Using fine-tuned classifiers based on YandexGPT

To use the examples, install cURL.

The example below is intended to be run in MacOS and Linux. To run it in Windows, see how to work with Bash in Microsoft Windows.

1. Create a file with the request body, e.g., body.json:

   {
     "modelUri": "cls://<folder_ID>/<classifier_ID>",
     "text": "<prompt_text>"
   }
   

   Where:

   • modelUri: ID of the model that will be used to classify the message. The parameter contains Yandex Cloud folder ID and the ID of the model tuned in DataSphere.
   • text: Message text. The total number of tokens per request must not exceed 8,000.

   The names of the classes between which the model will be distributing requests must be specified during model tuning; therefore, they are not provided in the request.

2. Send a request to the classifier by running the following command:

   export IAM_TOKEN=<IAM_token>
   curl \
     --request POST \
     --header "Authorization: Bearer ${IAM_TOKEN}" \
     --data "@<path_to_request_body_file>" \
     "https://llm.api.cloud.yandex.net:443/foundationModels/v1/textClassification"
   

   Note

   The https://llm.api.cloud.yandex.net:443/foundationModels/v1/textClassification endpoint works only with fine-tuned classifiers. For prompt-based classifiers, use https://llm.api.cloud.yandex.net/foundationModels/v1/fewShotTextClassification.

   In the response, the service will return the classification results with the confidence values for the probability of classifying the request text into each class:

   {
     "predictions": [
       {
         "label": "<class_1_name>",
         "confidence": 0.00010150671005249023
       },
       {
         "label": "<class_2_name>",
         "confidence": 0.000008225440979003906
       },
       ...
       {
         "label": "<class_n_name>",
         "confidence": 0.93212890625
       }
     ],
     "modelVersion": "<model_version>"
   }
   

   In multi-class classification, the sum of the confidence values for all classes is always 1.

   In multi-label classification, the confidence value for each class is calculated independently (the sum of the values is not equal to 1).