Discovery Engine v1beta API - Class TrainCustomModelRequest.Types.GcsTrainingInput (1.0.0-beta17)

Reference documentation and code samples for the Discovery Engine v1beta API class TrainCustomModelRequest.Types.GcsTrainingInput.

Cloud Storage training data input.

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"}

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"}

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.

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