from skl2onnx.common.data_types import StringTensorType # Define input data types as a dictionary input_types = {'input': StringTensorType([None, 10])} # Convert scikit-learn model to ONNX onnx_model = skl2onnx.convert_sklearn(model, 'my_model', initial_types=input_types)
from skl2onnx.common.data_types import StringTensorType # Define output data types as a dictionary output_types = {'output': StringTensorType([None])} # Generate ONNX graph graph = onnx.helper.make_graph(nodes, 'my_model', inputs, outputs, initializer) # Create ONNX model model = onnx.helper.make_model(graph, producer_name='my_producer') # Set output data types model.graph.output[0].type.CopyFrom(output_types['output'].to_onnx_type())In this example, StringTensorType is used to define the output data type of an ONNX model. The output is a one-dimensional tensor with variable size. In summary, StringTensorType is a data type in SKL2ONNX used to represent tensors with string values. It is used to define input and output data types of machine learning models.