final class SequenceMetadata extends GeneratedMessageV3 with SequenceMetadataOrBuilder
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- def getDefaultInstanceForType(): SequenceMetadata
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- def getJointGroup(): String
An arbitrary string defining a "group" of features that could be modeled as a single joint sequence. For example, consider a dataset that contains three sequential features "purchase_time", "product_id", "purchase_price". These belong to the same sequence of purchases and could be modeled jointly. Specifying joint_group = "purchase" on all three sequences would communicate that the features can be considered part of a single conceptual sequence.
An arbitrary string defining a "group" of features that could be modeled as a single joint sequence. For example, consider a dataset that contains three sequential features "purchase_time", "product_id", "purchase_price". These belong to the same sequence of purchases and could be modeled jointly. Specifying joint_group = "purchase" on all three sequences would communicate that the features can be considered part of a single conceptual sequence.
optional string joint_group = 4;- returns
The jointGroup.
- Definition Classes
- SequenceMetadata → SequenceMetadataOrBuilder
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- @Override()
- def getJointGroupBytes(): ByteString
An arbitrary string defining a "group" of features that could be modeled as a single joint sequence. For example, consider a dataset that contains three sequential features "purchase_time", "product_id", "purchase_price". These belong to the same sequence of purchases and could be modeled jointly. Specifying joint_group = "purchase" on all three sequences would communicate that the features can be considered part of a single conceptual sequence.
An arbitrary string defining a "group" of features that could be modeled as a single joint sequence. For example, consider a dataset that contains three sequential features "purchase_time", "product_id", "purchase_price". These belong to the same sequence of purchases and could be modeled jointly. Specifying joint_group = "purchase" on all three sequences would communicate that the features can be considered part of a single conceptual sequence.
optional string joint_group = 4;- returns
The bytes for jointGroup.
- Definition Classes
- SequenceMetadata → SequenceMetadataOrBuilder
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- @Override()
- def getOneofFieldDescriptor(oneof: OneofDescriptor): FieldDescriptor
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- GeneratedMessageV3 → AbstractMessage → MessageOrBuilder
- def getParserForType(): Parser[SequenceMetadata]
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- SequenceMetadata → GeneratedMessageV3 → Message → MessageLite
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- def getRepeatedField(field: FieldDescriptor, index: Int): AnyRef
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- def getRepeatedFieldCount(field: FieldDescriptor): Int
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- def getSequenceTruncationLimit(): Long
Specifies the maximum sequence length that should be processed. Sequences may exceed this limit but are expected to be truncated by modeling layers.
Specifies the maximum sequence length that should be processed. Sequences may exceed this limit but are expected to be truncated by modeling layers.
optional int64 sequence_truncation_limit = 5;- returns
The sequenceTruncationLimit.
- Definition Classes
- SequenceMetadata → SequenceMetadataOrBuilder
- Annotations
- @Override()
- def getSequentialStatus(): SequentialStatus
optional .tensorflow.metadata.v0.SequenceMetadata.SequentialStatus sequential_status = 3;optional .tensorflow.metadata.v0.SequenceMetadata.SequentialStatus sequential_status = 3;- returns
The sequentialStatus.
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- SequenceMetadata → SequenceMetadataOrBuilder
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- def getSerializedSize(): Int
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- SequenceMetadata → GeneratedMessageV3 → AbstractMessage → MessageLite
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- def hasField(field: FieldDescriptor): Boolean
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- def hasJointGroup(): Boolean
An arbitrary string defining a "group" of features that could be modeled as a single joint sequence. For example, consider a dataset that contains three sequential features "purchase_time", "product_id", "purchase_price". These belong to the same sequence of purchases and could be modeled jointly. Specifying joint_group = "purchase" on all three sequences would communicate that the features can be considered part of a single conceptual sequence.
An arbitrary string defining a "group" of features that could be modeled as a single joint sequence. For example, consider a dataset that contains three sequential features "purchase_time", "product_id", "purchase_price". These belong to the same sequence of purchases and could be modeled jointly. Specifying joint_group = "purchase" on all three sequences would communicate that the features can be considered part of a single conceptual sequence.
optional string joint_group = 4;- returns
Whether the jointGroup field is set.
- Definition Classes
- SequenceMetadata → SequenceMetadataOrBuilder
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- @Override()
- def hasOneof(oneof: OneofDescriptor): Boolean
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- GeneratedMessageV3 → AbstractMessage → MessageOrBuilder
- def hasSequenceTruncationLimit(): Boolean
Specifies the maximum sequence length that should be processed. Sequences may exceed this limit but are expected to be truncated by modeling layers.
Specifies the maximum sequence length that should be processed. Sequences may exceed this limit but are expected to be truncated by modeling layers.
optional int64 sequence_truncation_limit = 5;- returns
Whether the sequenceTruncationLimit field is set.
- Definition Classes
- SequenceMetadata → SequenceMetadataOrBuilder
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- @Override()
- def hasSequentialStatus(): Boolean
optional .tensorflow.metadata.v0.SequenceMetadata.SequentialStatus sequential_status = 3;optional .tensorflow.metadata.v0.SequenceMetadata.SequentialStatus sequential_status = 3;- returns
Whether the sequentialStatus field is set.
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- SequenceMetadata → SequenceMetadataOrBuilder
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