langtest.transform.sensitivity.AddNegation#

class AddNegation#

Bases: BaseSensitivity

A class for negating sensitivity-related phrases in the input text.

This class identifies common sensitivity-related phrases such as ‘is’, ‘was’, ‘are’, and ‘were’ in the input text and replaces them with their negations to make the text less sensitive.

alias_name#

The alias name for this sensitivity transformation.

Type:: str

transform(sample_list: List[Sample]) -> List[Sample]: Applies the sensitivity negation transformation to a list of samples.

__init__()#

Methods

`__init__`()
`async_run`(sample_list, model, **kwargs)	Creates a task to run the sensitivity measure.
`run`(sample_list, model, **kwargs)	Abstract method that implements the sensitivity measure.
`transform`(sample_list)	Abstract method that implements the sensitivity measure.

Attributes

`alias_name`
`supported_tasks`
`test_types`

class TestConfig#

Bases: dict

clear() → None. Remove all items from D.#

copy() → a shallow copy of D#

fromkeys(value=None, /)#: Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)#: Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items#

keys() → a set-like object providing a view on D's keys#

pop(k[, d]) → v, remove specified key and return the corresponding value.#: If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()#

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)#

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None. Update D from dict/iterable E and F.#: If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values#

async classmethod async_run(sample_list: List[Sample], model: ModelAPI, **kwargs)#

Creates a task to run the sensitivity measure.

Parameters:

sample_list (List[Sample]) – The input data to be transformed.
model (ModelAPI) – The model to be used for evaluation.
**kwargs – Additional arguments to be passed to the sensitivity measure.

Returns:

The task that runs the sensitivity measure.

Return type:

asyncio.Task

abstract async static run(sample_list: List[Sample], model: ModelAPI, **kwargs) → List[Sample]#

Abstract method that implements the sensitivity measure.

Parameters:

sample_list (List[Sample]) – The input data to be transformed.
model (ModelAPI) – The model to be used for evaluation.
**kwargs – Additional arguments to be passed to the sensitivity measure.

Returns:

The transformed data based on the implemented sensitivity measure.

Return type:

List[Sample]

static transform(sample_list: List[Sample]) → List[Sample]#

Abstract method that implements the sensitivity measure.

Parameters:: sample_list (List[Sample]) – The input data to be transformed.
Returns:: The transformed data based on the implemented sensitivity measure.
Return type:: Any