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)
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 called primary_table will 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.