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neuralset.extractors.text.TfidfEmbedding

pydantic model neuralset.extractors.text.TfidfEmbedding

Get TF-IDF embeddings for Sentence events.

Fields:

• event_types (str | tuple[str, ...])

• max_features (int)

field event_types: str | tuple[str, ...] = 'Sentence'

field max_features: int = 5000

property vectorizer: Any

prepare(obj: DataFrame | Sequence[Event] | Sequence[Segment]) → None

Pre-compute and cache extractor data for a collection of events.

This method triggers _get_data on every matching event so that expensive computation (e.g. model inference) is done once and cached. It then calls the extractor on a single event to populate the output shape, which is needed when allow_missing=True.

Call prepare before using the extractor in a dataloader.

Parameters:

obj (DataFrame or sequence of Event or sequence of Segment) – The structure containing the events. When calling prepare on several objects, prefer passing a list of events or segments over a DataFrame to avoid redundant conversion overhead.

get_embedding(text: str, language: str = '') → ndarray

requirements: tp.ClassVar[tuple[str, ...]] = ('rapidfuzz', 'rapidfuzz')

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