View Source GoogleApi.DiscoveryEngine.V1beta.Model.GoogleCloudDiscoveryengineV1betaTrainCustomModelRequestGcsTrainingInput (google_api_discovery_engine v0.15.0)

Cloud Storage training data input.

Attributes

corpusDataPath (type: String.t, default: nil) - The Cloud Storage corpus data which could be associated in train data. The data path format is gs:///. 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"}
queryDataPath (type: String.t, default: nil) - The gcs query data which could be associated in train data. The data path format is gs:///. A newline delimited jsonl/ndjson file. For search-tuning model, each line should have the _id and text. Example: {"_id": "query1", "text": "example query"}
testDataPath (type: String.t, default: nil) - 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.
trainDataPath (type: String.t, default: nil) - Cloud Storage training data path whose format should be gs:///. 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

Summary

Types

t()

Functions

decode(value, options)

Unwrap a decoded JSON object into its complex fields.

Types

t()

@type t() ::
  %GoogleApi.DiscoveryEngine.V1beta.Model.GoogleCloudDiscoveryengineV1betaTrainCustomModelRequestGcsTrainingInput{
    corpusDataPath: String.t() | nil,
    queryDataPath: String.t() | nil,
    testDataPath: String.t() | nil,
    trainDataPath: String.t() | nil
  }

Functions

decode(value, options)

@spec decode(struct(), keyword()) :: struct()

Unwrap a decoded JSON object into its complex fields.