GliNER

Bases: InternalEngine[PromptSignature, Result, Model, InferenceMode]

Source code in sieves/engines/glix_.py

class GliX(InternalEngine[PromptSignature, Result, Model, InferenceMode]):
    def __init__(
        self,
        model: Model,
        init_kwargs: dict[str, Any] | None,
        inference_kwargs: dict[str, Any] | None,
        strict_mode: bool,
        batch_size: int,
    ):
        super().__init__(model, init_kwargs, inference_kwargs, strict_mode, batch_size)
        self._model_wrappers: dict[InferenceMode, gliner.multitask.base.GLiNERBasePipeline] = {}

    @property
    def inference_modes(self) -> type[InferenceMode]:
        return InferenceMode

    @property
    def supports_few_shotting(self) -> bool:
        return False

    def build_executable(
        self,
        inference_mode: InferenceMode,
        prompt_template: str | None,
        prompt_signature: type[PromptSignature] | PromptSignature,
        fewshot_examples: Iterable[pydantic.BaseModel] = (),
    ) -> Executable[Result]:
        assert isinstance(prompt_signature, list)
        cls_name = self.__class__.__name__
        if len(list(fewshot_examples)):
            warnings.warn(f"Few-shot examples are not supported by engine {cls_name}.")

        # Lazily initialize multi-task wrapper for underlying GliNER model.
        if inference_mode not in self._model_wrappers:
            self._model_wrappers[inference_mode] = inference_mode.value(model=self._model)

        model = self._model_wrappers[inference_mode]

        # Overwrite prompt default template, if template specified. Note that this is a static prompt and GliX doesn't
        # do few-shotting, so we don't inject anything into the template.
        if prompt_template:
            self._model.prompt = jinja2.Template(prompt_template).render()

        def execute(values: Iterable[dict[str, Any]]) -> Iterable[Result]:
            """Execute prompts with engine for given values.
            :param values: Values to inject into prompts.
            :return Iterable[Result]: Results for prompts.
            """
            try:
                params: dict[InferenceMode, dict[str, Any]] = {
                    InferenceMode.classification: {"classes": prompt_signature, "multi_label": True},
                    InferenceMode.question_answering: {"questions": prompt_signature},
                    InferenceMode.summarization: {},
                    InferenceMode.ner: {"entity_types": prompt_signature},
                }
                selected_params = params[inference_mode]  # Select parameters based on inference mode
            except KeyError:
                raise ValueError(f"Inference mode {inference_mode} not supported by {cls_name} engine.")

            batch_size = self._batch_size if self._batch_size != -1 else sys.maxsize
            # Ensure values are read as generator for standardized batch handling (otherwise we'd have to use
            # different batch handling depending on whether lists/tuples or generators are used).
            values = (v for v in values)

            while batch := [vals["text"] for vals in itertools.islice(values, batch_size)]:
                if len(batch) == 0:
                    break
                if inference_mode == InferenceMode.ner:
                    yield from self._model.batch_predict_entities(texts=batch, labels=selected_params["entity_types"])
                else:
                    assert isinstance(selected_params, dict)
                    yield from model(batch, **(selected_params | self._inference_kwargs))

        return execute

model property

Return model instance.

Returns:

Type	Description
EngineModel	Model instance.

deserialize(config, **kwargs) classmethod

Generate Engine instance from config.

Parameters:

Name	Type	Description	Default
config	Config	Config to generate instance from.	required
kwargs	dict[str, Any]	Values to inject into loaded config.	{}

Returns:

Type	Description
InternalEngine[EnginePromptSignature, EngineResult, EngineModel, EngineInferenceMode]	Deserialized Engine instance.

Source code in sieves/engines/core.py

@classmethod
def deserialize(
    cls, config: Config, **kwargs: dict[str, Any]
) -> InternalEngine[EnginePromptSignature, EngineResult, EngineModel, EngineInferenceMode]:
    """Generate Engine instance from config.
    :param config: Config to generate instance from.
    :param kwargs: Values to inject into loaded config.
    :return: Deserialized Engine instance.
    """
    return cls(**config.to_init_dict(cls, **kwargs))

serialize()

Serializes engine.

Returns:

Type	Description
Config	Config instance.

Source code in sieves/engines/core.py

def serialize(self) -> Config:
    """Serializes engine.
    :return: Config instance.
    """
    return Config.create(self.__class__, self._attributes)