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This is documentation for Rasa Open Source Documentation v2.0.x, which is no longer actively maintained.
For up-to-date documentation, see the latest version (2.3.x).

Version: 2.0.x

rasa.nlu.classifiers.diet_classifier

EntityTagSpec Objects

class EntityTagSpec(NamedTuple)

Specification of an entity tag present in the training data.

DIETClassifier Objects

class DIETClassifier(IntentClassifier, EntityExtractor)

DIET (Dual Intent and Entity Transformer) is a multi-task architecture for intent classification and entity recognition.

The architecture is based on a transformer which is shared for both tasks. A sequence of entity labels is predicted through a Conditional Random Field (CRF) tagging layer on top of the transformer output sequence corresponding to the input sequence of tokens. The transformer output for the __CLS__ token and intent labels are embedded into a single semantic vector space. We use the dot-product loss to maximize the similarity with the target label and minimize similarities with negative samples.

__init__

| __init__(component_config: Optional[Dict[Text, Any]] = None, index_label_id_mapping: Optional[Dict[int, Text]] = None, entity_tag_specs: Optional[List[EntityTagSpec]] = None, model: Optional[RasaModel] = None) -> None

Declare instance variables with default values.

preprocess_train_data

| preprocess_train_data(training_data: TrainingData) -> RasaModelData

Prepares data for training.

Performs sanity checks on training data, extracts encodings for labels.

train

| train(training_data: TrainingData, config: Optional[RasaNLUModelConfig] = None, **kwargs: Any, ,) -> None

Train the embedding intent classifier on a data set.

process

| process(message: Message, **kwargs: Any) -> None

Return the most likely label and its similarity to the input.

persist

| persist(file_name: Text, model_dir: Text) -> Dict[Text, Any]

Persist this model into the passed directory.

Return the metadata necessary to load the model again.

load

| @classmethod
| load(cls, meta: Dict[Text, Any], model_dir: Text = None, model_metadata: Metadata = None, cached_component: Optional["DIETClassifier"] = None, **kwargs: Any, ,) -> "DIETClassifier"

Loads the trained model from the provided directory.