Model

srai.embedders.Model

Bases: LightningModule

Class for model based on LightningModule.

get_config

get_config() -> dict[str, Any]

Get model config.

Source code in srai/embedders/_base.py

def get_config(self) -> dict[str, Any]:
    """Get model config."""
    model_config = {
        k: v
        for k, v in vars(self).items()
        if k[0] != "_"
        and k
        not in (
            "training",
            "prepare_data_per_node",
            "allow_zero_length_dataloader_with_multiple_devices",
        )
    }

    return model_config

load

classmethod

load(path: Union[Path, str], **kwargs: Any) -> Model

Load model from a file.

PARAMETER	DESCRIPTION
path	Path to the file. TYPE: Union[Path, str]
**kwargs	Additional kwargs to pass to the model constructor. TYPE: dict DEFAULT: {}

Source code in srai/embedders/_base.py

@classmethod
def load(cls, path: Union[Path, str], **kwargs: Any) -> "Model":
    """
    Load model from a file.

    Args:
        path (Union[Path, str]): Path to the file.
        **kwargs (dict): Additional kwargs to pass to the model constructor.
    """
    import torch

    if isinstance(path, str):
        path = Path(path)

    model = cls(**kwargs)
    model.load_state_dict(torch.load(path))
    return model

save

save(path: Union[Path, str]) -> None

Save the model to a directory.

PARAMETER	DESCRIPTION
path	Path to the directory. TYPE: Path

Source code in srai/embedders/_base.py

def save(self, path: Union[Path, str]) -> None:
    """
    Save the model to a directory.

    Args:
        path (Path): Path to the directory.
    """
    import torch

    torch.save(self.state_dict(), path)