🌈 Types

class text_machina.src.types.DetectionLabels(value)

Bases: Enum

Labels for detection tasks.

GENERATED: str = 'generated'

HUMAN: str = 'human'

class text_machina.src.types.LabeledSpan(**data)

Bases: BaseModel

check_valid_positions()

Return type:

LabeledSpan

end: int

label: str

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'end': FieldInfo(annotation=int, required=True), 'label': FieldInfo(annotation=str, required=True), 'start': FieldInfo(annotation=int, required=True, metadata=[Ge(ge=0)])}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

start: int

class text_machina.src.types.Placeholders(value)

Bases: Enum

Placeholders to be used across TextMachina.

NO_EXTRACTOR: str = 'NO-EXTRACTOR'

NO_PROMPT: str = 'NO-PROMPT'

class text_machina.src.types.Prompt(**data)

Bases: BaseModel

Wrapper for a prompt.

extractor: str

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'extractor': FieldInfo(annotation=str, required=True), 'template': FieldInfo(annotation=str, required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

template: str

class text_machina.src.types.PromptedDataset(**data)

Bases: BaseModel

Wrapper for a prompted dataset used to generate MGT texts.

human_texts: List[str]

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'human_texts': FieldInfo(annotation=List[str], required=True), 'prompted_texts': FieldInfo(annotation=List[str], required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

prompted_texts: List[str]

class text_machina.src.types.TaskType(value)

Bases: str, Enum

An enumeration.

ATTRIBUTION: str = 'attribution'

BOUNDARY: str = 'boundary'

DETECTION: str = 'detection'

MIXCASE: str = 'mixcase'