notice

This is documentation for Rasa & Rasa Pro Documentation v2.x, which is no longer actively maintained.
For up-to-date documentation, see the latest version (3.x).

Version: 2.x

rasa.nlu.extractors.extractor

EntityTagSpec Objects

class EntityTagSpec(NamedTuple)

Specification of an entity tag present in the training data.

EntityExtractor Objects

class EntityExtractor(Component)

Entity extractors are components which extract entities.

They can be placed in the pipeline like other components, and can extract entities like a person's name, or a location.

add_extractor_name

| add_extractor_name(entities: List[Dict[Text, Any]]) -> List[Dict[Text, Any]]

Adds this extractor's name to a list of entities.

Arguments:

  • entities - the extracted entities.

Returns:

the modified entities.

add_processor_name

| add_processor_name(entity: Dict[Text, Any]) -> Dict[Text, Any]

Adds this extractor's name to the list of processors for this entity.

Arguments:

  • entity - the extracted entity and its metadata.

Returns:

the modified entity.

init_split_entities

| init_split_entities() -> Dict[Text, bool]

Initialises the behaviour for splitting entities by comma (or not).

Returns:

Defines desired behaviour for splitting specific entity types and default behaviour for splitting any entity types for which no behaviour is defined.

filter_irrelevant_entities

| @staticmethod
| filter_irrelevant_entities(extracted: list, requested_dimensions: set) -> list

Only return dimensions the user configured.

filter_trainable_entities

| filter_trainable_entities(entity_examples: List[Message]) -> List[Message]

Filters out untrainable entity annotations.

Creates a copy of entity_examples in which entities that have extractor set to something other than self.name (e.g. 'CRFEntityExtractor') are removed.

convert_predictions_into_entities

| @staticmethod
| convert_predictions_into_entities(text: Text, tokens: List[Token], tags: Dict[Text, List[Text]], split_entities_config: Dict[Text, bool] = None, confidences: Optional[Dict[Text, List[float]]] = None) -> List[Dict[Text, Any]]

Convert predictions into entities.

Arguments:

  • text - The text message.
  • tokens - Message tokens without CLS token.
  • tags - Predicted tags.
  • split_entities_config - config for handling splitting a list of entities
  • confidences - Confidences of predicted tags.

Returns:

Entities.

get_tag_for

| @staticmethod
| get_tag_for(tags: Dict[Text, List[Text]], tag_name: Text, idx: int) -> Text

Get the value of the given tag name from the list of tags.

Arguments:

  • tags - Mapping of tag name to list of tags;
  • tag_name - The tag name of interest.
  • idx - The index position of the tag.

Returns:

The tag value.

check_correct_entity_annotations

| @staticmethod
| check_correct_entity_annotations(training_data: TrainingData) -> None

Check if entities are correctly annotated in the training data.

If the start and end values of an entity do not match any start and end values of the respected token, we define an entity as misaligned and log a warning.

Arguments:

  • training_data - The training data.