spaCy/website/docs/api/entityrecognizer.md
Ines Montani e597110d31
💫 Update website (#3285)
<!--- Provide a general summary of your changes in the title. -->

## Description

The new website is implemented using [Gatsby](https://www.gatsbyjs.org) with [Remark](https://github.com/remarkjs/remark) and [MDX](https://mdxjs.com/). This allows authoring content in **straightforward Markdown** without the usual limitations. Standard elements can be overwritten with powerful [React](http://reactjs.org/) components and wherever Markdown syntax isn't enough, JSX components can be used. Hopefully, this update will also make it much easier to contribute to the docs. Once this PR is merged, I'll implement auto-deployment via [Netlify](https://netlify.com) on a specific branch (to avoid building the website on every PR). There's a bunch of other cool stuff that the new setup will allow us to do – including writing front-end tests, service workers, offline support, implementing a search and so on.

This PR also includes various new docs pages and content.
Resolves #3270. Resolves #3222. Resolves #2947. Resolves #2837.


### Types of change
enhancement

## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
2019-02-17 19:31:19 +01:00

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---
title: EntityRecognizer
tag: class
source: spacy/pipeline.pyx
---
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 `"ner"`.
## EntityRecognizer.Model {#model tag="classmethod"}
Initialize a model for the pipe. The model should implement the
`thinc.neural.Model` API. Wrappers are under development for most major machine
learning libraries.
| Name | Type | Description |
| ----------- | ------ | ------------------------------------- |
| `**kwargs` | - | Parameters for initializing the model |
| **RETURNS** | object | The initialized model. |
## EntityRecognizer.\_\_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
> ner = nlp.create_pipe("ner")
>
> # Construction from class
> from spacy.pipeline import EntityRecognizer
> ner = EntityRecognizer(nlp.vocab)
> ner.from_disk("/path/to/model")
> ```
| Name | Type | Description |
| ----------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The shared vocabulary. |
| `model` | `thinc.neural.Model` or `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`. |
| `**cfg` | - | Configuration parameters. |
| **RETURNS** | `EntityRecognizer` | The newly constructed object. |
## EntityRecognizer.\_\_call\_\_ {#call tag="method"}
Apply the pipe to one document. The document is modified in place, and returned.
Both [`__call__`](/api/entityrecognizer#call) and
[`pipe`](/api/entityrecognizer#pipe) delegate to the
[`predict`](/api/entityrecognizer#predict) and
[`set_annotations`](/api/entityrecognizer#set_annotations) methods.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> doc = nlp(u"This is a sentence.")
> processed = ner(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
## EntityRecognizer.pipe {#pipe tag="method"}
Apply the pipe to a stream of documents. Both
[`__call__`](/api/entityrecognizer#call) and
[`pipe`](/api/entityrecognizer#pipe) delegate to the
[`predict`](/api/entityrecognizer#predict) and
[`set_annotations`](/api/entityrecognizer#set_annotations) methods.
> #### Example
>
> ```python
> texts = [u"One doc", u"...", u"Lots of docs"]
> ner = EntityRecognizer(nlp.vocab)
> for doc in ner.pipe(texts, 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. |
## EntityRecognizer.predict {#predict tag="method"}
Apply the pipeline's model to a batch of docs, without modifying them.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> scores = ner.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | -------- | ------------------------- |
| `docs` | iterable | The documents to predict. |
| **RETURNS** | - | Scores from the model. |
## EntityRecognizer.set_annotations {#set_annotations tag="method"}
Modify a batch of documents, using pre-computed scores.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> scores = ner.predict([doc1, doc2])
> ner.set_annotations([doc1, doc2], scores)
> ```
| Name | Type | Description |
| -------- | -------- | ---------------------------------------------------------- |
| `docs` | iterable | The documents to modify. |
| `scores` | - | The scores to set, produced by `EntityRecognizer.predict`. |
## EntityRecognizer.update {#update tag="method"}
Learn from a batch of documents and gold-standard information, updating the
pipe's model. Delegates to [`predict`](/api/entityrecognizer#predict) and
[`get_loss`](/api/entityrecognizer#get_loss).
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> losses = {}
> optimizer = nlp.begin_training()
> ner.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. |
| `sgd` | callable | The optimizer. 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. |
## EntityRecognizer.get_loss {#get_loss tag="method"}
Find the loss and gradient of loss for the batch of documents and their
predicted scores.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> scores = ner.predict([doc1, doc2])
> loss, d_loss = ner.get_loss([doc1, doc2], [gold1, gold2], scores)
> ```
| Name | Type | Description |
| ----------- | -------- | ------------------------------------------------------------ |
| `docs` | iterable | The batch of documents. |
| `golds` | iterable | The gold-standard data. Must have the same length as `docs`. |
| `scores` | - | Scores representing the model's predictions. |
| **RETURNS** | tuple | The loss and the gradient, i.e. `(loss, gradient)`. |
## EntityRecognizer.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.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> nlp.pipeline.append(ner)
> optimizer = ner.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 [`EntityRecognizer`](/api/entityrecognizer#create_optimizer) if not set. |
| **RETURNS** | callable | An optimizer. |
## EntityRecognizer.create_optimizer {#create_optimizer tag="method"}
Create an optimizer for the pipeline component.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> optimizer = ner.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | -------- | -------------- |
| **RETURNS** | callable | The optimizer. |
## EntityRecognizer.use_params {#use_params tag="method, contextmanager"}
Modify the pipe's model, to use the given parameter values.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> with ner.use_params():
> ner.to_disk('/best_model')
> ```
| Name | Type | Description |
| -------- | ---- | ---------------------------------------------------------------------------------------------------------- |
| `params` | - | The parameter values to use in the model. At the end of the context, the original parameters are restored. |
## EntityRecognizer.add_label {#add_label tag="method"}
Add a new label to the pipe.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> ner.add_label('MY_LABEL')
> ```
| Name | Type | Description |
| ------- | ------- | ----------------- |
| `label` | unicode | The label to add. |
## EntityRecognizer.to_disk {#to_disk tag="method"}
Serialize the pipe to disk.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> ner.to_disk('/path/to/ner')
> ```
| 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. |
## EntityRecognizer.from_disk {#from_disk tag="method"}
Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> ner.from_disk('/path/to/ner')
> ```
| Name | Type | Description |
| ----------- | ------------------ | -------------------------------------------------------------------------- |
| `path` | unicode / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| **RETURNS** | `EntityRecognizer` | The modified `EntityRecognizer` object. |
## EntityRecognizer.to_bytes {#to_bytes tag="method"}
> #### example
>
> ```python
> ner = EntityRecognizer(nlp.vocab)
> ner_bytes = ner.to_bytes()
> ```
Serialize the pipe to a bytestring.
| Name | Type | Description |
| ----------- | ----- | ----------------------------------------------------- |
| `**exclude` | - | Named attributes to prevent from being serialized. |
| **RETURNS** | bytes | The serialized form of the `EntityRecognizer` object. |
## EntityRecognizer.from_bytes {#from_bytes tag="method"}
Load the pipe from a bytestring. Modifies the object in place and returns it.
> #### Example
>
> ```python
> ner_bytes = ner.to_bytes()
> ner = EntityRecognizer(nlp.vocab)
> ner.from_bytes(ner_bytes)
> ```
| Name | Type | Description |
| ------------ | ------------------ | ---------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| `**exclude` | - | Named attributes to prevent from being loaded. |
| **RETURNS** | `EntityRecognizer` | The `EntityRecognizer` object. |
## EntityRecognizer.labels {#labels tag="property"}
The labels currently added to the component.
> #### Example
>
> ```python
> ner.add_label("MY_LABEL")
> assert "MY_LABEL" in ner.labels
> ```
| Name | Type | Description |
| ----------- | ----- | ---------------------------------- |
| **RETURNS** | tuple | The labels added to the component. |