The tensor represents a bag of features where each index maps to
a feature.
[InputMetadata.index_feature_mapping][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.index_feature_mapping]
must be provided for this encoding. For example:
The tensor represents a bag of features where each index maps to a
feature. Zero values in the tensor indicates feature being
non-existent.
[InputMetadata.index_feature_mapping][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.index_feature_mapping]
must be provided for this encoding. For example:
The tensor is encoded into a 1-dimensional array represented by an
encoded tensor.
[InputMetadata.encoded_tensor_name][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.encoded_tensor_name]
must be provided for this encoding. For example:
Select this encoding when the input tensor is encoded into a
2-dimensional array represented by an encoded tensor.
[InputMetadata.encoded_tensor_name][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.encoded_tensor_name]
must be provided for this encoding. The first dimension of the encoded
tensor's shape is the same as the input tensor's shape. For example:
The tensor is a list of binaries representing whether a feature exists
or not (1 indicates existence).
[InputMetadata.index_feature_mapping][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.index_feature_mapping]
must be provided for this encoding. For example:
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-03-21 UTC."],[[["This documentation details the `ExplanationMetadata.Types.InputMetadata.Types.Encoding` enum within the Vertex AI v1beta1 API for .NET, defining how features are encoded."],["The `Encoding` enum provides several options including `Identity`, `BagOfFeatures`, `BagOfFeaturesSparse`, `CombinedEmbedding`, `ConcatEmbedding`, `Indicator`, and `Unspecified` which defaults to `Identity`."],["Different encoding methods represent data in distinct formats, such as bag-of-features, sparse representations, or various types of embeddings, with specific requirements for accompanying metadata like `index_feature_mapping` or `encoded_tensor_name`."],["Each encoding type has a specific use case and description, that can be used to better understand the input when looking for explanations, such as the `BagOfFeatures` encoding, which requires a list of input index to feature names."],["This API documentation also has reference links to previous versions, such as 1.0.0-beta20, and gives context as to the latest version, 1.0.0-beta21."]]],[]]