mirror of https://github.com/explosion/spaCy.git
458 lines
23 KiB
Markdown
458 lines
23 KiB
Markdown
---
|
|
title: EntityRecognizer
|
|
tag: class
|
|
source: spacy/pipeline/ner.pyx
|
|
teaser: 'Pipeline component for named entity recognition'
|
|
api_base_class: /api/pipe
|
|
api_string_name: ner
|
|
api_trainable: true
|
|
---
|
|
|
|
A transition-based named entity recognition component. The entity recognizer
|
|
identifies **non-overlapping labelled spans** of tokens. The transition-based
|
|
algorithm used encodes certain assumptions that are effective for "traditional"
|
|
named entity recognition tasks, but may not be a good fit for every span
|
|
identification problem. Specifically, the loss function optimizes for **whole
|
|
entity accuracy**, so if your inter-annotator agreement on boundary tokens is
|
|
low, the component will likely perform poorly on your problem. The
|
|
transition-based algorithm also assumes that the most decisive information about
|
|
your entities will be close to their initial tokens. If your entities are long
|
|
and characterized by tokens in their middle, the component will likely not be a
|
|
good fit for your task.
|
|
|
|
## 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.ner import DEFAULT_NER_MODEL
|
|
> config = {
|
|
> "moves": None,
|
|
> "update_with_oracle_cut_size": 100,
|
|
> "model": DEFAULT_NER_MODEL,
|
|
> }
|
|
> nlp.add_pipe("ner", config=config)
|
|
> ```
|
|
|
|
| Setting | Description |
|
|
| ----------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `moves` | A list of transition names. Inferred from the data if not provided. Defaults to `None`. ~~Optional[List[str]]~~ |
|
|
| `update_with_oracle_cut_size` | During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. Defaults to `100`. ~~int~~ |
|
|
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [TransitionBasedParser](/api/architectures#TransitionBasedParser). ~~Model[List[Doc], List[Floats2d]]~~ |
|
|
|
|
```python
|
|
%%GITHUB_SPACY/spacy/pipeline/ner.pyx
|
|
```
|
|
|
|
## EntityRecognizer.\_\_init\_\_ {#init tag="method"}
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> # Construction via add_pipe with default model
|
|
> ner = nlp.add_pipe("ner")
|
|
>
|
|
> # Construction via add_pipe with custom model
|
|
> config = {"model": {"@architectures": "my_ner"}}
|
|
> parser = nlp.add_pipe("ner", config=config)
|
|
>
|
|
> # Construction from class
|
|
> from spacy.pipeline import EntityRecognizer
|
|
> ner = EntityRecognizer(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).
|
|
|
|
| Name | Description |
|
|
| ----------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `vocab` | The shared vocabulary. ~~Vocab~~ |
|
|
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
|
|
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
|
|
| `moves` | A list of transition names. Inferred from the data if not provided. ~~Optional[List[str]]~~ |
|
|
| _keyword-only_ | |
|
|
| `update_with_oracle_cut_size` | During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. `100` is a good default. ~~int~~ |
|
|
|
|
## 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
|
|
> doc = nlp("This is a sentence.")
|
|
> ner = nlp.add_pipe("ner")
|
|
> # This usually happens under the hood
|
|
> processed = ner(doc)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | -------------------------------- |
|
|
| `doc` | The document to process. ~~Doc~~ |
|
|
| **RETURNS** | The processed document. ~~Doc~~ |
|
|
|
|
## 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 = nlp.add_pipe("ner")
|
|
> for doc in ner.pipe(docs, batch_size=50):
|
|
> pass
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------- |
|
|
| `docs` | 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~~ |
|
|
|
|
## EntityRecognizer.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. 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) and lets you customize
|
|
arguments it receives via the
|
|
[`[initialize.components]`](/api/data-formats#config-initialize) block in the
|
|
config.
|
|
|
|
<Infobox variant="warning" title="Changed in v3.0" id="begin_training">
|
|
|
|
This method was previously called `begin_training`.
|
|
|
|
</Infobox>
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> ner = nlp.add_pipe("ner")
|
|
> ner.initialize(lambda: [], nlp=nlp)
|
|
> ```
|
|
>
|
|
> ```ini
|
|
> ### config.cfg
|
|
> [initialize.components.ner]
|
|
>
|
|
> [initialize.components.ner.labels]
|
|
> @readers = "spacy.read_labels.v1"
|
|
> path = "corpus/labels/ner.json
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
|
|
| _keyword-only_ | |
|
|
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
|
|
| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Dict[str, Dict[str, int]]]~~ |
|
|
|
|
## EntityRecognizer.predict {#predict tag="method"}
|
|
|
|
Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
|
|
modifying them.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> ner = nlp.add_pipe("ner")
|
|
> scores = ner.predict([doc1, doc2])
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ------------------------------------------------------------- |
|
|
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
|
|
| **RETURNS** | A helper class for the parse state (internal). ~~StateClass~~ |
|
|
|
|
## EntityRecognizer.set_annotations {#set_annotations tag="method"}
|
|
|
|
Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> ner = nlp.add_pipe("ner")
|
|
> scores = ner.predict([doc1, doc2])
|
|
> ner.set_annotations([doc1, doc2], scores)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------- | ------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
|
|
| `scores` | The scores to set, produced by `EntityRecognizer.predict`. Returns an internal helper class for the parse state. ~~List[StateClass]~~ |
|
|
|
|
## EntityRecognizer.update {#update tag="method"}
|
|
|
|
Learn from a batch of [`Example`](/api/example) objects, updating the pipe's
|
|
model. Delegates to [`predict`](/api/entityrecognizer#predict) and
|
|
[`get_loss`](/api/entityrecognizer#get_loss).
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> ner = nlp.add_pipe("ner")
|
|
> optimizer = nlp.initialize()
|
|
> losses = ner.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]~~ |
|
|
|
|
## 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 = nlp.add_pipe("ner")
|
|
> scores = ner.predict([eg.predicted for eg in examples])
|
|
> loss, d_loss = ner.get_loss(examples, scores)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | --------------------------------------------------------------------------- |
|
|
| `examples` | The batch of examples. ~~Iterable[Example]~~ |
|
|
| `scores` | Scores representing the model's predictions. ~~StateClass~~ |
|
|
| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
|
|
|
|
## EntityRecognizer.score {#score tag="method" new="3"}
|
|
|
|
Score a batch of examples.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> scores = ner.score(examples)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | --------------------------------------------------------- |
|
|
| `examples` | The examples to score. ~~Iterable[Example]~~ |
|
|
| **RETURNS** | The scores. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
|
|
|
|
## EntityRecognizer.create_optimizer {#create_optimizer tag="method"}
|
|
|
|
Create an optimizer for the pipeline component.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> ner = nlp.add_pipe("ner")
|
|
> optimizer = ner.create_optimizer()
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ---------------------------- |
|
|
| **RETURNS** | The optimizer. ~~Optimizer~~ |
|
|
|
|
## EntityRecognizer.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
|
|
> ner = EntityRecognizer(nlp.vocab)
|
|
> with ner.use_params(optimizer.averages):
|
|
> ner.to_disk("/best_model")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------- | -------------------------------------------------- |
|
|
| `params` | The parameter values to use in the model. ~~dict~~ |
|
|
|
|
## EntityRecognizer.add_label {#add_label tag="method"}
|
|
|
|
Add a new label to the pipe. Note that you don't have to call this method if you
|
|
provide a **representative data sample** to the [`initialize`](#initialize)
|
|
method. In this case, all labels found in the sample will be automatically added
|
|
to the model, and the output dimension will be
|
|
[inferred](/usage/layers-architectures#thinc-shape-inference) automatically.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> ner = nlp.add_pipe("ner")
|
|
> ner.add_label("MY_LABEL")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ----------------------------------------------------------- |
|
|
| `label` | The label to add. ~~str~~ |
|
|
| **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
|
|
|
|
## EntityRecognizer.set_output {#set_output tag="method"}
|
|
|
|
Change the output dimension of the component's model by calling the model's
|
|
attribute `resize_output`. This is a function that takes the original model and
|
|
the new output dimension `nO`, and changes the model in place. When resizing an
|
|
already trained model, care should be taken to avoid the "catastrophic
|
|
forgetting" problem.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> ner = nlp.add_pipe("ner")
|
|
> ner.set_output(512)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ---- | --------------------------------- |
|
|
| `nO` | The new output dimension. ~~int~~ |
|
|
|
|
## EntityRecognizer.to_disk {#to_disk tag="method"}
|
|
|
|
Serialize the pipe to disk.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> ner = nlp.add_pipe("ner")
|
|
> ner.to_disk("/path/to/ner")
|
|
> ```
|
|
|
|
| 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]~~ |
|
|
|
|
## EntityRecognizer.from_disk {#from_disk tag="method"}
|
|
|
|
Load the pipe from disk. Modifies the object in place and returns it.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> ner = nlp.add_pipe("ner")
|
|
> ner.from_disk("/path/to/ner")
|
|
> ```
|
|
|
|
| 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 `EntityRecognizer` object. ~~EntityRecognizer~~ |
|
|
|
|
## EntityRecognizer.to_bytes {#to_bytes tag="method"}
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> ner = nlp.add_pipe("ner")
|
|
> ner_bytes = ner.to_bytes()
|
|
> ```
|
|
|
|
Serialize the pipe to a bytestring.
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------------------------------------- |
|
|
| _keyword-only_ | |
|
|
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
|
| **RETURNS** | The serialized form of the `EntityRecognizer` object. ~~bytes~~ |
|
|
|
|
## 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 = nlp.add_pipe("ner")
|
|
> ner.from_bytes(ner_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 `EntityRecognizer` object. ~~EntityRecognizer~~ |
|
|
|
|
## 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 | Description |
|
|
| ----------- | ------------------------------------------------------ |
|
|
| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
|
|
|
|
## EntityRecognizer.label_data {#label_data tag="property" new="3"}
|
|
|
|
The labels currently added to the component and their internal meta information.
|
|
This is the data generated by [`init labels`](/api/cli#init-labels) and used by
|
|
[`EntityRecognizer.initialize`](/api/entityrecognizer#initialize) to initialize
|
|
the model with a pre-defined label set.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> labels = ner.label_data
|
|
> ner.initialize(lambda: [], nlp=nlp, labels=labels)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ------------------------------------------------------------------------------- |
|
|
| **RETURNS** | The label data added to the component. ~~Dict[str, Dict[str, Dict[str, int]]]~~ |
|
|
|
|
## 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 = ner.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. |
|