automl Package

  • Reference

Contains automated machine learning classes for Azure Machine Learning SDKv2.

Main areas include managing AutoML tasks.

Classes

ClassificationJob

Configuration for AutoML Classification Job.

Initialize a new AutoML Classification task.
ColumnTransformer

Column transformer settings.
ForecastingJob

Configuration for AutoML Forecasting Task.

Initialize a new AutoML Forecasting task.
ForecastingSettings

Forecasting settings for an AutoML Job.
ImageClassificationJob

Configuration for AutoML multi-class Image Classification job.

Initialize a new AutoML multi-class Image Classification job.
ImageClassificationMultilabelJob

Configuration for AutoML multi-label Image Classification job.

Initialize a new AutoML multi-label Image Classification job.
ImageClassificationSearchSpace

Search space for AutoML Image Classification and Image Classification Multilabel tasks.
ImageInstanceSegmentationJob

Configuration for AutoML Image Instance Segmentation job.

Initialize a new AutoML Image Instance Segmentation job.
ImageLimitSettings

Limit settings for AutoML Image Verticals.

ImageLimitSettings is a class that contains the following parameters: max_concurrent_trials, max_trials, and timeout_minutes.

This is an optional configuration method to configure limits parameters such as timeouts etc.

Note

The number of concurrent runs is gated on the resources available in the specified compute target.

Ensure that the compute target has the available resources for the desired concurrency.

Tip

It's a good practice to match max_concurrent_trials count with the number of nodes in the cluster.

For example, if you have a cluster with 4 nodes, set max_concurrent_trials to 4.

Example usage

Configuration of ImageLimitSettings


   from azure.ai.ml import automl

   # Create the AutoML job with the related factory-function.
   image_instance_segmentation_job = automl.image_instance_segmentation(
       compute=compute_name,
       experiment_name=exp_name,
       training_data=my_training_data_input,
       validation_data=my_validation_data_input,
       target_column_name="label",
       primary_metric="MeanAveragePrecision",
       tags={"my_custom_tag": "custom value"},
   )
   # Set the limits for the AutoML job.
   image_instance_segmentation_job.set_limits(
       max_trials=10,
       max_concurrent_trials=2,
   )
   # Submit the AutoML job.
   image_instance_segmentation_job.submit()

Initialize an ImageLimitSettings object.

Constructor for ImageLimitSettings for AutoML Image Verticals.
ImageModelSettingsClassification

Model settings for AutoML Image Classification tasks.
ImageModelSettingsObjectDetection

Model settings for AutoML Image Object Detection Task.
ImageObjectDetectionJob

Configuration for AutoML Image Object Detection job.

Initialize a new AutoML Image Object Detection job.
ImageObjectDetectionSearchSpace

Search space for AutoML Image Object Detection and Image Instance Segmentation tasks.
ImageSweepSettings

Sweep settings for all AutoML Image Verticals.
NlpFeaturizationSettings

Featurization settings for all AutoML NLP Verticals.
NlpFixedParameters

Object to house fixed parameters for NLP jobs.
NlpLimitSettings

Limit settings for all AutoML NLP Verticals.
NlpSearchSpace

Search space for AutoML NLP tasks.
NlpSweepSettings

Sweep settings for all AutoML NLP tasks.
RegressionJob

Configuration for AutoML Regression Job.

Initialize a new AutoML Regression task.
SearchSpace

SearchSpace class for AutoML verticals.
StackEnsembleSettings

Advance setting to customize StackEnsemble run.
TabularFeaturizationSettings

Featurization settings for an AutoML Job.
TabularLimitSettings

Limit settings for a AutoML Table Verticals.
TextClassificationJob

Configuration for AutoML Text Classification Job.

Initializes a new AutoML Text Classification task.
TextClassificationMultilabelJob

Configuration for AutoML Text Classification Multilabel Job.

Initializes a new AutoML Text Classification Multilabel task.
TextNerJob

Configuration for AutoML Text NER Job.

Initializes a new AutoML Text NER task.
TrainingSettings

TrainingSettings class for Azure Machine Learning.

TrainingSettings class for Azure Machine Learning.

Enums

BlockedTransformers

Enum for all classification models supported by AutoML.
ClassificationModels

Enum for all classification models supported by AutoML.
ClassificationMultilabelPrimaryMetrics

Primary metrics for classification multilabel tasks.
ClassificationPrimaryMetrics

Primary metrics for classification tasks.
FeaturizationMode

Featurization mode - determines data featurization mode.
ForecastHorizonMode

Enum to determine forecast horizon selection mode.
ForecastingModels

Enum for all forecasting models supported by AutoML.
ForecastingPrimaryMetrics

Primary metrics for Forecasting task.
InstanceSegmentationPrimaryMetrics

Primary metrics for InstanceSegmentation tasks.
LearningRateScheduler

Learning rate scheduler enum.
LogTrainingMetrics
LogValidationLoss
NCrossValidationsMode

Determines how N-Cross validations value is determined.
ObjectDetectionPrimaryMetrics

Primary metrics for Image ObjectDetection task.
RegressionModels

Enum for all Regression models supported by AutoML.
RegressionPrimaryMetrics

Primary metrics for Regression task.
SamplingAlgorithmType
ShortSeriesHandlingConfiguration

The parameter defining how if AutoML should handle short time series.
StochasticOptimizer

Stochastic optimizer for image models.
TargetAggregationFunction

Target aggregate function.
TargetLagsMode

Target lags selection modes.
TargetRollingWindowSizeMode

Target rolling windows size mode.
UseStl

Configure STL Decomposition of the time-series target column.
ValidationMetricType

Metric computation method to use for validation metrics in image tasks.

Functions

classification

Function to create a ClassificationJob.

A classification job is used to train a model that best predict the class of a data sample. Various models are trained using the training data. The model with the best performance on the validation data based on the primary metric is selected as the final model.

classification(*, training_data: Input, target_column_name: str, primary_metric: str | None = None, enable_model_explainability: bool | None = None, weight_column_name: str | None = None, validation_data: Input | None = None, validation_data_size: float | None = None, n_cross_validations: str | int | None = None, cv_split_column_names: List[str] | None = None, test_data: Input | None = None, test_data_size: float | None = None, **kwargs) -> ClassificationJob

Parameters

training_data
Input

The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column).

target_column_name
str

The name of the label column. This parameter is applicable to training_data, validation_data and test_data parameters

primary_metric

The metric that Automated Machine Learning will optimize for model selection. Automated Machine Learning collects more metrics than it can optimize. For more information on how metrics are calculated, see https://docs.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric.

Acceptable values: accuracy, AUC_weighted, norm_macro_recall, average_precision_score_weighted, and precision_score_weighted Defaults to accuracy

enable_model_explainability
bool

Whether to enable explaining the best AutoML model at the end of all AutoML training iterations. The default is None. For more information, see Interpretability: model explanations in automated machine learning.

weight_column_name
str

The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers.

This parameter is applicable to training_data and validation_data parameters

validation_data
Input

The validation data to be used within the experiment. It should contain both training features and label column (optionally a sample weights column).

Defaults to None

validation_data_size
float

What fraction of the data to hold out for validation when user validation data is not specified. This should be between 0.0 and 1.0 non-inclusive.

Specify validation_data to provide validation data, otherwise set n_cross_validations or validation_data_size to extract validation data out of the specified training data. For custom cross validation fold, use cv_split_column_names.

For more information, see Configure data splits and cross-validation in automated machine learning.

Defaults to None

n_cross_validations
Union[str, int]

How many cross validations to perform when user validation data is not specified.

Specify validation_data to provide validation data, otherwise set n_cross_validations or validation_data_size to extract validation data out of the specified training data. For custom cross validation fold, use cv_split_column_names.

For more information, see Configure data splits and cross-validation in automated machine learning.

Defaults to None

cv_split_column_names
List[str]

List of names of the columns that contain custom cross validation split. Each of the CV split columns represents one CV split where each row are either marked 1 for training or 0 for validation.

Defaults to None

test_data
Input

The Model Test feature using test datasets or test data splits is a feature in Preview state and might change at any time. The test data to be used for a test run that will automatically be started after model training is complete. The test run will get predictions using the best model and will compute metrics given these predictions.

If this parameter or the test_data_size parameter are not specified then no test run will be executed automatically after model training is completed. Test data should contain both features and label column. If test_data is specified then the target_column_name parameter must be specified.

Defaults to None

test_data_size
float

The Model Test feature using test datasets or test data splits is a feature in Preview state and might change at any time. What fraction of the training data to hold out for test data for a test run that will automatically be started after model training is complete. The test run will get predictions using the best model and will compute metrics given these predictions.

This should be between 0.0 and 1.0 non-inclusive. If test_data_size is specified at the same time as validation_data_size, then the test data is split from training_data before the validation data is split. For example, if validation_data_size=0.1, test_data_size=0.1 and the original training data has 1000 rows, then the test data will have 100 rows, the validation data will contain 90 rows and the training data will have 810 rows.

For regression based tasks, random sampling is used. For classification tasks, stratified sampling is used. Forecasting does not currently support specifying a test dataset using a train/test split.

If this parameter or the test_data parameter are not specified then no test run will be executed automatically after model training is completed.

Defaults to None

Returns

A job object that can be submitted to an Azure ML compute for execution.

Return type

ClassificationJob

forecasting

Function to create a Forecasting job.

A forecasting task is used to predict target values for a future time period based on the historical data. Various models are trained using the training data. The model with the best performance on the validation data based on the primary metric is selected as the final model.

forecasting(*, training_data: Input, target_column_name: str, primary_metric: str | None = None, enable_model_explainability: bool | None = None, weight_column_name: str | None = None, validation_data: Input | None = None, validation_data_size: float | None = None, n_cross_validations: str | int | None = None, cv_split_column_names: List[str] | None = None, test_data: Input | None = None, test_data_size: float | None = None, forecasting_settings: ForecastingSettings | None = None, **kwargs) -> ForecastingJob

Parameters

training_data
Input

The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column).

target_column_name
str

The name of the label column. This parameter is applicable to training_data, validation_data and test_data parameters

primary_metric

The metric that Automated Machine Learning will optimize for model selection. Automated Machine Learning collects more metrics than it can optimize. For more information on how metrics are calculated, see https://docs.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric.

Acceptable values: r2_score, normalized_mean_absolute_error, normalized_root_mean_squared_error Defaults to normalized_root_mean_squared_error

enable_model_explainability
bool

Whether to enable explaining the best AutoML model at the end of all AutoML training iterations. The default is None. For more information, see Interpretability: model explanations in automated machine learning.

weight_column_name
str

The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers.

This parameter is applicable to training_data and validation_data parameters

validation_data
Input

The validation data to be used within the experiment. It should contain both training features and label column (optionally a sample weights column).

Defaults to None

validation_data_size
float

What fraction of the data to hold out for validation when user validation data is not specified. This should be between 0.0 and 1.0 non-inclusive.

Specify validation_data to provide validation data, otherwise set n_cross_validations or validation_data_size to extract validation data out of the specified training data. For custom cross validation fold, use cv_split_column_names.

For more information, see Configure data splits and cross-validation in automated machine learning.

Defaults to None

n_cross_validations
Union[str, int]

How many cross validations to perform when user validation data is not specified.

Specify validation_data to provide validation data, otherwise set n_cross_validations or validation_data_size to extract validation data out of the specified training data. For custom cross validation fold, use cv_split_column_names.

For more information, see Configure data splits and cross-validation in automated machine learning.

Defaults to None

cv_split_column_names
List[str]

List of names of the columns that contain custom cross validation split. Each of the CV split columns represents one CV split where each row are either marked 1 for training or 0 for validation.

Defaults to None

test_data
Input

The Model Test feature using test datasets or test data splits is a feature in Preview state and might change at any time. The test data to be used for a test run that will automatically be started after model training is complete. The test run will get predictions using the best model and will compute metrics given these predictions.

If this parameter or the test_data_size parameter are not specified then no test run will be executed automatically after model training is completed. Test data should contain both features and label column. If test_data is specified then the target_column_name parameter must be specified.

Defaults to None

test_data_size
float

The Model Test feature using test datasets or test data splits is a feature in Preview state and might change at any time. What fraction of the training data to hold out for test data for a test run that will automatically be started after model training is complete. The test run will get predictions using the best model and will compute metrics given these predictions.

This should be between 0.0 and 1.0 non-inclusive. If test_data_size is specified at the same time as validation_data_size, then the test data is split from training_data before the validation data is split. For example, if validation_data_size=0.1, test_data_size=0.1 and the original training data has 1000 rows, then the test data will have 100 rows, the validation data will contain 90 rows and the training data will have 810 rows.

For regression based tasks, random sampling is used. For classification tasks, stratified sampling is used. Forecasting does not currently support specifying a test dataset using a train/test split.

If this parameter or the test_data parameter are not specified then no test run will be executed automatically after model training is completed.

Defaults to None

forecasting_settings
ForecastingSettings

The settings for the forecasting task

Returns

A job object that can be submitted to an Azure ML compute for execution.

Return type

ForecastingJob

image_classification

Creates an object for AutoML Image multi-class Classification job.

image_classification(*, training_data: Input, target_column_name: str, primary_metric: str | ClassificationPrimaryMetrics | None = None, validation_data: Input | None = None, validation_data_size: float | None = None, **kwargs) -> ImageClassificationJob

Parameters

training_data
Input

The training data to be used within the experiment.

target_column_name
str

The name of the label column. This parameter is applicable to training_data and validation_data parameters.

primary_metric

The metric that Automated Machine Learning will optimize for model selection. Automated Machine Learning collects more metrics than it can optimize. For more information on how metrics are calculated, see https://docs.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric.

Acceptable values: accuracy, AUC_weighted, norm_macro_recall, average_precision_score_weighted, and precision_score_weighted Defaults to accuracy.

validation_data
Input

The validation data to be used within the experiment.

validation_data_size
float

What fraction of the data to hold out for validation when user validation data is not specified. This should be between 0.0 and 1.0 non-inclusive.

Specify validation_data to provide validation data, otherwise set validation_data_size to extract validation data out of the specified training data.

Defaults to .2

kwargs
dict

A dictionary of additional configuration parameters.

Returns

Image classification job object that can be submitted to an Azure ML compute for execution.

Return type

ImageClassificationJob

image_classification_multilabel

Creates an object for AutoML Image multi-label Classification job.

image_classification_multilabel(*, training_data: Input, target_column_name: str, primary_metric: str | ClassificationMultilabelPrimaryMetrics | None = None, validation_data: Input | None = None, validation_data_size: float | None = None, **kwargs) -> ImageClassificationMultilabelJob

Parameters

training_data
Input

The training data to be used within the experiment.

target_column_name
str

The name of the label column. This parameter is applicable to training_data and validation_data parameters.

primary_metric

The metric that Automated Machine Learning will optimize for model selection. Automated Machine Learning collects more metrics than it can optimize. For more information on how metrics are calculated, see https://docs.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric.

Acceptable values: accuracy, AUC_weighted, norm_macro_recall, average_precision_score_weighted, precision_score_weighted, and Iou Defaults to Iou.

validation_data
Input

The validation data to be used within the experiment.

validation_data_size
float

What fraction of the data to hold out for validation when user validation data is not specified. This should be between 0.0 and 1.0 non-inclusive.

Specify validation_data to provide validation data, otherwise set validation_data_size to extract validation data out of the specified training data.

Defaults to .2

kwargs
dict

A dictionary of additional configuration parameters.

Returns

Image multi-label classification job object that can be submitted to an Azure ML compute for execution.

Return type

ImageClassificationMultilabelJob

image_instance_segmentation

Creates an object for AutoML Image Instance Segmentation job.

image_instance_segmentation(*, training_data: Input, target_column_name: str, primary_metric: str | InstanceSegmentationPrimaryMetrics | None = None, validation_data: Input | None = None, validation_data_size: float | None = None, **kwargs) -> ImageInstanceSegmentationJob

Parameters

training_data
Input

The training data to be used within the experiment.

target_column_name
str

The name of the label column. This parameter is applicable to training_data and validation_data parameters.

primary_metric

The metric that Automated Machine Learning will optimize for model selection. Automated Machine Learning collects more metrics than it can optimize. For more information on how metrics are calculated, see https://docs.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric.

Acceptable values: MeanAveragePrecision Defaults to MeanAveragePrecision.

validation_data
Input

The validation data to be used within the experiment.

validation_data_size
float

What fraction of the data to hold out for validation when user validation data is not specified. This should be between 0.0 and 1.0 non-inclusive.

Specify validation_data to provide validation data, otherwise set validation_data_size to extract validation data out of the specified training data.

Defaults to .2

kwargs
dict

A dictionary of additional configuration parameters.

Returns

Image instance segmentation job

Return type

ImageInstanceSegmentationJob

image_object_detection

Creates an object for AutoML Image Object Detection job.

image_object_detection(*, training_data: Input, target_column_name: str, primary_metric: str | ObjectDetectionPrimaryMetrics | None = None, validation_data: Input | None = None, validation_data_size: float | None = None, **kwargs) -> ImageObjectDetectionJob

Parameters

training_data
Input

The training data to be used within the experiment.

target_column_name
str

The name of the label column. This parameter is applicable to training_data and validation_data parameters.

primary_metric

The metric that Automated Machine Learning will optimize for model selection. Automated Machine Learning collects more metrics than it can optimize. For more information on how metrics are calculated, see https://docs.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric.

Acceptable values: MeanAveragePrecision Defaults to MeanAveragePrecision.

validation_data
Input

The validation data to be used within the experiment.

validation_data_size
float

What fraction of the data to hold out for validation when user validation data is not specified. This should be between 0.0 and 1.0 non-inclusive.

Specify validation_data to provide validation data, otherwise set validation_data_size to extract validation data out of the specified training data.

Defaults to .2

kwargs
dict

A dictionary of additional configuration parameters.

Returns

Image object detection job object that can be submitted to an Azure ML compute for execution.

Return type

ImageObjectDetectionJob

regression

Function to create a Regression Job.

A regression job is used to train a model to predict continuous values of a target variable from a dataset. Various models are trained using the training data. The model with the best performance on the validation data based on the primary metric is selected as the final model.

regression(*, training_data: Input, target_column_name: str, primary_metric: str | None = None, enable_model_explainability: bool | None = None, weight_column_name: str | None = None, validation_data: Input | None = None, validation_data_size: float | None = None, n_cross_validations: str | int | None = None, cv_split_column_names: List[str] | None = None, test_data: Input | None = None, test_data_size: float | None = None, **kwargs) -> RegressionJob

Parameters

training_data
Input

The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column).

target_column_name
str

The name of the label column. This parameter is applicable to training_data, validation_data and test_data parameters

primary_metric

The metric that Automated Machine Learning will optimize for model selection. Automated Machine Learning collects more metrics than it can optimize. For more information on how metrics are calculated, see https://docs.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric.

Acceptable values: spearman_correlation, r2_score, normalized_mean_absolute_error, normalized_root_mean_squared_error. Defaults to normalized_root_mean_squared_error

enable_model_explainability
bool

Whether to enable explaining the best AutoML model at the end of all AutoML training iterations. The default is None. For more information, see Interpretability: model explanations in automated machine learning.

weight_column_name
str

The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers.

This parameter is applicable to training_data and validation_data parameters

validation_data
Input

The validation data to be used within the experiment. It should contain both training features and label column (optionally a sample weights column).

Defaults to None

validation_data_size
float

What fraction of the data to hold out for validation when user validation data is not specified. This should be between 0.0 and 1.0 non-inclusive.

Specify validation_data to provide validation data, otherwise set n_cross_validations or validation_data_size to extract validation data out of the specified training data. For custom cross validation fold, use cv_split_column_names.

For more information, see Configure data splits and cross-validation in automated machine learning.

Defaults to None

n_cross_validations
Union[str, int]

How many cross validations to perform when user validation data is not specified.

Specify validation_data to provide validation data, otherwise set n_cross_validations or validation_data_size to extract validation data out of the specified training data. For custom cross validation fold, use cv_split_column_names.

For more information, see Configure data splits and cross-validation in automated machine learning.

Defaults to None

cv_split_column_names
List[str]

List of names of the columns that contain custom cross validation split. Each of the CV split columns represents one CV split where each row are either marked 1 for training or 0 for validation.

Defaults to None

test_data
Input

The Model Test feature using test datasets or test data splits is a feature in Preview state and might change at any time. The test data to be used for a test run that will automatically be started after model training is complete. The test run will get predictions using the best model and will compute metrics given these predictions.

If this parameter or the test_data_size parameter are not specified then no test run will be executed automatically after model training is completed. Test data should contain both features and label column. If test_data is specified then the target_column_name parameter must be specified.

Defaults to None

test_data_size
float

The Model Test feature using test datasets or test data splits is a feature in Preview state and might change at any time. What fraction of the training data to hold out for test data for a test run that will automatically be started after model training is complete. The test run will get predictions using the best model and will compute metrics given these predictions.

This should be between 0.0 and 1.0 non-inclusive. If test_data_size is specified at the same time as validation_data_size, then the test data is split from training_data before the validation data is split. For example, if validation_data_size=0.1, test_data_size=0.1 and the original training data has 1000 rows, then the test data will have 100 rows, the validation data will contain 90 rows and the training data will have 810 rows.

For regression based tasks, random sampling is used. For classification tasks, stratified sampling is used. Forecasting does not currently support specifying a test dataset using a train/test split.

If this parameter or the test_data parameter are not specified then no test run will be executed automatically after model training is completed.

Defaults to None

Returns

A job object that can be submitted to an Azure ML compute for execution.

Return type

RegressionJob

text_classification

Function to create a TextClassificationJob.

A text classification job is used to train a model that can predict the class/category of a text data. Input training data should include a target column that classifies the text into exactly one class.

text_classification(*, training_data: Input, target_column_name: str, validation_data: Input, primary_metric: str | None = None, log_verbosity: str | None = None, **kwargs) -> TextClassificationJob

Parameters

training_data
Input

The training data to be used within the experiment. It should contain both training features and a target column.

target_column_name
str

Name of the target column.

validation_data
Input

The validation data to be used within the experiment. It should contain both training features and a target column.

primary_metric
Union[str, ClassificationPrimaryMetrics]

Primary metric for the task. Acceptable values: accuracy, AUC_weighted, precision_score_weighted

log_verbosity
str

Log verbosity level.

kwargs
dict

A dictionary of additional configuration parameters.

Returns

The TextClassificationJob object.

Return type

TextClassificationJob

text_classification_multilabel

Function to create a TextClassificationMultilabelJob.

A text classification multilabel job is used to train a model that can predict the classes/categories of a text data. Input training data should include a target column that classifies the text into class(es). For more information on format of multilabel data, refer to: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-nlp-models#multi-label

text_classification_multilabel(*, training_data: Input, target_column_name: str, validation_data: Input, primary_metric: str | None = None, log_verbosity: str | None = None, **kwargs) -> TextClassificationMultilabelJob

Parameters

training_data
Input

The training data to be used within the experiment. It should contain both training features and a target column.

target_column_name
str

Name of the target column.

validation_data
Input

The validation data to be used within the experiment. It should contain both training features and a target column.

primary_metric
str

Primary metric for the task. Acceptable values: accuracy

log_verbosity
str

Log verbosity level.

kwargs
dict

A dictionary of additional configuration parameters.

Returns

The TextClassificationMultilabelJob object.

Return type

TextClassificationMultilabelJob

text_ner

Function to create a TextNerJob.

A text named entity recognition job is used to train a model that can predict the named entities in the text. Input training data should be a text file in CoNLL format. For more information on format of text NER data, refer to: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-nlp-models#named-entity-recognition-ner

text_ner(*, training_data: Input, validation_data: Input, primary_metric: str | None = None, log_verbosity: str | None = None, **kwargs) -> TextNerJob

Parameters

training_data
Input

The training data to be used within the experiment. It should contain both training features and a target column.

validation_data
Input

The validation data to be used within the experiment. It should contain both training features and a target column.

primary_metric
str

Primary metric for the task. Acceptable values: accuracy

log_verbosity
str

Log verbosity level.

kwargs
dict

A dictionary of additional configuration parameters.

Returns

The TextNerJob object.

Return type

TextNerJob