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Overview

The run_dataset_evaluation method allows you to run evaluations on a dataset using one or more evaluators. This is essential for measuring model performance and data quality.

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Method Signature

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Synchronous

def run_dataset_evaluation(
    dataset_id: str,
    evaluator_ids: List[str],
    evaluation_name: Optional[str] = None
) -> Dict[str, Any]

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Asynchronous

async def run_dataset_evaluation(
    dataset_id: str,
    evaluator_ids: List[str],
    evaluation_name: Optional[str] = None
) -> Dict[str, Any]

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Parameters

ParameterTypeRequiredDescription
dataset_idstrYesThe unique identifier of the dataset to evaluate
evaluator_idsList[str]YesList of evaluator IDs to use for evaluation
evaluation_namestrNoOptional name for the evaluation run

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Returns

Returns a dictionary containing the evaluation job information and status.

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Examples

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Basic Usage

from keywordsai import KeywordsAI

client = KeywordsAI(api_key="your-api-key")

# Run evaluation on dataset
evaluation = client.datasets.run_dataset_evaluation(
    dataset_id="dataset_123",
    evaluator_ids=["evaluator_456", "evaluator_789"]
)

print(f"Evaluation started: {evaluation['evaluation_id']}")
print(f"Status: {evaluation['status']}")

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With Custom Name

# Run evaluation with custom name
evaluation = client.datasets.run_dataset_evaluation(
    dataset_id="dataset_123",
    evaluator_ids=["evaluator_456"],
    evaluation_name="Quality Check - Week 1"
)

print(f"Named evaluation '{evaluation['name']}' started")

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Asynchronous Usage

import asyncio
from keywordsai import AsyncKeywordsAI

async def run_evaluation_example():
    client = AsyncKeywordsAI(api_key="your-api-key")
    
    evaluation = await client.datasets.run_dataset_evaluation(
        dataset_id="dataset_123",
        evaluator_ids=["evaluator_456", "evaluator_789"],
        evaluation_name="Async Evaluation"
    )
    
    print(f"Evaluation {evaluation['evaluation_id']} started")
    return evaluation

asyncio.run(run_evaluation_example())

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Multiple Evaluators

# Run comprehensive evaluation with multiple evaluators
evaluators = [
    "accuracy_evaluator",
    "relevance_evaluator",
    "safety_evaluator"
]

evaluation = client.datasets.run_dataset_evaluation(
    dataset_id="dataset_123",
    evaluator_ids=evaluators,
    evaluation_name="Comprehensive Quality Check"
)

print(f"Running {len(evaluators)} evaluators on dataset")

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Error Handling

try:
    evaluation = client.datasets.run_dataset_evaluation(
        dataset_id="dataset_123",
        evaluator_ids=["evaluator_456"]
    )
    print(f"Evaluation started successfully: {evaluation['evaluation_id']}")
except Exception as e:
    print(f"Error starting evaluation: {e}")

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Common Use Cases

  • Quality assurance for training datasets
  • Model performance benchmarking
  • Automated dataset validation
  • A/B testing of different model versions
  • Compliance and safety checks