TextEmbedding

API reference for TextEmbedding

TextEmbedding

Represents custom features derived from text embeddings with TF-IDF analysis.

TextEmbedding extends VectorFeature to handle text-based embeddings with additional text-specific analysis capabilities. It combines vector clustering with TF-IDF analysis to provide both semantic clustering and keyword extraction for text data.

The feature type is automatically set to CustomFeatureType.FROM_TEXT_EMBEDDING and uses clustering combined with TF-IDF summarization for drift computation.

Examples

# Creating a text embedding feature for review analysis:

    text_feature = TextEmbedding(
    name=”review_sentiment_clusters”,
    column=”review_embedding”,
    source_column=”review_text”,
    n_clusters=8,
    n_tags=20

)

    # Creating a feature for document classification:

    doc_feature = TextEmbedding(
    name=”document_topic_clusters”,
    column=”doc_embedding”,
    source_column=”document_content”,
    n_clusters=12,
    n_tags=15

)

classmethod validate_n_tags(value)

Parameters

value (int) Return type: int