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.
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.