Use automated machine learning for natural language processing (NLP)

Automated machine learning (AutoML) has revolutionized the field of natural language processing (NLP) by simplifying and accelerating the development of NLP models. In this article, we will explore how to use AutoML for NLP tasks as part of designing and implementing a data science solution on Azure.

Azure services for NLP

Azure provides several services and tools for NLP tasks, such as Text Analytics, Language Understanding (LUIS), and Cognitive Services. However, when dealing with complex NLP problems or specific use cases, using AutoML can be more efficient and effective.

Creating an Azure Machine Learning workspace

To begin, let’s create an Azure Machine Learning workspace and import the necessary packages:

!pip install azureml-sdk[notebooks]
from azureml.core import Workspace, Experiment
from azureml.train.automl import AutoMLConfig

Defining the AutoML configuration

Next, we need to define the configuration for our AutoML experiment:

workspace = Workspace.from_config()
experiment = Experiment(workspace, 'nlp_experiment')

automl_config = AutoMLConfig(task='text_classification',
primary_metric='accuracy',
training_data=training_data,
label_column_name=label_column,
n_cross_validations=5,
max_concurrent_iterations=4,
iterations=10)

In the code snippet above, we specify the task as ‘text_classification’ since we are working on an NLP classification problem. We also define the primary metric to evaluate the models, which in this case is ‘accuracy’. Additionally, we provide the training data, label column, number of cross validations, and maximum concurrent iterations.

Running the AutoML experiment

Now, we can run the AutoML experiment:

run = experiment.submit(automl_config, show_output=True)

The experiment will utilize various algorithms, feature engineering techniques, and hyperparameters to find the best model for our NLP task. During the process, the AutoML will log the progress and display the intermediate results.

Accessing the best model

Once the experiment is completed, we can access the best model and explore its performance:

best_run, fitted_model = run.get_output()

With the best model in hand, we can now evaluate it on the test dataset and make predictions:

test_data = dataset[split_index:]
test_predictions = fitted_model.predict(test_data)

Optimizing NLP models with AutoML

AutoML not only simplifies the model development process but also allows us to optimize the model’s performance by experimenting with different configurations and algorithms. We can easily compare multiple models generated by AutoML using the performance metrics acquired during the experiment.

Conclusion

Using automated machine learning for natural language processing tasks can significantly speed up the development of NLP models. Azure provides a comprehensive set of tools and services, including AutoML, to facilitate the process. By leveraging AutoML, data scientists can efficiently design and implement NLP solutions on Azure, enabling them to focus on the creative aspects of their NLP projects while benefiting from the power of automated model generation and optimization.

Give it a try and unlock the potential of automated machine learning for your NLP tasks on Azure!