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The ai.translate function uses generative AI to translate input text into a new language (of your choice), with a single line of code.
Note
- This article covers using ai.translate with PySpark. To use ai.translate with pandas, see this article.
- See other AI functions in this overview article.
- Learn how to customize the configuration of AI functions.
Overview
The ai.translate function is available for Spark DataFrames. You must specify an existing input column name as a parameter, along with a target language.
The function returns a new DataFrame with translations for each input text row, stored in an output column.
Syntax
df.ai.translate(to_lang="spanish", input_col="text", output_col="translations")
Parameters
| Name | Description |
|---|---|
to_lang Required |
A string that represents the target language for text translations. |
input_col Required |
A string that contains the name of an existing column with input text values to translate. |
output_col Optional |
A string that contains the name of a new column that stores translations for each input text row. If you don't set this parameter, a default name generates for the output column. |
error_col Optional |
A string that contains the name of a new column that stores any OpenAI errors that result from processing each input text row. If you don't set this parameter, a default name generates for the error column. If an input row has no errors, the value in this column is null. |
Returns
The function returns a Spark DataFrame that includes a new column that contains translations for the text in the input column row. If the input text is null, the result is null.
Example
# This code uses AI. Always review output for mistakes.
df = spark.createDataFrame([
("Hello! How are you doing today?",),
("Tell me what you'd like to know, and I'll do my best to help.",),
("The only thing we have to fear is fear itself.",),
], ["text"])
translations = df.ai.translate(to_lang="spanish", input_col="text", output_col="translations")
display(translations)
This example code cell provides the following output:
Related content
Detect sentiment with ai.analyze_sentiment.
Categorize text with ai.classify.
Generate vector embeddings with ai.embed.
Extract entities with ai_extract.
Fix grammar with ai.fix_grammar.
Answer custom user prompts with ai.generate_response.
Calculate similarity with ai.similarity.
Summarize text with ai.summarize.
Learn more about the full set of AI functions.
Customize the configuration of AI functions.
Did we miss a feature you need? Suggest it on the Fabric Ideas forum.