hezar.models.model_outputs module

Define all model outputs here

class hezar.models.model_outputs.Image2TextOutput(text: str | None = None, score: str | None = None)

Bases: ModelOutput

score: str | None = None

text: str | None = None

class hezar.models.model_outputs.MaskFillingOutput(token: int | None = None, sequence: str | None = None, token_id: str | None = None, score: float | None = None)

Bases: ModelOutput

score: float | None = None

sequence: str | None = None

token: int | None = None

token_id: str | None = None

class hezar.models.model_outputs.ModelOutput

Bases: object

Base class for all models’ prediction outputs (model.predict()/model.post_process() outputs).

Note that prediction outputs must all be a list of ModelOutput objects since we consider only batch inferences.

The helper functions in the class enable it to be treated as a mapping or a dict object.

items()

keys()

to_dict()

values()

class hezar.models.model_outputs.SequenceLabelingOutput(token: list[list[str]] | None = None, label: list[list[str]] | None = None, start: int | None = None, end: int | None = None, score: list[list[float]] | None = None)

Bases: ModelOutput

end: int | None = None

label: list[list[str]] | None = None

score: list[list[float]] | None = None

start: int | None = None

token: list[list[str]] | None = None

class hezar.models.model_outputs.SpeechRecognitionOutput(text: str | None = None, chunks: list[dict] | None = None)

Bases: ModelOutput

chunks: list[dict] | None = None

text: str | None = None

class hezar.models.model_outputs.TextClassificationOutput(label: str | None = None, score: float | None = None)

Bases: ModelOutput

label: str | None = None

score: float | None = None

class hezar.models.model_outputs.TextDetectionOutput(boxes: list[int] | None = None)

Bases: ModelOutput

boxes: list[int] | None = None

class hezar.models.model_outputs.TextGenerationOutput(text: str | None = None)

Bases: ModelOutput

text: str | None = None