xpark.dataset.TextClassify

class xpark.dataset.TextClassify(labels: list[str | dict[str, str]], /, *, base_url: str, model: str, api_key: str = 'NOT_SET', max_qps: int | None = None, max_retries: int = 0, multi_label: bool = False, fallback_response: str | list[str] | None = 'NOT_SET', **kwargs: dict[str, Any])

TextClassify processor extracts the single label that best matches the text content.

Parameters:

• labels –

  The labels to classify into. Accepts two formats:

  • list[str]: plain label names, e.g. ["science", "sport"]

  • list[dict]: dicts with "label" (required) and "description" (optional), e.g. [{"label": "science", "description": "natural science and research"}]

  Descriptions are injected into the prompt to guide the model when label names alone are ambiguous.

• base_url – The base URL of the LLM server.

• model – The request model name.

• api_key – The request API key.

• max_qps – The maximum number of requests per second.

• max_retries – The maximum number of retries per request in the event of failures. We retry with exponential backoff upto this specific maximum retries.

• fallback_response – The response value to return when the LLM request fails. If set to None, the exception will be raised instead.

• multi_label – If True, the processor will return a list of labels that match the text content.

• **kwargs – Keyword arguments to pass to the openai.AsyncClient.chat.completions.create API.

Examples

from xpark.dataset.expressions import col
from xpark.dataset import TextClassify, from_items

ds = from_items(
    [
        "The research team discovered a new exoplanet orbiting a nearby star.",
        "Manchester United secured a dramatic victory in the final minutes of the match.",
        "The government introduced new policies to reduce carbon emissions over the next decade.",
    ]
)

# Plain labels
ds = ds.with_column(
    "class",
    TextClassify(
        ["science", "sport", "politics"],
        model="deepseek-v3-0324",
        base_url=os.getenv("LLM_ENDPOINT"),
        api_key=os.getenv("LLM_API_KEY"),
    )
    .options(num_workers={"IO": 1})
    .with_column(col("item")),
)

# Labels with descriptions
ds = ds.with_column(
    "class",
    TextClassify(
        [
            {"label": "science", "description": "natural science, research, and technology"},
            {"label": "sport", "description": "sports events and athletic competitions"},
            {"label": "politics", "description": "government policies and political affairs"},
        ],
        model="deepseek-v3-0324",
        base_url=os.getenv("LLM_ENDPOINT"),
        api_key=os.getenv("LLM_API_KEY"),
    )
    .options(num_workers={"IO": 1})
    .with_column(col("item")),
)

Methods

__call__(texts)	Call self as a function.
options(**kwargs)
with_column(texts)

__call__(texts: pa.ChunkedArray) → pa.Array

Call self as a function.

options(**kwargs: Unpack[ExprUDFOptions]) → Self

with_column(texts: pa.ChunkedArray) → pa.Array