# Metadata — ONNX Runtime 1.14.0 documentation

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Click here to download the full example code

# Metadata

ONNX format contains metadata related to how the model was produced. It is useful when the model is deployed to production to keep track of which instance was used at a specific time. Let’s see how to do that with a simple logistic regression model trained with scikit-learn and converted with sklearn-onnx.

    from onnxruntime.datasets import get_example

    example = get_example("logreg_iris.onnx")

    import onnx

    model = onnx.load(example)

    print("doc_string={}".format(model.doc_string))
    print("domain={}".format(model.domain))
    print("ir_version={}".format(model.ir_version))
    print("metadata_props={}".format(model.metadata_props))
    print("model_version={}".format(model.model_version))
    print("producer_name={}".format(model.producer_name))
    print("producer_version={}".format(model.producer_version))

    doc_string=
    domain=onnxml
    ir_version=3
    metadata_props=[]
    model_version=0
    producer_name=OnnxMLTools
    producer_version=1.2.0.0116

With ONNX Runtime:

    import onnxruntime as rt

    sess = rt.InferenceSession(example, providers=rt.get_available_providers())
    meta = sess.get_modelmeta()

    print("custom_metadata_map={}".format(meta.custom_metadata_map))
    print("description={}".format(meta.description))
    print("domain={}".format(meta.domain, meta.domain))
    print("graph_name={}".format(meta.graph_name))
    print("producer_name={}".format(meta.producer_name))
    print("version={}".format(meta.version))

    custom_metadata_map={}
    description=
    domain=onnxml
    graph_name=3c59201b940f410fa29dc71ea9d5767d
    producer_name=OnnxMLTools
    version=0

Total running time of the script: ( 0 minutes 0.003 seconds)

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