DocumentClassificationSettings

Settings for DocumentClassification. Values fall back through three levels: document → SDK → built-in default. Writes target the document only when set on a document’s settings, otherwise the SDK globally when set on SdkSettings.

Tags: Vision

from nutrient_sdk import DocumentClassificationSettings

Construction

# Per-document override
with Document.open("input.pdf") as doc:
    settings = doc.settings.document_classification_settings
    settings.some_field = new_value          # mutate fields directly

# SDK-wide default (applies to all documents)
SdkSettings.document_classification_settings.some_field = new_value

Settings are configured by writing to fields on the returned object. The settings property itself cannot be reassigned — doc.settings.document_classification_settings = other_settings is rejected.

Properties

image_weight

@property
def image_weight(self) -> float

@image_weight.setter
def image_weight(self, value: float) -> None

Relative weight of the image branch in the fused prediction. Set to 0 to use the text branch alone. Default 0.5.

Type: float

Default: 0.5f

pooling_temperature

@property
def pooling_temperature(self) -> float

@pooling_temperature.setter
def pooling_temperature(self, value: float) -> None

Margin-weighted pooling temperature used by both branches to combine the per-chunk (text) and per-page (image) distributions into one. Higher values let the most distinctive chunk/page dominate more aggressively; values approaching 0 approach a plain average. Default 8.

Type: float

Default: 8f

text

@property
def text(self) -> str

@text.setter
def text(self, value: str) -> None

Document text for the text branch. When non-empty, this text is scored directly. When empty (default), the text is derived from the document by the extraction pipeline at the tier set by Engine (e.g. AdaptiveOcr for fast OCR, Icr for local layout models, VlmEnhancedIcr for VLM-enhanced) — multi-page documents are extracted in full. Set TextWeight to 0 to skip the text branch (and its extraction) entirely.

Type: str

Default: string.Empty

text_weight

@property
def text_weight(self) -> float

@text_weight.setter
def text_weight(self, value: float) -> None

Relative weight of the text branch in the fused prediction. The fused probability for a label is (TextWeight·Pt + ImageWeight·Pi) / (TextWeight + ImageWeight). Set to 0 to use the image branch alone. Default 0.5.

Type: float

Default: 0.5f

top_k

@property
def top_k(self) -> int

@top_k.setter
def top_k(self, value: int) -> None

Maximum number of ranked predictions to return. 0 (default) returns all candidates, ranked by fused probability descending.

Type: int

Default: 0