Describe a Model
Description
The DESCRIBE statement is used to display the attributes of an existing model.
The available options to describe a model depend on the underlying engine.
Syntax
Here is how to retrieve general information on the model:
DESCRIBE model_name;
Or:
DESCRIBE MODEL model_name;
This command is similar to the below command:
SELECT *
FROM models
WHERE name = 'model_name';
One difference between these two commands is that DESCRIBE outputs an additional column that stores all available options to describe a model, depending on the underlying engine.
Examples
Lightwood Models
MindsDB uses the Lightwood engine by default. Let’s see how to describe such models.
DESCRIBE [MODEL] home_rentals_model;
On execution we get:
+--------------------------------------+--------------------+-----------+---------+--------+---------+----------+----------+--------------+---------------+-----------------+--------+--------------------------------------+----------------------------+--------+
| tables | NAME | ENGINE | PROJECT | ACTIVE | VERSION | STATUS | ACCURACY | PREDICT | UPDATE_STATUS | MINDSDB_VERSION | ERROR | SELECT_DATA_QUERY | TRAINING_OPTIONS | TAG |
+--------------------------------------+--------------------+-----------+---------+--------+---------+----------+----------+--------------+---------------+-----------------+--------+--------------------------------------+----------------------------+--------+
| ["info","features","model","jsonai"] | home_rentals_model | lightwood | mindsdb | true | 1 | complete | 0.999 | rental_price | up_to_date | 23.4.4.0 | [NULL] | SELECT * FROM demo_data.home_rentals | {'target': 'rental_price'} | [NULL] |
+--------------------------------------+--------------------+-----------+---------+--------+---------+----------+----------+--------------+---------------+-----------------+--------+--------------------------------------+----------------------------+--------+
The tables output column lists all available options to describe a model.
DESCRIBE [MODEL] home_rentals_model.info;
The above command returns the following output columns:
| Name | Description |
|---|---|
accuracies | It lists the accuracy function used to evaluate the model and the achieved score. |
column_importances | It lists all feature-type columns and assigns importance values. |
outputs | The target column. |
inputs | All the feature columns. |
NLP Models
MindsDB offers NLP models that utilize either Hugging Face or OpenAI engines. Let’s see how to describe such models.
DESCRIBE [MODEL] sentiment_classifier;
On execution we get:
+---------------------+----------------------+--------+---------+--------+---------+----------+----------+-----------+---------------+-----------------+--------+-------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+
| tables | NAME | ENGINE | PROJECT | ACTIVE | VERSION | STATUS | ACCURACY | PREDICT | UPDATE_STATUS | MINDSDB_VERSION | ERROR | SELECT_DATA_QUERY | TRAINING_OPTIONS | TAG |
+---------------------+----------------------+--------+---------+--------+---------+----------+----------+-----------+---------------+-----------------+--------+-------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+
| ["args","metadata"] | sentiment_classifier | openai | mindsdb | true | 1 | complete | [NULL] | sentiment | up_to_date | 23.1.3.2 | [NULL] | [NULL] | {'target': 'sentiment', 'using': {'prompt_template': 'describe the sentiment of the reviews\n strictly as "positive", "neutral", or "negative".\n "I love the product":positive\n "It is a scam":negative\n "{{review}}.":'}} | [NULL] |
+---------------------+----------------------+--------+---------+--------+---------+----------+----------+-----------+---------------+-----------------+--------+-------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------+
The tables output column lists all available options to describe a model.
DESCRIBE [MODEL] sentiment_classifier.args;
The above command returns the following output columns:
| Name | Description |
|---|---|
key | It stores parameters, such as prompt_template and target. |
value | It stores parameter values. |
Nixtla Models
MindsDB integrates Nixtla engines, such as StatsForecast, NeuralForecast, and HierarchicalForecast. Let’s see how to describe models based on Nixtla engines.
DESCRIBE [MODEL] quarterly_expenditure_forecaster;
On execution we get:
+-----------------------------+----------------------------------+---------------+---------+--------+---------+----------+----------+-------------+---------------+-----------------+--------+---------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------+--------+
| tables | NAME | ENGINE | PROJECT | ACTIVE | VERSION | STATUS | ACCURACY | PREDICT | UPDATE_STATUS | MINDSDB_VERSION | ERROR | SELECT_DATA_QUERY | TRAINING_OPTIONS | TAG |
+-----------------------------+----------------------------------+---------------+---------+--------+---------+----------+----------+-------------+---------------+-----------------+--------+---------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------+--------+
| ["info","features","model"] | quarterly_expenditure_forecaster | statsforecast | mindsdb | true | 1 | complete | [NULL] | expenditure | up_to_date | 23.4.4.0 | [NULL] | SELECT * FROM historical_expenditures | {'target': 'expenditure', 'using': {}, 'timeseries_settings': {'is_timeseries': True, 'order_by': 'month', 'horizon': 3, 'group_by': ['category']}} | [NULL] |
+-----------------------------+----------------------------------+---------------+---------+--------+---------+----------+----------+-------------+---------------+-----------------+--------+---------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------+--------+
The tables output column lists all available options to describe a model.
DESCRIBE [MODEL] quarterly_expenditure_forecaster.info;
The above command returns the following output columns:
| Name | Description |
|---|---|
accuracies | It lists the chosen model name and its accuracy score. |
outputs | The target column. |
inputs | All the feature columns. |
Other Models
Models that utlize LangChain or are brought to MindsDB via MLflow can be described as follows:
DESCRIBE [MODEL] other_model;
The above command returs ["info"] in its first output column.
DESCRIBE [MODEL] other_model.info;
The above command lists basic model information.
If you need more information on how to DESCRIBE [MODEL] or understand the results, feel free to ask us on the community Slack workspace.
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