MonkeyLearn is a No-code text analysis tool. MindsDB allows you to use pre-built & custom MonkeyLearn models to use its features like classifying text according to user needs and fields of interest like business, reviews, comments, and customer feedback.

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How to bring MonkeyLearn Models to MindsDB

Before creating a model, you will need to create the ML_ENGINE for MonkeyLearn using the CREATE ML_ENGINE syntax

CREATE ML_ENGINE monkeylearn_engine
FROM monkeylearn
USING
  monkeylearn_api_key = 'monkeylearn_api_key';

Once the ML_ENGINE is created, we use the CREATE MODEL statement to bring MonkeyLearn models to MindsDB.

For this example, you will make use of MonkeyLearn’s pre-made model E-commerce Support Ticket Classifier.

CREATE MODEL mindsdb.ecommerce_ticket_classifier
PREDICT tag
USING
engine = 'monkeylearn_engine',
monkeylearn_api_key = 'api_key',
model_id = 'model_id',
input_column = 'text';

On execution, you get:

Where:

ExpressionDescription
ecommerce_ticket_classifierThe model name provided to the model created in MindsDB.
tagThe column that will provide the predicted result.
engineThe ML framework engine used, which is MonkeyLearn.
monkeylearn_api_keyThe API Key of the model provided by MonkeyLearn.
model_idThe respective model’s ID you want to make use of.
input_columnSpecifies the input column fed to the model

You can use the DESCRIBE syntax to verify the model’s status.

DESCRIBE ecommerce_ticket_classifier;

On execution, you get:

Use the SELECT statement to make a prediction on the model.

SELECT * FROM ecommerce_ticket_classifier
WHERE text = 'Where is my order? The delivery status shows shipped. When I call the delivery driver there is no response!';

On execution, you get:

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Create and train a model.

You can also create a model with a dataset. For this example, we will be using a dataset consisting of messages for E-commerce support tickets. The dataset will be uploaded as a file onto the GUI.

Use the CREATE MODEL syntax:

CREATE MODEL mindsdb.ecommerce_ticket_classifier2
FROM files (select * from queries2)
PREDICT tag
USING
engine = 'monkeylearn_engine',
monkeylearn_api_key = 'api_key',
model_id = 'model_id',
input_column = 'text';

Use the SELECT statement to make a prediction

SELECT * FROM ecommerce_ticket_classifier2
WHERE text = 'I ordered 4 units but only received 3';

On execution, you get:

The MindsDB model created with the MonkeyLearn model successfully predicted the tag of an E-commerce support ticket according to the text input.