Version: 3.x
rasa.nlu.utils._spacy_utils
SpacyNLP Objects
class SpacyNLP(Component)
The core component that links spaCy to related components in the pipeline.
load_model
| @staticmethod
| load_model(spacy_model_name: Text) -> "Language"
Try loading the model, catching the OSError if missing.
provide_context
| provide_context() -> Dict[Text, Any]
Creates a context dictionary from spaCy nlp object.
doc_for_text
| doc_for_text(text: Text) -> "Doc"
Makes a spaCy doc object from a string of text.
preprocess_text
| preprocess_text(text: Optional[Text]) -> Text
Processes the text before it is handled by spaCy.
merge_content_lists
| @staticmethod
| merge_content_lists(indexed_training_samples: List[Tuple[int, Text]], doc_lists: List[Tuple[int, "Doc"]]) -> List[Tuple[int, "Doc"]]
Merge lists with processed Docs back into their original order.
filter_training_samples_by_content
| @staticmethod
| filter_training_samples_by_content(indexed_training_samples: List[Tuple[int, Text]]) -> Tuple[List[Tuple[int, Text]], List[Tuple[int, Text]]]
Separates empty training samples from content bearing ones.
process_content_bearing_samples
| process_content_bearing_samples(samples_to_pipe: List[Tuple[int, Text]]) -> List[Tuple[int, "Doc"]]
Sends content bearing training samples to spaCy's pipe.
process_non_content_bearing_samples
| process_non_content_bearing_samples(empty_samples: List[Tuple[int, Text]]) -> List[Tuple[int, "Doc"]]
Creates empty Doc-objects from zero-lengthed training samples strings.
load
| @classmethod
| load(cls, meta: Dict[Text, Any], model_dir: Text, model_metadata: "Metadata" = None, cached_component: Optional["SpacyNLP"] = None, **kwargs: Any, ,) -> "SpacyNLP"
Loads trained component (see parent class for full docstring).
ensure_proper_language_model
| @staticmethod
| ensure_proper_language_model(nlp: Optional["Language"]) -> None
Checks if the spacy language model is properly loaded.
Raises an exception if the model is invalid.