hanlp¶

hanlp.load(save_dir: str, verbose=None, **kwargs) → hanlp.common.component.Component [source]¶

Load a pretrained component from an identifier.

Parameters

save_dir (str) – The identifier to the saved component. It could be a remote URL or a local path.
verbose – True to print loading progress.
**kwargs – Arguments passed to hanlp.common.torch_component.TorchComponent.load(), e.g., devices is a useful argument to specify which GPU devices a PyTorch component will use.

Examples:

import hanlp
# Load component onto the 0-th GPU.
hanlp.load(..., devices=0)
# Load component onto the 0-th and 1-st GPU using data parallelization.
hanlp.load(..., devices=[0,1])

Note

A component can have dependencies on other components or resources, which will be recursively loaded. So it’s common to see multiple downloading messages per single load.

Returns: A pretrained component.
Return type: hanlp.common.component.Component

hanlp.pipeline(*pipes) → hanlp.components.pipeline.Pipeline[source]¶

Creates a pipeline of components. It’s made for bundling KerasComponents. For TorchComponent, use MultiTaskLearning instead.

Parameters: *pipes – Components if pre-defined any.
Returns: A pipeline, which is list of components in order.

HanLP documentation

hanlp¶