Class TrainCustomModelRequest.GcsTrainingInput.Builder (0.49.0)

public static final class TrainCustomModelRequest.GcsTrainingInput.Builder extends GeneratedMessageV3.Builder<TrainCustomModelRequest.GcsTrainingInput.Builder> implements TrainCustomModelRequest.GcsTrainingInputOrBuilder

Cloud Storage training data input.

Protobuf type google.cloud.discoveryengine.v1alpha.TrainCustomModelRequest.GcsTrainingInput

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > TrainCustomModelRequest.GcsTrainingInput.Builder

Implements

TrainCustomModelRequest.GcsTrainingInputOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

AbstractMessage.Builder.mergeFrom(byte[])

AbstractMessage.Builder.mergeFrom(byte[],ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[],int,int)

AbstractMessage.Builder.mergeFrom(byte[],int,int,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(ByteString)

AbstractMessage.Builder.mergeFrom(ByteString,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(CodedInputStream)

AbstractMessage.Builder.mergeFrom(CodedInputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(Message)

AbstractMessage.Builder.mergeFrom(InputStream)

AbstractMessage.Builder.mergeFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.newUninitializedMessageException(Message)

AbstractMessage.Builder.toString()

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,Collection<? super T>)

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,List<? super T>)

AbstractMessageLite.Builder.internalMergeFrom(MessageType)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

AbstractMessageLite.Builder.mergeFrom(MessageLite)

AbstractMessageLite.Builder.newUninitializedMessageException(MessageLite)

GeneratedMessageV3.Builder.addRepeatedField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.clear()

GeneratedMessageV3.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.clone()

GeneratedMessageV3.Builder.getAllFields()

GeneratedMessageV3.Builder.getDescriptorForType()

GeneratedMessageV3.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.getParentForChildren()

GeneratedMessageV3.Builder.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldBuilder(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMapFieldReflection(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMutableMapFieldReflection(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)

GeneratedMessageV3.Builder.setField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.setRepeatedField(Descriptors.FieldDescriptor,int,Object)

com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)

GeneratedMessageV3.Builder.setUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.setUnknownFieldsProto3(UnknownFieldSet)

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns
Type	Description
`Descriptor`

Methods

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public TrainCustomModelRequest.GcsTrainingInput.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
`field`	`FieldDescriptor`
`value`	`Object`

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.addRepeatedField(Descriptors.FieldDescriptor field, Object value)

build()

public TrainCustomModelRequest.GcsTrainingInput build()

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput`

buildPartial()

public TrainCustomModelRequest.GcsTrainingInput buildPartial()

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput`

clear()

public TrainCustomModelRequest.GcsTrainingInput.Builder clear()

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearCorpusDataPath()

public TrainCustomModelRequest.GcsTrainingInput.Builder clearCorpusDataPath()

The Cloud Storage corpus data which could be associated in train data. The data path format is gs://<bucket_to_data>/<jsonl_file_name>. A newline delimited jsonl/ndjson file.

For search-tuning model, each line should have the _id, title and text. Example: {"_id": "doc1", title: "relevant doc", "text": "relevant text"}

string corpus_data_path = 1;

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`	This builder for chaining.

clearField(Descriptors.FieldDescriptor field)

public TrainCustomModelRequest.GcsTrainingInput.Builder clearField(Descriptors.FieldDescriptor field)

Parameter
Name	Description
`field`	`FieldDescriptor`

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearField(Descriptors.FieldDescriptor field)

clearOneof(Descriptors.OneofDescriptor oneof)

public TrainCustomModelRequest.GcsTrainingInput.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter
Name	Description
`oneof`	`OneofDescriptor`

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearOneof(Descriptors.OneofDescriptor oneof)

clearQueryDataPath()

public TrainCustomModelRequest.GcsTrainingInput.Builder clearQueryDataPath()

The gcs query data which could be associated in train data. The data path format is gs://<bucket_to_data>/<jsonl_file_name>. A newline delimited jsonl/ndjson file.

For search-tuning model, each line should have the _id and text. Example: {"_id": "query1", "text": "example query"}

string query_data_path = 2;

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`	This builder for chaining.

clearTestDataPath()

public TrainCustomModelRequest.GcsTrainingInput.Builder clearTestDataPath()

Cloud Storage test data. Same format as train_data_path. If not provided, a random 80/20 train/test split will be performed on train_data_path.

string test_data_path = 4;

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`	This builder for chaining.

clearTrainDataPath()

public TrainCustomModelRequest.GcsTrainingInput.Builder clearTrainDataPath()

Cloud Storage training data path whose format should be gs://<bucket_to_data>/<tsv_file_name>. The file should be in tsv format. Each line should have the doc_id and query_id and score (number).

For search-tuning model, it should have the query-id corpus-id score as tsv file header. The score should be a number in [0, inf+). The larger the number is, the more relevant the pair is. Example:

query-id\tcorpus-id\tscore
query1\tdoc1\t1

string train_data_path = 3;

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`	This builder for chaining.

clone()

public TrainCustomModelRequest.GcsTrainingInput.Builder clone()

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

getCorpusDataPath()

public String getCorpusDataPath()

The Cloud Storage corpus data which could be associated in train data. The data path format is gs://<bucket_to_data>/<jsonl_file_name>. A newline delimited jsonl/ndjson file.

For search-tuning model, each line should have the _id, title and text. Example: {"_id": "doc1", title: "relevant doc", "text": "relevant text"}

string corpus_data_path = 1;

Returns
Type	Description
`String`	The corpusDataPath.

getCorpusDataPathBytes()

public ByteString getCorpusDataPathBytes()

The Cloud Storage corpus data which could be associated in train data. The data path format is gs://<bucket_to_data>/<jsonl_file_name>. A newline delimited jsonl/ndjson file.

For search-tuning model, each line should have the _id, title and text. Example: {"_id": "doc1", title: "relevant doc", "text": "relevant text"}

string corpus_data_path = 1;

Returns
Type	Description
`ByteString`	The bytes for corpusDataPath.

getDefaultInstanceForType()

public TrainCustomModelRequest.GcsTrainingInput getDefaultInstanceForType()

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput`

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns
Type	Description
`Descriptor`

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

getQueryDataPath()

public String getQueryDataPath()

The gcs query data which could be associated in train data. The data path format is gs://<bucket_to_data>/<jsonl_file_name>. A newline delimited jsonl/ndjson file.

For search-tuning model, each line should have the _id and text. Example: {"_id": "query1", "text": "example query"}

string query_data_path = 2;

Returns
Type	Description
`String`	The queryDataPath.

getQueryDataPathBytes()

public ByteString getQueryDataPathBytes()

The gcs query data which could be associated in train data. The data path format is gs://<bucket_to_data>/<jsonl_file_name>. A newline delimited jsonl/ndjson file.

For search-tuning model, each line should have the _id and text. Example: {"_id": "query1", "text": "example query"}

string query_data_path = 2;

Returns
Type	Description
`ByteString`	The bytes for queryDataPath.

getTestDataPath()

public String getTestDataPath()

Cloud Storage test data. Same format as train_data_path. If not provided, a random 80/20 train/test split will be performed on train_data_path.

string test_data_path = 4;

Returns
Type	Description
`String`	The testDataPath.

getTestDataPathBytes()

public ByteString getTestDataPathBytes()

Cloud Storage test data. Same format as train_data_path. If not provided, a random 80/20 train/test split will be performed on train_data_path.

string test_data_path = 4;

Returns
Type	Description
`ByteString`	The bytes for testDataPath.

getTrainDataPath()

public String getTrainDataPath()

Cloud Storage training data path whose format should be gs://<bucket_to_data>/<tsv_file_name>. The file should be in tsv format. Each line should have the doc_id and query_id and score (number).

For search-tuning model, it should have the query-id corpus-id score as tsv file header. The score should be a number in [0, inf+). The larger the number is, the more relevant the pair is. Example:

query-id\tcorpus-id\tscore
query1\tdoc1\t1

string train_data_path = 3;

Returns
Type	Description
`String`	The trainDataPath.

getTrainDataPathBytes()

public ByteString getTrainDataPathBytes()

Cloud Storage training data path whose format should be gs://<bucket_to_data>/<tsv_file_name>. The file should be in tsv format. Each line should have the doc_id and query_id and score (number).

For search-tuning model, it should have the query-id corpus-id score as tsv file header. The score should be a number in [0, inf+). The larger the number is, the more relevant the pair is. Example:

query-id\tcorpus-id\tscore
query1\tdoc1\t1

string train_data_path = 3;

Returns
Type	Description
`ByteString`	The bytes for trainDataPath.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns
Type	Description
`FieldAccessorTable`

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns
Type	Description
`boolean`

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeFrom(TrainCustomModelRequest.GcsTrainingInput other)

public TrainCustomModelRequest.GcsTrainingInput.Builder mergeFrom(TrainCustomModelRequest.GcsTrainingInput other)

Parameter
Name	Description
`other`	`TrainCustomModelRequest.GcsTrainingInput`

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public TrainCustomModelRequest.GcsTrainingInput.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
`input`	`CodedInputStream`
`extensionRegistry`	`ExtensionRegistryLite`

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Exceptions
Type	Description
`IOException`

mergeFrom(Message other)

public TrainCustomModelRequest.GcsTrainingInput.Builder mergeFrom(Message other)

Parameter
Name	Description
`other`	`Message`

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(Message other)

mergeUnknownFields(UnknownFieldSet unknownFields)

public final TrainCustomModelRequest.GcsTrainingInput.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
`unknownFields`	`UnknownFieldSet`

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.mergeUnknownFields(UnknownFieldSet unknownFields)

setCorpusDataPath(String value)

public TrainCustomModelRequest.GcsTrainingInput.Builder setCorpusDataPath(String value)

The Cloud Storage corpus data which could be associated in train data. The data path format is gs://<bucket_to_data>/<jsonl_file_name>. A newline delimited jsonl/ndjson file.

For search-tuning model, each line should have the _id, title and text. Example: {"_id": "doc1", title: "relevant doc", "text": "relevant text"}

string corpus_data_path = 1;

Parameter
Name	Description
`value`	`String` The corpusDataPath to set.

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`	This builder for chaining.

setCorpusDataPathBytes(ByteString value)

public TrainCustomModelRequest.GcsTrainingInput.Builder setCorpusDataPathBytes(ByteString value)

The Cloud Storage corpus data which could be associated in train data. The data path format is gs://<bucket_to_data>/<jsonl_file_name>. A newline delimited jsonl/ndjson file.

For search-tuning model, each line should have the _id, title and text. Example: {"_id": "doc1", title: "relevant doc", "text": "relevant text"}

string corpus_data_path = 1;

Parameter
Name	Description
`value`	`ByteString` The bytes for corpusDataPath to set.

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`	This builder for chaining.

setField(Descriptors.FieldDescriptor field, Object value)

public TrainCustomModelRequest.GcsTrainingInput.Builder setField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
`field`	`FieldDescriptor`
`value`	`Object`

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.setField(Descriptors.FieldDescriptor field, Object value)

setQueryDataPath(String value)

public TrainCustomModelRequest.GcsTrainingInput.Builder setQueryDataPath(String value)

The gcs query data which could be associated in train data. The data path format is gs://<bucket_to_data>/<jsonl_file_name>. A newline delimited jsonl/ndjson file.

For search-tuning model, each line should have the _id and text. Example: {"_id": "query1", "text": "example query"}

string query_data_path = 2;

Parameter
Name	Description
`value`	`String` The queryDataPath to set.

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`	This builder for chaining.

setQueryDataPathBytes(ByteString value)

public TrainCustomModelRequest.GcsTrainingInput.Builder setQueryDataPathBytes(ByteString value)

The gcs query data which could be associated in train data. The data path format is gs://<bucket_to_data>/<jsonl_file_name>. A newline delimited jsonl/ndjson file.

For search-tuning model, each line should have the _id and text. Example: {"_id": "query1", "text": "example query"}

string query_data_path = 2;

Parameter
Name	Description
`value`	`ByteString` The bytes for queryDataPath to set.

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`	This builder for chaining.

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public TrainCustomModelRequest.GcsTrainingInput.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

Parameters
Name	Description
`field`	`FieldDescriptor`
`index`	`int`
`value`	`Object`

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

setTestDataPath(String value)

public TrainCustomModelRequest.GcsTrainingInput.Builder setTestDataPath(String value)

Cloud Storage test data. Same format as train_data_path. If not provided, a random 80/20 train/test split will be performed on train_data_path.

string test_data_path = 4;

Parameter
Name	Description
`value`	`String` The testDataPath to set.

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`	This builder for chaining.

setTestDataPathBytes(ByteString value)

public TrainCustomModelRequest.GcsTrainingInput.Builder setTestDataPathBytes(ByteString value)

Cloud Storage test data. Same format as train_data_path. If not provided, a random 80/20 train/test split will be performed on train_data_path.

string test_data_path = 4;

Parameter
Name	Description
`value`	`ByteString` The bytes for testDataPath to set.

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`	This builder for chaining.

setTrainDataPath(String value)

public TrainCustomModelRequest.GcsTrainingInput.Builder setTrainDataPath(String value)

Cloud Storage training data path whose format should be gs://<bucket_to_data>/<tsv_file_name>. The file should be in tsv format. Each line should have the doc_id and query_id and score (number).

For search-tuning model, it should have the query-id corpus-id score as tsv file header. The score should be a number in [0, inf+). The larger the number is, the more relevant the pair is. Example:

query-id\tcorpus-id\tscore
query1\tdoc1\t1

string train_data_path = 3;

Parameter
Name	Description
`value`	`String` The trainDataPath to set.

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`	This builder for chaining.

setTrainDataPathBytes(ByteString value)

public TrainCustomModelRequest.GcsTrainingInput.Builder setTrainDataPathBytes(ByteString value)

Cloud Storage training data path whose format should be gs://<bucket_to_data>/<tsv_file_name>. The file should be in tsv format. Each line should have the doc_id and query_id and score (number).

For search-tuning model, it should have the query-id corpus-id score as tsv file header. The score should be a number in [0, inf+). The larger the number is, the more relevant the pair is. Example:

query-id\tcorpus-id\tscore
query1\tdoc1\t1

string train_data_path = 3;

Parameter
Name	Description
`value`	`ByteString` The bytes for trainDataPath to set.

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`	This builder for chaining.

setUnknownFields(UnknownFieldSet unknownFields)

public final TrainCustomModelRequest.GcsTrainingInput.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
`unknownFields`	`UnknownFieldSet`

Returns
Type	Description
`TrainCustomModelRequest.GcsTrainingInput.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.setUnknownFields(UnknownFieldSet unknownFields)