public static final class TrainCustomModelRequest.GcsTrainingInput extends GeneratedMessageV3 implements TrainCustomModelRequest.GcsTrainingInputOrBuilder
Cloud Storage training data input.
Protobuf type google.cloud.discoveryengine.v1.TrainCustomModelRequest.GcsTrainingInput
Inherited Members
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT)
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT,int)
com.google.protobuf.GeneratedMessageV3.<T>emptyList(java.lang.Class<T>)
com.google.protobuf.GeneratedMessageV3.internalGetMapFieldReflection(int)
Static Fields
public static final int CORPUS_DATA_PATH_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int QUERY_DATA_PATH_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int TEST_DATA_PATH_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int TRAIN_DATA_PATH_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
Static Methods
public static TrainCustomModelRequest.GcsTrainingInput getDefaultInstance()
public static final Descriptors.Descriptor getDescriptor()
public static TrainCustomModelRequest.GcsTrainingInput.Builder newBuilder()
public static TrainCustomModelRequest.GcsTrainingInput.Builder newBuilder(TrainCustomModelRequest.GcsTrainingInput prototype)
public static TrainCustomModelRequest.GcsTrainingInput parseDelimitedFrom(InputStream input)
public static TrainCustomModelRequest.GcsTrainingInput parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
public static TrainCustomModelRequest.GcsTrainingInput parseFrom(byte[] data)
| Parameter |
| Name |
Description |
data |
byte[]
|
public static TrainCustomModelRequest.GcsTrainingInput parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
public static TrainCustomModelRequest.GcsTrainingInput parseFrom(ByteString data)
public static TrainCustomModelRequest.GcsTrainingInput parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
public static TrainCustomModelRequest.GcsTrainingInput parseFrom(CodedInputStream input)
public static TrainCustomModelRequest.GcsTrainingInput parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public static TrainCustomModelRequest.GcsTrainingInput parseFrom(InputStream input)
public static TrainCustomModelRequest.GcsTrainingInput parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
public static TrainCustomModelRequest.GcsTrainingInput parseFrom(ByteBuffer data)
public static TrainCustomModelRequest.GcsTrainingInput parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
public static Parser<TrainCustomModelRequest.GcsTrainingInput> parser()
Methods
public boolean equals(Object obj)
| Parameter |
| Name |
Description |
obj |
Object
|
Overrides
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.
|
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.
|
public TrainCustomModelRequest.GcsTrainingInput getDefaultInstanceForType()
public Parser<TrainCustomModelRequest.GcsTrainingInput> getParserForType()
Overrides
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.
|
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.
|
public int getSerializedSize()
| Returns |
| Type |
Description |
int |
|
Overrides
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.
|
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.
|
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\tscorequery1\tdoc1\t1
string train_data_path = 3;
| Returns |
| Type |
Description |
String |
The trainDataPath.
|
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\tscorequery1\tdoc1\t1
string train_data_path = 3;
| Returns |
| Type |
Description |
ByteString |
The bytes for trainDataPath.
|
| Returns |
| Type |
Description |
int |
|
Overrides
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Overrides
public final boolean isInitialized()
Overrides
public TrainCustomModelRequest.GcsTrainingInput.Builder newBuilderForType()
protected TrainCustomModelRequest.GcsTrainingInput.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Overrides
protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
| Returns |
| Type |
Description |
Object |
|
Overrides
public TrainCustomModelRequest.GcsTrainingInput.Builder toBuilder()
public void writeTo(CodedOutputStream output)
Overrides
