mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-31 16:07:41 +03:00
283 lines
13 KiB
Markdown
283 lines
13 KiB
Markdown
---
|
|
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"`.
|
|
|
|
## Default config {#config}
|
|
|
|
This is the default configuration used to initialize the model powering the
|
|
pipeline component. See the [model architectures](/api/architectures)
|
|
documentation for details on the architectures and their arguments and
|
|
hyperparameters. To learn more about how to customize the config and train
|
|
custom models, check out the [training config](/usage/training#config) docs.
|
|
|
|
```python
|
|
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/defaults/entity_linker_defaults.cfg
|
|
```
|
|
|
|
## EntityLinker.\_\_init\_\_ {#init tag="method"}
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> # Construction via create_pipe with default model
|
|
> entity_linker = nlp.create_pipe("entity_linker")
|
|
>
|
|
> # Construction via create_pipe with custom model
|
|
> config = {"model": {"@architectures": "my_el"}}
|
|
> entity_linker = nlp.create_pipe("entity_linker", config)
|
|
>
|
|
> # Construction from class with custom model from file
|
|
> from spacy.pipeline import EntityLinker
|
|
> model = util.load_config("model.cfg", create_objects=True)["model"]
|
|
> 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.create_pipe`](/api/language#create_pipe).
|
|
|
|
| Name | Type | Description |
|
|
| ------- | ------- | ------------------------------------------------------------------------------- |
|
|
| `vocab` | `Vocab` | The shared vocabulary. |
|
|
| `model` | `Model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
|
|
| `**cfg` | - | Configuration parameters. |
|
|
|
|
| **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("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[Doc]` | 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 = entity_linker.predict([doc1, doc2])
|
|
> ```
|
|
|
|
| Name | Type | Description |
|
|
| ----------- | --------------- | ------------------------------------------------------------ |
|
|
| `docs` | `Iterable[Doc]` | The documents to predict. |
|
|
| **RETURNS** | `Iterable[str]` | The predicted KB identifiers for the entities in the `docs`. |
|
|
|
|
## 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 = entity_linker.predict([doc1, doc2])
|
|
> entity_linker.set_annotations([doc1, doc2], kb_ids)
|
|
> ```
|
|
|
|
| Name | Type | Description |
|
|
| -------- | --------------- | ------------------------------------------------------------------------------------------------- |
|
|
| `docs` | `Iterable[Doc]` | The documents to modify. |
|
|
| `kb_ids` | `Iterable[str]` | The knowledge base identifiers for the entities in the docs, predicted by `EntityLinker.predict`. |
|
|
|
|
## 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) and
|
|
[`get_loss`](/api/entitylinker#get_loss).
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> entity_linker = EntityLinker(nlp.vocab, nel_model)
|
|
> optimizer = nlp.begin_training()
|
|
> losses = entity_linker.update(examples, sgd=optimizer)
|
|
> ```
|
|
|
|
| Name | Type | Description |
|
|
| ----------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
|
|
| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
|
|
| _keyword-only_ | | |
|
|
| `drop` | float | The dropout rate. |
|
|
| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/entitylinker#set_annotations). |
|
|
| `sgd` | `Optimizer` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
|
|
| `losses` | `Dict[str, float]` | Optional record of the loss during training. The value keyed by the model's name is updated. |
|
|
| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
|
|
|
|
## 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. Return an
|
|
[`Optimizer`](https://thinc.ai/docs/api-optimizers) object. 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 |
|
|
| -------------- | ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `get_examples` | `Iterable[Example]` | Optional gold-standard annotations in the form of [`Example`](/api/example) objects. |
|
|
| `pipeline` | `List[(str, callable)]` | Optional list of pipeline components that this component is part of. |
|
|
| `sgd` | `Optimizer` | An optional [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. Will be created via [`create_optimizer`](/api/entitylinker#create_optimizer) if not set. |
|
|
| **RETURNS** | `Optimizer` | 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** | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. |
|
|
|
|
## 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` | str / `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` | str / `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. |
|