For Translation: CSV file
translation.csv, with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .TSV file which describes examples that have given ML_USE, using the following row format per line: TEXT_SNIPPET (in source language) \tTEXT_SNIPPET (in target language)- For Tables: Output depends on whether the dataset was imported from GCS or BigQuery. GCS case:
[gcs_destination][google.cloud.automl.v1beta1.OutputConfig.gcs_destination] must be set. Exported are CSV file(s)
tables_1.csv,tables_2.csv,...,\tables_N.csvwith each having as header line the table's column names, and all other lines contain values for the header columns. BigQuery case:[bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination] pointing to a BigQuery project must be set. In the given project a new dataset will be created with name
export_data_<automl-dataset-display-name>_<timestamp-of-export-call>where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In that dataset a new table calledprimary_tablewill be created, and filled with precisely the same data as this obtained on import.
Attributes: destination: Required. The destination of the output. gcs_destination: The Google Cloud Storage location where the output is to be written to. For Image Object Detection, Text Extraction, Video Classification and Tables, in the given directory a new directory will be created with name: export_data-- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export output will be written into that directory. bigquery_destination: The BigQuery location where the output is to be written to.