---
title: DependencyParser
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 `"parser"`.
## DependencyParser.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. |
## DependencyParser.\_\_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
> parser = nlp.create_pipe("parser")
>
> # Construction from class
> from spacy.pipeline import DependencyParser
> parser = DependencyParser(nlp.vocab)
> parser.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** | `DependencyParser` | The newly constructed object. |
## DependencyParser.\_\_call\_\_ {#call tag="method"}
Apply the pipe to one document. The document is modified in place, and returned.
Both [`__call__`](/api/dependencyparser#call) and
[`pipe`](/api/dependencyparser#pipe) delegate to the
[`predict`](/api/dependencyparser#predict) and
[`set_annotations`](/api/dependencyparser#set_annotations) methods.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> doc = nlp(u"This is a sentence.")
> processed = parser(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------ |
| `doc` | `Doc` | The document to process. |
| **RETURNS** | `Doc` | The processed document. |
## DependencyParser.pipe {#pipe tag="method"}
Apply the pipe to a stream of documents. Both
[`__call__`](/api/dependencyparser#call) and
[`pipe`](/api/dependencyparser#pipe) delegate to the
[`predict`](/api/dependencyparser#predict) and
[`set_annotations`](/api/dependencyparser#set_annotations) methods.
> #### Example
>
> ```python
> texts = [u"One doc", u"...", u"Lots of docs"]
> parser = DependencyParser(nlp.vocab)
> for doc in parser.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. |
## DependencyParser.predict {#predict tag="method"}
Apply the pipeline's model to a batch of docs, without modifying them.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> scores = parser.predict([doc1, doc2])
> ```
| Name | Type | Description |
| ----------- | -------- | ------------------------- |
| `docs` | iterable | The documents to predict. |
| **RETURNS** | - | Scores from the model. |
## DependencyParser.set_annotations {#set_annotations tag="method"}
Modify a batch of documents, using pre-computed scores.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> scores = parser.predict([doc1, doc2])
> parser.set_annotations([doc1, doc2], scores)
> ```
| Name | Type | Description |
| -------- | -------- | ---------------------------------------------------------- |
| `docs` | iterable | The documents to modify. |
| `scores` | - | The scores to set, produced by `DependencyParser.predict`. |
## DependencyParser.update {#update tag="method"}
Learn from a batch of documents and gold-standard information, updating the
pipe's model. Delegates to [`predict`](/api/dependencyparser#predict) and
[`get_loss`](/api/dependencyparser#get_loss).
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> losses = {}
> optimizer = nlp.begin_training()
> parser.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. |
## DependencyParser.get_loss {#get_loss tag="method"}
Find the loss and gradient of loss for the batch of documents and their
predicted scores.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> scores = parser.predict([doc1, doc2])
> loss, d_loss = parser.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)`. |
## DependencyParser.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
> parser = DependencyParser(nlp.vocab)
> nlp.pipeline.append(parser)
> optimizer = parser.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 [`DependencyParser`](/api/dependencyparser#create_optimizer) if not set. |
| **RETURNS** | callable | An optimizer. |
## DependencyParser.create_optimizer {#create_optimizer tag="method"}
Create an optimizer for the pipeline component.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> optimizer = parser.create_optimizer()
> ```
| Name | Type | Description |
| ----------- | -------- | -------------- |
| **RETURNS** | callable | The optimizer. |
## DependencyParser.use_params {#use_params tag="method, contextmanager"}
Modify the pipe's model, to use the given parameter values.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> with parser.use_params():
> parser.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. |
## DependencyParser.add_label {#add_label tag="method"}
Add a new label to the pipe.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> parser.add_label('MY_LABEL')
> ```
| Name | Type | Description |
| ------- | ------- | ----------------- |
| `label` | unicode | The label to add. |
## DependencyParser.to_disk {#to_disk tag="method"}
Serialize the pipe to disk.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> parser.to_disk('/path/to/parser')
> ```
| 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. |
## DependencyParser.from_disk {#from_disk tag="method"}
Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> parser.from_disk('/path/to/parser')
> ```
| Name | Type | Description |
| ----------- | ------------------ | -------------------------------------------------------------------------- |
| `path` | unicode / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
| **RETURNS** | `DependencyParser` | The modified `DependencyParser` object. |
## DependencyParser.to_bytes {#to_bytes tag="method"}
> #### example
>
> ```python
> parser = DependencyParser(nlp.vocab)
> parser_bytes = parser.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 `DependencyParser` object. |
## DependencyParser.from_bytes {#from_bytes tag="method"}
Load the pipe from a bytestring. Modifies the object in place and returns it.
> #### Example
>
> ```python
> parser_bytes = parser.to_bytes()
> parser = DependencyParser(nlp.vocab)
> parser.from_bytes(parser_bytes)
> ```
| Name | Type | Description |
| ------------ | ------------------ | ---------------------------------------------- |
| `bytes_data` | bytes | The data to load from. |
| `**exclude` | - | Named attributes to prevent from being loaded. |
| **RETURNS** | `DependencyParser` | The `DependencyParser` object. |
## DependencyParser.labels {#labels tag="property"}
The labels currently added to the component.
> #### Example
>
> ```python
> parser.add_label("MY_LABEL")
> assert "MY_LABEL" in parser.labels
> ```
| Name | Type | Description |
| ----------- | ----- | ---------------------------------- |
| **RETURNS** | tuple | The labels added to the component. |