Class TrainCustomModelRequest.GcsTrainingInput.Builder (0.42.0)

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

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

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

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

clearOneof(Descriptors.OneofDescriptor oneof)

public TrainCustomModelRequest.GcsTrainingInput.Builder clearOneof(Descriptors.OneofDescriptor oneof)
Parameter
Name Description
oneof OneofDescriptor
Returns
Type Description
TrainCustomModelRequest.GcsTrainingInput.Builder
Overrides

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

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

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

isInitialized()

public final boolean isInitialized()
Returns
Type Description
boolean
Overrides

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
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

mergeUnknownFields(UnknownFieldSet unknownFields)

public final TrainCustomModelRequest.GcsTrainingInput.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
Parameter
Name Description
unknownFields UnknownFieldSet
Returns
Type Description
TrainCustomModelRequest.GcsTrainingInput.Builder
Overrides

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

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

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