Version: 3.x

rasa.nlu.extractors.mitie_entity_extractor

MitieEntityExtractor Objects

@DefaultV1Recipe.register(
DefaultV1Recipe.ComponentType.ENTITY_EXTRACTOR,
is_trainable=True,
model_from="MitieNLP",
)
class MitieEntityExtractor(GraphComponent, EntityExtractorMixin)

A Mitie Entity Extractor (which is a thin wrapper around Dlib-ml).

required_components

@classmethod
def required_components(cls) -> List[Type]

Components that should be included in the pipeline before this component.

required_packages

@staticmethod
def required_packages() -> List[Text]

Any extra python dependencies required for this component to run.

get_default_config

@staticmethod
def get_default_config() -> Dict[Text, Any]

The component's default config (see parent class for full docstring).

__init__

def __init__(config: Dict[Text, Any],
model_storage: ModelStorage,
resource: Resource,
ner: Optional["mitie.named_entity_extractor"] = None) -> None

Creates a new instance.

Arguments:

  • config - The configuration.
  • model_storage - Storage which graph components can use to persist and load themselves.
  • resource - Resource locator for this component which can be used to persist and load itself from the model_storage.
  • ner - Mitie named entity extractor

validate_config

def validate_config(cls, config: Dict[Text, Any]) -> None

Checks whether the given configuration is valid.

Arguments:

  • config - a configuration for a Mitie entity extractor component

create

@classmethod
def create(cls, config: Dict[Text, Any], model_storage: ModelStorage,
resource: Resource,
execution_context: ExecutionContext) -> GraphComponent

Creates a new MitieEntityExtractor.

Arguments:

  • config - This config overrides the default_config.
  • model_storage - Storage which graph components can use to persist and load themselves.
  • resource - Resource locator for this component which can be used to persist and load itself from the model_storage.
  • execution_context - Information about the current graph run. Unused.
  • Returns - An instantiated MitieEntityExtractor.

train

def train(training_data: TrainingData, model: MitieModel) -> Resource

Trains a MITIE named entity recognizer.

Arguments:

  • training_data - the training data
  • model - a MitieModel

Returns:

resource for loading the trained model

process

def process(messages: List[Message], model: MitieModel) -> List[Message]

Extracts entities from messages and appends them to the attribute.

If no patterns where found during training, then the given messages will not be modified. In particular, if no ENTITIES attribute exists yet, then it will not be created.

If no pattern can be found in the given message, then no entities will be added to any existing list of entities. However, if no ENTITIES attribute exists yet, then an ENTITIES attribute will be created.

Returns:

the given list of messages that have been modified

load

@classmethod
def load(cls, config: Dict[Text, Any], model_storage: ModelStorage,
resource: Resource, execution_context: ExecutionContext,
**kwargs: Any) -> MitieEntityExtractor

Loads trained component (see parent class for full docstring).

persist

def persist() -> None

Persist this model.