nemo_curator.eval.llm_judge

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Package Contents

Classes

NameDescription
LLMJudgeWorkflowEnd-to-end config-driven LLM judge workflow.

API

class nemo_curator.eval.llm_judge.LLMJudgeWorkflow(
judge_config: str | pathlib.Path,
input_path: str,
output_path: str,
files_per_partition: int | None = None,
language: str | None = None,
fasttext_langid_model_path: str | None = None,
min_langid_score: float = 0.3,
language_text_field: str = 'raw_text',
checkpoint_path: str | None = None
)
Dataclass

Bases: WorkflowBase

End-to-end config-driven LLM judge workflow.

Loads a judge config YAML (models, Jinja prompt templates, score rubrics, and execution.stages), starts a Dynamo/vLLM inference server hosting the configured judge models, then runs one Curator pipeline containing: reader -> optional FastText language gate -> one NDD DataDesignerStage (+ its filters) per judge stage -> writer.

checkpoint_path
str | None = None
config
dict[str, object] = field(init=False)
config_path
Path = field(init=False)
fasttext_langid_model_path
str | None = None
files_per_partition
int | None = None
input_format
DataFormat = 'jsonl'
input_path
str
judge_config
str | Path
language
str | None = None
language_text_field
str = 'raw_text'
min_langid_score
float = 0.3
output_format
DataFormat = 'jsonl'
output_path
str
nemo_curator.eval.llm_judge.LLMJudgeWorkflow.__post_init__() -> None
nemo_curator.eval.llm_judge.LLMJudgeWorkflow._build_judge_stages(
endpoint: str
) -> list[tuple[str, data_designer.config.DataDesignerConfigBuilder, list[data_designer.config.ModelProvider], dict[str, object] | None, int | None, list[dict[str, object]]]]
nemo_curator.eval.llm_judge.LLMJudgeWorkflow.run() -> nemo_curator.pipeline.workflow.WorkflowRunResult

Run the complete LLM judge pipeline.

Returns: WorkflowRunResult

WorkflowRunResult containing the pipeline output tasks and timing metadata.