langtest.utils.custom_types.sample.ToxicitySample#

class ToxicitySample(*, prompt: str, completion: str = None, prompt_toxicity: str | List = None, completion_toxicity: str = None, state: str = None, dataset_name: str = None, task: str = 'toxicity', category: str = None, test_type: str = None)#

Bases: BaseModel

A class Representing a sample for toxicity task.

prompt#

The prompt text.

Type:

str

completion#

The completion text.

Type:

str

prompt_toxicity#

The toxicity of the prompt text.

Type:

Union[str, List]

completion_toxicity#

The toxicity of the completion text.

Type:

str

state#

The state of the sample.

Type:

str

dataset_name#

The name of the dataset the sample belongs to.

Type:

str

task#

The task associated with the sample.

Type:

str

category#

The category of the sample.

Type:

str

test_type#

The type of test the sample belongs to.

Type:

str

__init__(**data)#

Constructor method

Methods

__init__(**data)	Constructor method
construct([_fields_set])	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
copy(*[, include, exclude, update, deep])	Duplicate a model, optionally choose which fields to include, exclude and change.
dict(*[, include, exclude, by_alias, ...])	Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
from_orm(obj)
is_pass()	Checks if the sample passes based on the maximum score.
json(*[, include, exclude, by_alias, ...])	Generate a JSON representation of the model, include and exclude arguments as per dict().
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
run(model, **kwargs)	Runs the original and perturbed sentences through the model
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
to_dict()	Converts the ToxicitySample object to a dictionary.
update_forward_refs(**localns)	Try to update ForwardRefs on fields based on this Model, globalns and localns.
validate(value)

Attributes

prompt
completion
prompt_toxicity
completion_toxicity
state
dataset_name
task
category
test_type

classmethod construct(_fields_set: SetStr | None = None, **values: Any) → Model#

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: DictStrAny | None = None, deep: bool = False) → Model#

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters:

• include – fields to include in new model

• exclude – fields to exclude from new model, as with values this takes precedence over include

• update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

• deep – set to True to make a deep copy of the model

Returns:

new model instance

dict(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny#

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

is_pass() → bool#

Checks if the sample passes based on the maximum score.

json(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

run(model, **kwargs)#

Runs the original and perturbed sentences through the model

to_dict() → Dict[str, Any]#

Converts the ToxicitySample object to a dictionary.

Returns:

A dictionary representation of the ToxicitySample object.

Return type:

Dict[str, Any]

classmethod update_forward_refs(**localns: Any) → None#

Try to update ForwardRefs on fields based on this Model, globalns and localns.