Pipeline component for model evaluation for supported tasks. \ Generates predictions on a given model, followed by computing model performance metrics to score the model quality for supported tasks.

Version: 0.0.36

type : evaluation sub_type : subgraph

View in Studio: https://ml.azure.com/registries/azureml/components/model_evaluation_pipeline/version/0.0.36

Name	Description	Type	Default	Optional	Enum
compute_name		string	serverless
instance_type		string	STANDARD_NC24S_V3

model prediction

Name	Description	Type	Default	Optional	Enum
task	Task type	string	tabular-classification		['tabular-classification', 'tabular-classification-multilabel', 'tabular-regression', 'text-classification', 'text-classification-multilabel', 'text-named-entity-recognition', 'text-summarization', 'question-answering', 'text-translation', 'text-generation', 'fill-mask', 'image-classification', 'image-classification-multilabel', 'chat-completion', 'image-object-detection', 'image-instance-segmentation']
test_data	Test Data	uri_folder		False
mlflow_model	Mlflow Model (could be a registered model or part of another pipeline	mlflow_model		False
label_column_name	Label column name in provided test dataset (Ex: label)	string		True
input_column_names	Input column names in provided test dataset (Ex : column1). Add comma delimited values in case of multiple input columns (Ex : column1,column2)	string		True
device		string	auto	False	['auto', 'cpu', 'gpu']
batch_size		integer		True

compute metrics

Name	Description	Type	Default	Optional	Enum
evaluation_config	Additional parameters required for evaluation. See How to create a config here	uri_file		True
evaluation_config_params	JSON Serialized string of evaluation_config	string		True
openai_config_params	Required OpenAI Params for calculating GPT Based metrics for QnA task	string		True

Name	Description	Type
evaluation_result	Output dir to save the evaluation result	uri_folder