OutputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)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) \t TEXT_SNIPPET (in target language)- For Tables: Output depends on whether the dataset was imported from GCS or BigQuery. GCS case:
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 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.
Attributes
| Name | Description |
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. |