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spaCy/website/docs/api/entitylinker.md

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Documentation for Entity Linking (#4065) * document token ent_kb_id * document span kb_id * update pipeline documentation * prior and context weights as bool's instead * entitylinker api documentation * drop for both models * finish entitylinker documentation * small fixes * documentation for KB * candidate documentation * links to api pages in code * small fix * frequency examples as counts for consistency * consistent documentation about tensors returned by predict * add entity linking to usage 101 * add entity linking infobox and KB section to 101 * entity-linking in linguistic features * small typo corrections * training example and docs for entity_linker * predefined nlp and kb * revert back to similarity encodings for simplicity (for now) * set prior probabilities to 0 when excluded * code clean up * bugfix: deleting kb ID from tokens when entities were removed * refactor train el example to use either model or vocab * pretrain_kb example for example kb generation * add to training docs for KB + EL example scripts * small fixes * error numbering * ensure the language of vocab and nlp stay consistent across serialization * equality with = * avoid conflict in errors file * add error 151 * final adjustements to the train scripts - consistency * update of goldparse documentation * small corrections * push commit * typo fix * add candidate API to kb documentation * update API sidebar with EntityLinker and KnowledgeBase * remove EL from 101 docs * remove entity linker from 101 pipelines / rephrase * custom el model instead of existing model * set version to 2.2 for EL functionality * update documentation for 2 CLI scripts
2019-09-12 12:38:34 +03:00
---
title: EntityLinker
teaser: Functionality to disambiguate a named entity in text to a unique knowledge base identifier.
tag: class
source: spacy/pipeline/pipes.pyx
new: 2.2
---
This class is a subclass of `Pipe` and follows the same API. The pipeline
component is available in the [processing pipeline](/usage/processing-pipelines)
via the ID `"entity_linker"`.
## EntityLinker.Model {#model tag="classmethod"}
Initialize a model for the pipe. The model should implement the
`thinc.neural.Model` API, and should contain a field `tok2vec` that contains
the context encoder. Wrappers are under development for most major machine
learning libraries.
| Name | Type | Description |
| ----------- | ------ | ------------------------------------- |
| `**kwargs` | - | Parameters for initializing the model |
| **RETURNS** | object | The initialized model. |
## EntityLinker.\_\_init\_\_ {#init tag="method"}
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.create_pipe`](/api/language#create_pipe).
> #### Example
>
> ```python
> # Construction via create_pipe
> entity_linker = nlp.create_pipe("entity_linker")
>
> # Construction from class
> from spacy.pipeline import EntityLinker
> entity_linker = EntityLinker(nlp.vocab)
> entity_linker.from_disk("/path/to/model")
> ```
| Name | Type | Description |
| --------------- | ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | `thinc.neural.Model` / `True` | The model powering the pipeline component. If no model is supplied, the model is created when you call `begin_training`, `from_disk` or `from_bytes`. |
| `hidden_width` | int | Width of the hidden layer of the entity linking model, defaults to 128. |
| `incl_prior` | bool | Whether or not to include prior probabilities in the model. Defaults to True. |
| `incl_context` | bool | Whether or not to include the local context in the model (if not: only prior probabilites are used). Defaults to True. |
| **RETURNS** | `EntityLinker` | The newly constructed object. |
## 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
> entity_linker = EntityLinker(nlp.vocab)
> doc = nlp(u"This is a sentence.")
> # This usually happens under the hood
> processed = entity_linker(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
## 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 = EntityLinker(nlp.vocab)
> for doc in entity_linker.pipe(docs, batch_size=50):
> pass
> ```
| Name | Type | Description |
| ------------ | -------- | ------------------------------------------------------ |
| `stream` | iterable | A stream of documents. |
| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
## EntityLinker.predict {#predict tag="method"}
Apply the pipeline's model to a batch of docs, without modifying them.
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> kb_ids, tensors = entity_linker.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | -------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `docs` | iterable | The documents to predict. |
| **RETURNS** | tuple | A `(kb_ids, tensors)` tuple where `kb_ids` are the model's predicted KB identifiers for the entities in the `docs`, and `tensors` are the token representations used to predict these identifiers. |
## 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 = EntityLinker(nlp.vocab)
> kb_ids, tensors = entity_linker.predict([doc1, doc2])
> entity_linker.set_annotations([doc1, doc2], kb_ids, tensors)
> ```
| Name | Type | Description |
| ---------- | -------- | --------------------------------------------------------------------------------------------------- |
| `docs` | iterable | The documents to modify. |
| `kb_ids` | iterable | The knowledge base identifiers for the entities in the docs, predicted by `EntityLinker.predict`. |
| `tensors` | iterable | The token representations used to predict the identifiers. |
## EntityLinker.update {#update tag="method"}
Learn from a batch of documents and gold-standard information, updating both the
pipe's entity linking model and context encoder. Delegates to [`predict`](/api/entitylinker#predict) and
[`get_loss`](/api/entitylinker#get_loss).
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> losses = {}
> optimizer = nlp.begin_training()
> entity_linker.update([doc1, doc2], [gold1, gold2], losses=losses, sgd=optimizer)
> ```
| Name | Type | Description |
| -------- | -------- | ------------------------------------------------------------------------------------------------------------- |
| `docs` | iterable | A batch of documents to learn from. |
| `golds` | iterable | The gold-standard data. Must have the same length as `docs`. |
| `drop` | float | The dropout rate, used both for the EL model and the context encoder. |
| `sgd` | callable | The optimizer for the EL model. Should take two arguments `weights` and `gradient`, and an optional ID. |
| `losses` | dict | Optional record of the loss during training. The value keyed by the model's name is updated. |
## EntityLinker.get_loss {#get_loss tag="method"}
Find the loss and gradient of loss for the entities in a batch of documents and their
predicted scores.
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> kb_ids, tensors = entity_linker.predict(docs)
> loss, d_loss = entity_linker.get_loss(docs, [gold1, gold2], kb_ids, tensors)
> ```
| Name | Type | Description |
| --------------- | -------- | ------------------------------------------------------------ |
| `docs` | iterable | The batch of documents. |
| `golds` | iterable | The gold-standard data. Must have the same length as `docs`. |
| `kb_ids` | iterable | KB identifiers representing the model's predictions. |
| `tensors` | iterable | The token representations used to predict the identifiers |
| **RETURNS** | tuple | The loss and the gradient, i.e. `(loss, gradient)`. |
## EntityLinker.set_kb {#set_kb tag="method"}
Define the knowledge base (KB) used for disambiguating named entities to KB identifiers.
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> entity_linker.set_kb(kb)
> ```
| Name | Type | Description |
| --------------- | --------------- | ------------------------------------------------------------ |
| `kb` | `KnowledgeBase` | The [`KnowledgeBase`](/api/kb). |
## EntityLinker.begin_training {#begin_training tag="method"}
Initialize the pipe for training, using data examples if available. If no model
has been initialized yet, the model is added.
Before calling this method, a knowledge base should have been defined with [`set_kb`](/api/entitylinker#set_kb).
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> entity_linker.set_kb(kb)
> nlp.add_pipe(entity_linker, last=True)
> optimizer = entity_linker.begin_training(pipeline=nlp.pipeline)
> ```
| Name | Type | Description |
| ------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `gold_tuples` | iterable | Optional gold-standard annotations from which to construct [`GoldParse`](/api/goldparse) objects. |
| `pipeline` | list | Optional list of pipeline components that this component is part of. |
| `sgd` | callable | An optional optimizer. Should take two arguments `weights` and `gradient`, and an optional ID. Will be created via [`EntityLinker`](/api/entitylinker#create_optimizer) if not set. |
| **RETURNS** | callable | An optimizer. |
## EntityLinker.create_optimizer {#create_optimizer tag="method"}
Create an optimizer for the pipeline component.
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> optimizer = entity_linker.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | -------- | -------------- |
| **RETURNS** | callable | The optimizer. |
## EntityLinker.use_params {#use_params tag="method, contextmanager"}
Modify the pipe's EL model, to use the given parameter values.
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> with entity_linker.use_params(optimizer.averages):
> entity_linker.to_disk("/best_model")
> ```
| Name | Type | Description |
| -------- | ---- | ---------------------------------------------------------------------------------------------------------- |
| `params` | dict | The parameter values to use in the model. At the end of the context, the original parameters are restored. |
## EntityLinker.to_disk {#to_disk tag="method"}
Serialize the pipe to disk.
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> entity_linker.to_disk("/path/to/entity_linker")
> ```
| Name | Type | Description |
| --------- | ---------------- | --------------------------------------------------------------------------------------------------------------------- |
| `path` | unicode / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
## EntityLinker.from_disk {#from_disk tag="method"}
Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> entity_linker = EntityLinker(nlp.vocab)
> entity_linker.from_disk("/path/to/entity_linker")
> ```
| Name | Type | Description |
| ----------- | ------------------ | -------------------------------------------------------------------------- |
| `path` | unicode / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. |
| **RETURNS** | `EntityLinker` | The modified `EntityLinker` object. |
## 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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