood
when the `nlp` object is called on a text and all pipeline components are
applied to the `Doc` in order. Both [`__call__`](/api/entitylinker#call) and
[`pipe`](/api/entitylinker#pipe) delegate to the
[`predict`](/api/entitylinker#predict) and
[`set_annotations`](/api/entitylinker#set_annotations) methods.
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker")
> for doc in entity_linker.pipe(docs, batch_size=50):
> pass
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## EntityLinker.set_kb {#set_kb tag="method" new="3"}
The `kb_loader` should be a function that takes a `Vocab` instance and creates
the `KnowledgeBase`, ensuring that the strings of the knowledge base are synced
with the current vocab.
> #### Example
>
> ```python
> def create_kb(vocab):
> kb = KnowledgeBase(vocab, entity_vector_length=128)
> kb.add_entity(...)
> kb.add_alias(...)
> return kb
> entity_linker = nlp.add_pipe("entity_linker")
> entity_linker.set_kb(create_kb)
> ```
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------- |
| `kb_loader` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. ~~Callable[[Vocab], KnowledgeBase]~~ |
## EntityLinker.initialize {#initialize tag="method" new="3"}
Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. **At least one example
should be supplied.** The data examples are used to **initialize the model** of
the component and can either be the full training data or a representative
sample. Initialization includes validating the network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize).
Optionally, a `kb_loader` argument may be specified to change the internal
knowledge base. This argument should be a function that takes a `Vocab` instance
and creates the `KnowledgeBase`, ensuring that the strings of the knowledge base
are synced with the current vocab.
<Infobox variant="warning" title="Changed in v3.0" id="begin_training">
This method was previously called `begin_training`.
</Infobox>
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker")
> entity_linker.initialize(lambda: examples, nlp=nlp, kb_loader=my_kb)
> ```
| Name | Description |
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
| `kb_loader` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. ~~Callable[[Vocab], KnowledgeBase]~~ |
## EntityLinker.predict {#predict tag="method"}
Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
modifying them. Returns the KB IDs for each entity in each doc, including `NIL`
if there is no prediction.
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker")
> kb_ids = entity_linker.predict([doc1, doc2])
> ```
| Name | Description |
| ----------- | -------------------------------------------------------------------------- |
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
| **RETURNS** | The predicted KB identifiers for the entities in the `docs`. ~~List[str]~~ |
## EntityLinker.set_annotations {#set_annotations tag="method"}
Modify a batch of documents, using pre-computed entity IDs for a list of named
entities.
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker")
> kb_ids = entity_linker.predict([doc1, doc2])
> entity_linker.set_annotations([doc1, doc2], kb_ids)
> ```
| Name | Description |
| -------- | --------------------------------------------------------------------------------------------------------------- |
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
| `kb_ids` | The knowledge base identifiers for the entities in the docs, predicted by `EntityLinker.predict`. ~~List[str]~~ |
## EntityLinker.update {#update tag="method"}
Learn from a batch of [`Example`](/api/example) objects, updating both the
pipe's entity linking model and context encoder. Delegates to
[`predict`](/api/entitylinker#predict).
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker")
> optimizer = nlp.initialize()
> losses = entity_linker.update(examples, sgd=optimizer)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | |
| `drop` | The dropout rate. ~~float~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## EntityLinker.create_optimizer {#create_optimizer tag="method"}
Create an optimizer for the pipeline component.
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker")
> optimizer = entity_linker.create_optimizer()
> ```
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## EntityLinker.use_params {#use_params tag="method, contextmanager"}
Modify the pipe's model, to use the given parameter values. At the end of the
context, the original parameters are restored.
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker")
> with entity_linker.use_params(optimizer.averages):
> entity_linker.to_disk("/best_model")
> ```
| Name | Description |
| -------- | -------------------------------------------------- |
| `params` | The parameter values to use in the model. ~~dict~~ |
## EntityLinker.to_disk {#to_disk tag="method"}
Serialize the pipe to disk.
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker")
> entity_linker.to_disk("/path/to/entity_linker")
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## EntityLinker.from_disk {#from_disk tag="method"}
Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker")
> entity_linker.from_disk("/path/to/entity_linker")
> ```
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `EntityLinker` object. ~~EntityLinker~~ |
## EntityLinker.to_bytes {#to_bytes tag="method"}
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker")
> entity_linker_bytes = entity_linker.to_bytes()
> ```
Serialize the pipe to a bytestring, including the `KnowledgeBase`.
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `EntityLinker` object. ~~bytes~~ |
## EntityLinker.from_bytes {#from_bytes tag="method"}
Load the pipe from a bytestring. Modifies the object in place and returns it.
> #### Example
>
> ```python
> entity_linker_bytes = entity_linker.to_bytes()
> entity_linker = nlp.add_pipe("entity_linker")
> entity_linker.from_bytes(entity_linker_bytes)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `EntityLinker` object. ~~EntityLinker~~ |
## Serialization fields {#serialization-fields}
During serialization, spaCy will export several data fields used to restore
different aspects of the object. If needed, you can exclude them from
serialization by passing in the string names via the `exclude` argument.
> #### Example
>
> ```python
> data = entity_linker.to_disk("/path", exclude=["vocab"])
> ```
| Name | Description |
| ------- | -------------------------------------------------------------- |
| `vocab` | The shared [`Vocab`](/api/vocab). |
| `cfg` | The config file. You usually don't want to exclude this. |
| `model` | The binary model data. You usually don't want to exclude this. |
| `kb` | The knowledge base. You usually don't want to exclude this. |
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