Pipeline

TextCategorizer

class
String name:textcatBase class:TrainablePipeTrainable:
Pipeline component for text classification

The text categorizer predicts categories over a whole document. and comes in two flavours: textcat and textcat_multilabel. When you need to predict exactly one true label per document, use the textcat which has mutually exclusive labels. If you want to perform multi-label classification and predict zero, one or more labels per document, use the textcat_multilabel component instead.

Both components are documented on this page.

Config and implementation

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 or in your config.cfg for training. See the model architectures documentation for details on the architectures and their arguments and hyperparameters.

SettingDescription
thresholdCutoff to consider a prediction “positive”, relevant when printing accuracy results. float
modelA model instance that predicts scores for each category. Defaults to TextCatEnsemble. Model[List[Doc], List[Floats2d]]
explosion/spaCy/master/spacy/pipeline/textcat.py
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TextCategorizer.__init__ 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.add_pipe.

NameDescription
vocabThe shared vocabulary. Vocab
modelThe Thinc Model powering the pipeline component. Model[List[Doc], List[Floats2d]]
nameString name of the component instance. Used to add entries to the losses during training. str
keyword-only
thresholdCutoff to consider a prediction “positive”, relevant when printing accuracy results. float

TextCategorizer.__call__ 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__ and pipe delegate to the predict and set_annotations methods.

NameDescription
docThe document to process. Doc

TextCategorizer.pipe 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__ and pipe delegate to the predict and set_annotations methods.

NameDescription
streamA stream of documents. Iterable[Doc]
keyword-only
batch_sizeThe number of documents to buffer. Defaults to 128. int

TextCategorizer.initialize methodv3.0

Initialize the component for training. get_examples should be a function that returns an iterable of 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 and setting up the label scheme based on the data. This method is typically called by Language.initialize and lets you customize arguments it receives via the [initialize.components] block in the config.

NameDescription
get_examplesFunction that returns gold-standard annotations in the form of Example objects. Callable[[], Iterable[Example]]
keyword-only
nlpThe current nlp object. Defaults to None. Optional[Language]
labelsThe label information to add to the component, as provided by the label_data property after initialization. To generate a reusable JSON file from your data, you should run the 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[Iterable[str]]
positive_labelThe positive label for a binary task with exclusive classes, None otherwise and by default. This parameter is not available when using the textcat_multilabel component. Optional[str]

TextCategorizer.predict method

Apply the component’s model to a batch of Doc objects without modifying them.

NameDescription
docsThe documents to predict. Iterable[Doc]

TextCategorizer.set_annotations method

Modify a batch of Doc objects using pre-computed scores.

NameDescription
docsThe documents to modify. Iterable[Doc]
scoresThe scores to set, produced by TextCategorizer.predict.

TextCategorizer.update method

Learn from a batch of Example objects containing the predictions and gold-standard annotations, and update the component’s model. Delegates to predict and get_loss.

NameDescription
examplesA batch of Example objects to learn from. Iterable[Example]
keyword-only
dropThe dropout rate. float
sgdAn optimizer. Will be created via create_optimizer if not set. Optional[Optimizer]
lossesOptional record of the loss during training. Updated using the component name as the key. Optional[Dict[str, float]]

TextCategorizer.rehearse methodexperimentalv3.0

Perform a “rehearsal” update from a batch of data. Rehearsal updates teach the current model to make predictions similar to an initial model to try to address the “catastrophic forgetting” problem. This feature is experimental.

NameDescription
examplesA batch of Example objects to learn from. Iterable[Example]
keyword-only
dropThe dropout rate. float
sgdAn optimizer. Will be created via create_optimizer if not set. Optional[Optimizer]
lossesOptional record of the loss during training. Updated using the component name as the key. Optional[Dict[str, float]]

TextCategorizer.get_loss method

Find the loss and gradient of loss for the batch of documents and their predicted scores.

NameDescription
examplesThe batch of examples. Iterable[Example]
scoresScores representing the model’s predictions.

TextCategorizer.score methodv3.0

Score a batch of examples.

NameDescription
examplesThe examples to score. Iterable[Example]
keyword-only

TextCategorizer.create_optimizer method

Create an optimizer for the pipeline component.

NameDescription

TextCategorizer.use_params methodcontextmanager

Modify the pipe’s model to use the given parameter values.

NameDescription
paramsThe parameter values to use in the model. dict

TextCategorizer.add_label method

Add a new label to the pipe. Raises an error if the output dimension is already set, or if the model has already been fully initialized. Note that you don’t have to call this method if you provide a representative data sample to the 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 automatically.

NameDescription
labelThe label to add. str

TextCategorizer.to_disk method

Serialize the pipe to disk.

NameDescription
pathA 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
excludeString names of serialization fields to exclude. Iterable[str]

TextCategorizer.from_disk method

Load the pipe from disk. Modifies the object in place and returns it.

NameDescription
pathA path to a directory. Paths may be either strings or Path-like objects. Union[str, Path]
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

TextCategorizer.to_bytes method

Serialize the pipe to a bytestring.

NameDescription
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

TextCategorizer.from_bytes method

Load the pipe from a bytestring. Modifies the object in place and returns it.

NameDescription
bytes_dataThe data to load from. bytes
keyword-only
excludeString names of serialization fields to exclude. Iterable[str]

TextCategorizer.labels property

The labels currently added to the component.

NameDescription

TextCategorizer.label_data propertyv3.0

The labels currently added to the component and their internal meta information. This is the data generated by init labels and used by TextCategorizer.initialize to initialize the model with a pre-defined label set.

NameDescription

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.

NameDescription
vocabThe shared Vocab.
cfgThe config file. You usually don’t want to exclude this.
modelThe binary model data. You usually don’t want to exclude this.