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TextClassification

TextClassification is a Singleton task that generates a JSON object containing an example for a text classification task.

Inputs

  • task_description (str): The description of the text classification task.
  • language (str): The language in which the texts should be written.
  • clarity (str): The clarity of the input text (e.g., "clear", "ambiguous").
  • difficulty (str): The education level required to understand the input text (e.g., "college", "high school").

Outputs

  • classification_example (dict): A JSON object containing 'input_text', 'label', and 'misleading_label' for the specified text classification task.

Example

Generate a text classification example based on the given parameters. This example uses the GEMMA2_9B_FP16 model.

import os
import asyncio
import json
from dria.factory import TextClassification
from dria.client import Dria
from dria.models import Task, Model

dria = Dria(rpc_token=os.environ["DRIA_RPC_TOKEN"])


async def evaluate():
    text_classification = TextClassification()
    res = await dria.execute(
        Task(
            workflow=text_classification.workflow(
                task_description="Classify movie reviews as positive or negative",
                language="English",
                clarity="clear",
                difficulty="high school"
            ).model_dump(),
            models=[Model.GEMMA2_9B_FP16],
        ),
        timeout=45,
    )
    return text_classification.parse_result(res)


def main():
    result = asyncio.run(evaluate())
    print(json.dumps(result, indent=2))


if __name__ == "__main__":
    main()

Expected output

{
  "input_text": "The plot was somewhat predictable, but the performances were top-notch and kept me engaged throughout.",
  "label": "positive",
  "misleading_label": "negative",
  "model": "qwen2.5:32b-instruct-fp16"
}

References