model

superduper.ext.sentence_transformers.model

`SentenceTransformer`

SentenceTransformer(self,
     identifier: str,
     db: dataclasses.InitVar[typing.Optional[ForwardRef('Datalayer')]] = None,
     uuid: str = None,
     *,
     preferred_devices: 't.Sequence[str]' = ('cuda',
     'mps',
     'cpu'),
     device: str = 'cpu',
     artifacts: 'dc.InitVar[t.Optional[t.Dict]]' = None,
     signature: Literal['*args',
     '**kwargs',
     '*args,
    **kwargs',
     'singleton'] = 'singleton',
     datatype: 'EncoderArg' = None,
     output_schema: 't.Optional[Schema]' = None,
     flatten: 'bool' = False,
     model_update_kwargs: 't.Dict' = None,
     predict_kwargs: 't.Dict' = None,
     compute_kwargs: 't.Dict' = None,
     validation: 't.Optional[Validation]' = None,
     metric_values: 't.Dict' = None,
     object: Optional[sentence_transformers.SentenceTransformer.SentenceTransformer] = None,
     model: Optional[str] = None,
     preprocess: Optional[Callable] = None,
     postprocess: Optional[Callable] = None) -> None

Parameter	Description
identifier	Identifier of the leaf.
db	Datalayer instance.
uuid	UUID of the leaf.
artifacts	A dictionary of artifacts paths and `DataType` objects
signature	The signature of the model.
datatype	DataType instance.
output_schema	Output schema (mapping of encoders).
flatten	Flatten the model outputs.
model_update_kwargs	The kwargs to use for model update.
predict_kwargs	Additional arguments to use at prediction time.
compute_kwargs	Kwargs used for compute backend job submit. Example (Ray backend): compute_kwargs = dict(resources=...).
validation	The validation `Dataset` instances to use.
metric_values	The metrics to evaluate on.
object	The SentenceTransformer object to use.
model	The model name, e.g. 'all-MiniLM-L6-v2'.
device	The device to use, e.g. 'cpu' or 'cuda'.
preprocess	The preprocessing function to apply to the input.
postprocess	The postprocessing function to apply to the output.
preferred_devices	A list of devices to prefer, in that order.

A model for sentence embeddings using sentence-transformers.

SentenceTransformer​

`SentenceTransformer`