spaCy/website/docs/api/entityrecognizer.md

---
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` / `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.
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/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.")
> # This usually happens under the hood
> 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. 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/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)
> for doc in ner.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. |

## 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** | tuple    | A `(scores, tensors)` tuple where `scores` is the model's prediction for each document and `tensors` is the token representations used to predict the scores. Each tensor is an array with one row for each token in the document. |

## 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. |