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Translate text with the
The ai.translate function uses Generative AI to translate input text to a new language of your choice—all with a single line of code.
AI functions turbocharge data engineering by putting the power of Fabric's built-in large languages models into your hands. To learn more, visit this overview article.
Important
This feature is in preview, for use in the Fabric 1.3 runtime and higher.
- Review the prerequisites in this overview article, including the library installations that are temporarily required to use AI functions.
- By default, AI functions are currently powered by the gpt-3.5-turbo (0125) model. To learn more about billing and consumption rates, visit this article.
- Although the underlying model can handle several languages, most of the AI functions are optimized for use on English-language texts.
- During the initial rollout of AI functions, users are temporarily limited to 1,000 requests per minute with Fabric's built-in AI endpoint.
Tip
The ai.translate function was tested with 10 languages: Czech, English, Finnish, French, German, Greek, Italian, Polish, Spanish, and Swedish. Your results with other languages may vary.
Use ai.translate with pandas
The ai.translate function extends the pandas Series class. Call the function on a pandas DataFrame text column to translate each input row into a target language of your choosing.
The function returns a pandas Series that contains translations, which you can store in a new DataFrame column.
Syntax
df["translations"] = df["text"].ai.translate("target_language")
Parameters
| Name | Description |
|---|---|
to_lang Required |
A string representing the target language for text translations. |
Returns
A pandas Series that contains translations for each row of input text. If the input text is null, the result is null.
Example
# This code uses AI. Always review output for mistakes.
# Read terms: https://azure.microsoft.com/support/legal/preview-supplemental-terms/
df = pd.DataFrame([
"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."
], columns=["text"])
df["translations"] = df["text"].ai.translate("spanish")
display(df)
Use ai.translate with PySpark
The ai.translate function is also 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 be translated. |
output_col Optional |
A string that contains the name of a new column that stores translations for each input text row. If this parameter isn't set, a default name is generated 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 this parameter isn't set, a default name is generated for the error column. If an input row has no errors, the value in this column is null. |
Returns
A Spark DataFrame with 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.
# Read terms: https://azure.microsoft.com/support/legal/preview-supplemental-terms/
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)
Related content
- Calculate similarity with
ai.similarity. - Categorize text with
ai.classify. - Detect sentiment with
ai.analyze_sentiment. - Extract entities with
ai_extract. - Fix grammar with
ai.fix_grammar. - Summarize text with
ai.summarize. - Answer custom user prompts with
ai.generate_response. - To learn more about the full set of AI functions, visit this overview article.
- Learn how to customize the configuration of AI functions here.
- Did we miss a feature you need? Suggest it on the Fabric Ideas forum.