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Version: Main/Unreleased

rasa.nlu.extractors.crf_entity_extractor

CRFEntityExtractorOptions Objects

class CRFEntityExtractorOptions(str, Enum)

Features that can be used for the 'CRFEntityExtractor'.

CRFEntityExtractor Objects

@DefaultV1Recipe.register(
DefaultV1Recipe.ComponentType.ENTITY_EXTRACTOR, is_trainable=True
)
class CRFEntityExtractor(GraphComponent, EntityExtractorMixin)

Implements conditional random fields (CRF) to do named entity recognition.

required_components

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

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

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,
entity_taggers: Optional[Dict[Text, "CRF"]] = None) -> None

Creates an instance of entity extractor.

create

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

Creates a new untrained component (see parent class for full docstring).

required_packages

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

Any extra python dependencies required for this component to run.

train

def train(training_data: TrainingData) -> Resource

Trains the extractor on a data set.

process

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

Augments messages with entities.

extract_entities

def extract_entities(message: Message) -> List[Dict[Text, Any]]

Extract entities from the given message using the trained model(s).

load

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

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

persist

def persist() -> None

Persist this model into the passed directory.