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"
),
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"
}