model

superduper.ext.cohere.model

`CohereEmbed`

CohereEmbed(self,
     identifier: str,
     db: dataclasses.InitVar[typing.Optional[ForwardRef('Datalayer')]] = None,
     uuid: str = None,
     *,
     artifacts: 'dc.InitVar[t.Optional[t.Dict]]' = None,
     signature: str = '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,
     model: 't.Optional[str]' = None,
     max_batch_size: 'int' = 8,
     client_kwargs: Dict[str,
     Any] = None,
     shape: Optional[Sequence[int]] = None,
     batch_size: int = 100) -> 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	Model signature.
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.
model	The Model to use, e.g. `'text-embedding-ada-002'`
max_batch_size	Maximum batch size.
client_kwargs	The keyword arguments to pass to the client.
shape	The shape as `tuple` of the embedding.
batch_size	The batch size to use for the predictor.

Cohere embedding predictor.

`CohereGenerate`

CohereGenerate(self,
     identifier: str,
     db: dataclasses.InitVar[typing.Optional[ForwardRef('Datalayer')]] = None,
     uuid: str = None,
     *,
     artifacts: 'dc.InitVar[t.Optional[t.Dict]]' = None,
     signature: str = '*args,
    **kwargs',
     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,
     model: 't.Optional[str]' = None,
     max_batch_size: 'int' = 8,
     client_kwargs: Dict[str,
     Any] = None,
     takes_context: bool = True,
     prompt: str = '') -> 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	Model signature.
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.
model	The Model to use, e.g. `'text-embedding-ada-002'`
max_batch_size	Maximum batch size.
client_kwargs	The keyword arguments to pass to the client.
takes_context	Whether the model takes context into account.
prompt	The prompt to use to seed the response.

Cohere realistic text generator (chat predictor).

`Cohere`

Cohere(self,
     identifier: str,
     db: dataclasses.InitVar[typing.Optional[ForwardRef('Datalayer')]] = None,
     uuid: str = None,
     *,
     artifacts: 'dc.InitVar[t.Optional[t.Dict]]' = None,
     signature: 'Signature' = '*args,
    **kwargs',
     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,
     model: 't.Optional[str]' = None,
     max_batch_size: 'int' = 8,
     client_kwargs: Dict[str,
     Any] = 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	Model signature.
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.
model	The Model to use, e.g. `'text-embedding-ada-002'`
max_batch_size	Maximum batch size.
client_kwargs	The keyword arguments to pass to the client.

Cohere predictor.

CohereEmbed​

CohereGenerate​

Cohere​

`CohereEmbed`

`CohereGenerate`

`Cohere`