Detect Languages


Support for Machine Learning Studio (classic) will end on 31 August 2024. We recommend you transition to Azure Machine Learning by that date.

Beginning 1 December 2021, you will not be able to create new Machine Learning Studio (classic) resources. Through 31 August 2024, you can continue to use the existing Machine Learning Studio (classic) resources.

ML Studio (classic) documentation is being retired and may not be updated in the future.

Detects the language of each line in the input file

Category: Text Analytics


Applies to: Machine Learning Studio (classic) only

Similar drag-and-drop modules are available in Azure Machine Learning designer.

Module Overview

This article describes how to use the Detect Languages module in Machine Learning Studio (classic) to analyze text input and identify the language associated with each record in the input.

The language detection algorithm can identify many different languages. Just specify the string column to analyze, and the total number of languages to detect. The algorithm will analyze each row of text, and assign a probability score for each language. The language in the first result column is the language that got the highest score.

How to configure Detect Languages

  1. Add the dataset containing the text you want to analyze to an experiment in Machine Learning Studio (classic). The column with the text to analyze must be the string data type.

    The datset need not contain a label column; the language detection algorithm works purely on linguistic features of the supported languages.

    If you are importing new data, make sure that your data is saved in the UTF-8 format. Other Unicode formats are not supported.

  2. Add the Detect Languages module to your experiment, and connect the dataset with the text for language detection.

  3. For Text column, choose the column you want to analyze.

  4. For Upper bound on number of languages to detect, indicate the maximum number of languages to detect.

    Setting an upper bound on the number of languages can improve performance.

  5. Run the experiment.


The Detect Languages module outputs a language identifier and score for each row.

For example, the following table contains a sample analysis on test data.

  • The first two columns col1 and language label are columns passed through from the input dataset. In this example, because the input dataset was designed for testing the module, the expected language was already known, and is provided in the label column.

  • The remaining columns are generated by the Detect Languages module. If there are equi-probable language matches, several languages might be listed, with a score for each. In this case, the module predicts just one language for each row, together with the probability score for that language.

    If the module fails to detect any language with a sufficiently high score, a result of (Unknown) with a score of 0 is output. However, the languages supported by the module can change over time as the API is updated.

Col1 Language label Col1 Language Col1 Iso6391 Language Col1 Iso6391 Language Score
It was a wonderful hotel with a friendly staff and good service English English en 100
Es war ein wunderbares Hotel mit freundlichem Personal und guter service German German de 100
C’est un magnifique hôtel avec un personnel sympathique et un service de qualité French French fr 100
Det var et dejligt hotel med et venligt personale og god service Danish Danish nl 100
Va ser un magnífic hotel amb un personal amable i bon servei Catalan Catalan ca 92.30769348
とても素敵なホテルで、スタッフは親切で、サービスもよかった Japanese (Unknown) 0
qu mebpa'mey naQ friendly QaQ chavmoH je Klingon French fr 77.5


For examples of how the Detect Languages module is used in an experiment, see the Azure AI Gallery:

  • Filter Movie Titles by Language: Detects the language used in movie names, and then uses the language identifier to split the dataset into English vs non-English movies.

Technical notes

For a general idea of the languages that potentially can be detected, refer to Bing Translator.

Many more languages can be detected than Machine Learning currently supports for advanced text analytics. We recommend that you use the results of Detect Languages to filter the results that you send to other modules that require language-specific processing.

The underlying linguistic services are also used by the Text Analytics service in Azure Cognitive Services.

Expected inputs

Name Type Description
Dataset Data Table The input

Module parameters

Name Type Range Optional Default Description
Upper bound on number of languages to detect Integer [1;184] Required 1 Upper bound on number of languages to detect.
Text column ColumnSelection Required Name or one-based index of text column.


Name Type Description
Results dataset Data Table The result


Exception Description
Error 0003 Exception occurs if one or more of inputs are null or empty.
Error 0010 Exception occurs if input datasets have column names that should match but do not.
Error 0016 Exception occurs if input datasets passed to the module should have compatible column types but do not.
Error 0008 Exception occurs if parameter is not in range.

For a list of errors specific to Studio (classic) modules, see Machine Learning Error codes.

For a list of API exceptions, see Machine Learning REST API Error Codes.

See also

Text Analytics
A-Z Module List