Source code for datasets.tasks.text_classification

from dataclasses import dataclass
from typing import ClassVar, Dict, Optional, Tuple

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate

[docs]@dataclass(frozen=True) class TextClassification(TaskTemplate): # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization task: str = "text-classification" input_schema: ClassVar[Features] = Features({"text": Value("string")}) # TODO(lewtun): Find a more elegant approach without descriptors. label_schema: ClassVar[Features] = Features({"labels": ClassLabel}) text_column: str = "text" label_column: str = "labels" labels: Optional[Tuple[str]] = None def __post_init__(self): if self.labels: assert len(self.labels) == len(set(self.labels)), "Labels must be unique" # Cast labels to tuple to allow hashing self.__dict__["labels"] = tuple(sorted(self.labels)) self.__dict__["label_schema"] = self.label_schema.copy() self.label_schema["labels"] = ClassLabel(names=self.labels) @property def column_mapping(self) -> Dict[str, str]: return { self.text_column: "text", self.label_column: "labels", }