The Classification use case of the LWAI Prediction API lets you use embedding models to compute similarity scores between the incoming text and the labels. It returns the labels ranked in order of most similar to least similar. The classification use case is compatible with all Lucidworks hosted pre-trained and custom embedding models. The default behavior of embedding models is to always have a score returned. The topK and similarityCutoff parameters can be used to achieve behaviors where only the following are returned:
  • The single most applicable label
  • Labels with similarities that exceed a threshold
  • A set number of items
  • A set number if it exceeds the threshold
For detailed API specifications in Swagger/OpenAPI format, see Platform APIs.

​
Prerequisites

To use this API, you need:
  • The unique APPLICATION_ID for your Lucidworks AI application. For more information, see credentials to use APIs.
  • A bearer token generated with a scope value of machinelearning.predict. For more information, see Authentication API.
  • The USE_CASE and MODEL_ID fields for the use case request. The path is: /ai/prediction/USE_CASE/MODEL_ID. A list of supported models is returned in the Lucidworks AI Use Case API.

​
Unique values for the classification use case

Some parameter values available in the classification use case are unique to this use case, including values for the useCaseConfig parameter. Refer to the API spec for more information.

​
Classification use case example

curl --request POST \
  --url https://APPLICATION_ID.applications.lucidworks.com/ai/prediction/classification/{MODEL_ID} \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer ACCESS_TOKEN'
  --data '{
    "batch": [
        {
          "text": "Not all those who wander are lost."
        }
    ],
   "useCaseConfig": {
     "labels": [
        "Harry Potter",
        "Lord of the Rings"
        ]
    }
}'
For information about custom configuration parameters, see Classification configuration for custom embedding model.

​
Classification with 100 classes or less

If there are 100 or fewer classes and labels for the input, Lucidworks recommends you use the Classification use case. Whether you are using a pre-trained model, or a model you have trained, you can list all the possible labels in the request labels parameter along with the text to classify. The response returns all labels in descending order of the highest similarity score. You can also use the topK and similarityCutoff parameters to limit the response and more easily utilize the output labels. For more information about the parameters, see Unique values for the classification use case.

​
Classification with more than 100 classes

If there are more than 100 classes or labels, you must incorporate a side-car collection that contains all of the labels.

​
Using the Lucidworks AI embeddings and side-car collection

If you use the Lucidworks AI embeddings models and a side-car collection, the general process flow is as follows:
  • Use a pre-trained or custom model to vectorize the labels when the side-car collection is indexed.
  • After the labels are indexed, create a query pipeline with a vectorized stage and a hybrid stage to search the labels.
    • The query pipeline can also be used in a different query pipeline to check the labels.
    • The hybrid stage can be used to replicate the topK and similarityCutoff parameter settings to limit the response and more easily utilize the output labels.

​
Using the Fusion Smart Answers model and side-car collection

If you use the Fusion Smart Answers Coldstart training or the Smart Answers Supervised Training job, the general process is as follows:
  • To format the input, set the class field and the field to be classified as a pair of documents in a collection.
  • Input the collection into the Fusion job with the following information:
    • Specify which field contains the documents to be used to learn about the vocabulary.
    • Separate the fields by a comma. For example class,query.
  • Save and run the Fusion job.
  • The subsequent model can be used as the model to both index and query the side-car classes’ collection.

​
Evaluating classification

To evaluate how well the classification is performed, use the F-score metric. This is also referred to as the F1 metric. The formula for the metric is:
Classification F1 metric formula
In the implementation of the F1 score in the evaluation mode, it is more precisely the Micro F1 score, where each query is evaluated equally.
Classification Micro F1 metric formula
The following is an example of the F1 metric where a selection of ecommerce and knowledgement management query classification tasks were run on a Fusion classification job. The job used logistic regression then StarSpace, and the Lucidworks AI trained model classification. The Lucidworks AI custom trained classification model outperformed the Fusion jobs when correctly classifying queries. Classification F1 metric example