reference-automl-nlp-cli-text-classification.md

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CLI (v2) Automated ML text classification job YAML schema	Azure Machine Learning	Reference documentation for the CLI (v2) automated ML text classification job YAML schema.	machine-learning	machine-learning	core	reference	cliv2	ssalgado	ssalgadodev	12/22/2022	xiaoxiaoli

CLI (v2) Automated ML text classification job YAML schema

[!INCLUDE cli v2]

[!INCLUDE schema note]

Every Azure Machine Learning entity has a schematized YAML representation. You can create a new entity from a YAML configuration file with a .yml or .yaml extension.

This article provides a reference for some syntax concepts you will encounter while configuring these YAML files for NLP text classification jobs.

The source JSON schema can be found at https://azuremlsdk2.blob.core.windows.net/preview/0.0.1/autoMLNLPTextClassificationJob.schema.json

YAML syntax

Key	Type	Description	Allowed values	Default value
$schema	string	Represents the location/url to load the YAML schema. If the user uses the Azure Machine Learning VS Code extension to author the YAML file, including $schema at the top of the file enables the user to invoke schema and resource completions.
type	const	Required. The type of job.	automl	automl
task	const	Required. The type of AutoML task. Task description of text classification: There are multiple possible classes and each sample can be classified as exactly one class. The task is to predict the correct class for each sample. For example, classifying a movie script as "Comedy" or "Romantic".	text_classification
name	string	Name of the job. Must be unique across all jobs in the workspace. If omitted, Azure Machine Learning will autogenerate a GUID for the name.
display_name	string	Display name of the job in the studio UI. Can be non-unique within the workspace. If omitted, Azure Machine Learning will autogenerate a human-readable adjective-noun identifier for the display name.
experiment_name	string	Experiment name to organize the job under. Each job's run record will be organized under the corresponding experiment in the studio's "Experiments" tab. If omitted, Azure Machine Learning will default it to the name of the working directory where the job was created.
description	string	Description of the job.
tags	object	Dictionary of tags for the job.
compute	string	Name of the compute target to execute the job on. To reference an existing compute in the workspace, we use syntax: azureml:<compute_name>
log_verbosity	number	Different levels of log verbosity.	not_set, debug, info, warning, error, critical	info
primary_metric	string	The metric that AutoML will optimize for model selection.	accuracy, auc_weighted, precision_score_weighted	accuracy
target_column_name	string	Required. The name of the column to target for predictions. It must always be specified. This parameter is applicable to training_data and validation_data.
training_data	object	Required. The data to be used within the job. For multi-class classification, the dataset can contain several text columns and exactly one label column.
validation_data	object	Required. The validation data to be used within the job. It should be consistent with the training data in terms of the set of columns, data type for each column, order of columns from left to right and at least two unique labels. Note: the column names within each dataset should be unique.
limits	object	Dictionary of limit configurations of the job. Parameters in this section: max_concurrent_trials, max_nodes, max_trials, timeout_minutes, trial_timeout_minutes. See limits for detail.
training_parameters	object	Dictionary containing training parameters for the job. See supported hyperparameters for detail. Note: Hyperparameters set in the training_parameters are fixed across all sweeping runs and thus don't need to be included in the search space.
sweep	object	Dictionary containing sweep parameters for the job. It has two keys - sampling_algorithm (required) and early_termination. For more information, see model sweeping and hyperparameter tuning sections.
search_space	object	Dictionary of the hyperparameter search space. The key is the name of the hyperparameter and the value is the parameter expression. All parameters that are fixable via training_parameters are supported here (to be instead swept over). See supported hyperparameters for more detail. There are two types of hyperparameters: - Discrete Hyperparameters: Discrete hyperparameters are specified as a choice among discrete values. choice can be one or more comma-separated values, a range object, or any arbitrary list object. Advanced discrete hyperparameters can also be specified using a distribution - randint, qlognormal, qnormal, qloguniform, quniform. For more information, see this section. - Continuous hyperparameters: Continuous hyperparameters are specified as a distribution over a continuous range of values. Currently supported distributions are - lognormal, normal, loguniform, uniform. For more information, see this section. See parameter expressions for the set of possible expressions to use.
outputs	object	Dictionary of output configurations of the job. The key is a name for the output within the context of the job and the value is the output configuration.
outputs.best_model	object	Dictionary of output configurations for best model. For more information, see Best model output configuration.

Other syntax used in configurations:

Limits

Key	Type	Description	Allowed values	Default value
max_concurrent_trials	integer	Represents the maximum number of trials (children jobs) that would be executed in parallel.		1
max_trials	integer	Represents the maximum number of trials an AutoML nlp job can try to run a training algorithm with different combination of hyperparameters.		1
timeout_minutes	integer	Represents the maximum amount of time in minutes that the submitted AutoML NLP job can take to run . After this, the job will get terminated. The default timeout in AutoML NLP jobs is 7 days.		10080
trial_timeout_minutes	integer	Represents the maximum amount of time in minutes that each trial (child job) in the submitted AutoML job can take run. After this, the child job will get terminated.
max_nodes	integer	The maximum number of nodes from the backing compute cluster to leverage for the job.		1

Supported hyperparameters

The following table describes the hyperparameters that AutoML NLP supports.

Parameter name	Description	Syntax
gradient_accumulation_steps	The number of backward operations whose gradients are to be summed up before performing one step of gradient descent by calling the optimizer’s step function. This is leveraged to use an effective batch size which is gradient_accumulation_steps times larger than the maximum size that fits the GPU.	Must be a positive integer.
learning_rate	Initial learning rate.	Must be a float in the range (0, 1).
learning_rate_scheduler	Type of learning rate scheduler.	Must choose from linear, cosine, cosine_with_restarts, polynomial, constant, constant_with_warmup.
model_name	Name of one of the supported models.	Must choose from bert_base_cased, bert_base_uncased, bert_base_multilingual_cased, bert_base_german_cased, bert_large_cased, bert_large_uncased, distilbert_base_cased, distilbert_base_uncased, roberta_base, roberta_large, distilroberta_base, xlm_roberta_base, xlm_roberta_large, xlnet_base_cased, xlnet_large_cased.
number_of_epochs	Number of training epochs.	Must be a positive integer.
training_batch_size	Training batch size.	Must be a positive integer.
validation_batch_size	Validation batch size.	Must be a positive integer.
warmup_ratio	Ratio of total training steps used for a linear warmup from 0 to learning_rate.	Must be a float in the range [0, 1].
weight_decay	Value of weight decay when optimizer is sgd, adam, or adamw.	Must be a float in the range [0, 1].

Training or validation data

Key	Type	Description	Allowed values	Default value
description	string	The detailed information that describes this input data.
path	string	The path from where data should be loaded. Path can be a file path, folder path or pattern for paths. pattern specifies a search pattern to allow globbing(* and **) of files and folders containing data. Supported URI types are azureml, https, wasbs, abfss, and adl. For more information on how to use the azureml:// URI format, see core yaml syntax. URI of the location of the artifact file. If this URI doesn't have a scheme (for example, http:, azureml: etc.), then it's considered a local reference and the file it points to is uploaded to the default workspace blob-storage as the entity is created.
mode	string	Dataset delivery mechanism.	direct	direct
type	const	In order to generate nlp models, the user needs to bring training data in the form of an MLTable. For more information, see preparing data	mltable	mltable

Best model output configuration

Key	Type	Description	Allowed values	Default value
type	string	Required. Type of best model. AutoML allows only mlflow models.	mlflow_model	mlflow_model
path	string	Required. URI of the location where the model-artifact file(s) are stored. If this URI doesn't have a scheme (for example, http:, azureml: etc.), then it's considered a local reference and the file it points to is uploaded to the default workspace blob-storage as the entity is created.
storage_uri	string	The HTTP URL of the Model. Use this URL with az storage copy -s THIS_URL -d DESTINATION_PATH --recursive to download the data.

Remarks

The az ml job command can be used for managing Azure Machine Learning jobs.

Examples

Examples are available in the examples GitHub repository. Examples relevant to text classification job are linked below.

YAML: AutoML text classification job

:::code language="yaml" source="~/azureml-examples-main/cli/jobs/automl-standalone-jobs/cli-automl-text-classification-newsgroup/cli-automl-text-classification-newsgroup.yml":::

YAML: AutoML text classification pipeline job

:::code language="yaml" source="~/azureml-examples-main/cli/jobs/pipelines/automl/cli-automl-text-classification-newsgroup-pipeline/pipeline.yml":::

Next steps

• Install and use the CLI (v2)