* 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.csv with 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 called primary_table will be created, and filled
with precisely the same data as this obtained on import.
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.