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Tuning a text classification model

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Updated at May 13, 2025

Model tuning based on the LoRA method is at the Preview stage.

If an out-of-the-box classifier model is not enough, you can fine-tune a YandexGPT Lite-based classifier model for it to classify your requests more accurately.

Getting started

To use the examples:

You can start working from the management console right away.

  1. Create a service account and assign the ai.editor role to it.

  2. Get the service account API key and save it.

    The following examples use API key authentication. Yandex Cloud ML SDK also supports IAM token and OAuth token authentication. For more information, see Authentication in Yandex Cloud ML SDK.

  3. Use the pip package manager to install the ML SDK library:

    pip install yandex-cloud-ml-sdk

  1. Get API authentication credentials as described in Authentication with the Yandex Foundation Models API.

  2. To use the examples, install cURL.

  3. Install gRPCurl.

  4. (Optional) Install the jq JSON stream processor.

  5. Get an IAM token used for authentication in the API.

    Note

    The IAM token has a short lifetime: no more than 12 hours.

Upload the dataset

Depending on the classification type the fine-tuned model will be used for, prepare UTF-8-encoded tuning data in JSON Lines format. If you want to split your data into two datasets for tuning and validation, repeat the steps below for each dataset. Use the IDs you got after uploading the datasets to start the fine-tuning process.

In this example, we only use the tuning dataset for fine-tuning.

Create a tuning dataset:

  1. In the management console, select the folder for which your account has the ai.playground.user and ai.datasets.editor roles or higher.

  2. From the list of services, select Foundation Models.

  3. In the left-hand panel, click Datasets.

  4. Click Create dataset.

  5. Enter a name and descriptions for the dataset. Follow these naming requirements:

    • It must be from 2 to 63 characters long.
    • It may contain lowercase Latin letters, numbers, and hyphens.
    • It must start with a letter and cannot end with a hyphen.

  6. In the Type field, select the classification type: Multi-label classification or Multi-class classification.

  7. Delete or add dataset labels. You can use them to split or join resources into logical groups.

  8. Click Select file or drag the JSON file you created earlier to the loading area.

  9. Click Create dataset.

  1. Create a file named dataset-create.py and add the following code to it:

    #!/usr/bin/env python3

from __future__ import annotations

import pathlib
from yandex_cloud_ml_sdk import YCloudML
from yandex_cloud_ml_sdk.auth import YandexCloudCLIAuth


def main() -> None:

    sdk = YCloudML(
        folder_id="<folder_ID>",
        auth="<API_key>",
    )

    # Viewing the list of all uploaded datasets
    for dataset in sdk.datasets.list():
        print(f"List of existing datasets {dataset=}")

    # Deleting all previously uploaded datasets
    for dataset in sdk.datasets.list():
        dataset.delete()

    # Creating a tuning dataset for the YandexGPT Lite base model
    dataset_draft = sdk.datasets.draft_from_path(
        task_type="<classification_type>",
        path="<file_path>",
        upload_format="jsonlines",
        name="multiclass",
    )

    # Waiting for the data to be uploaded and the dataset to be created
    operation = dataset_draft.upload_deferred()
    tuning_dataset = operation.wait()
    print(f"new {tuning_dataset=}")


if __name__ == "__main__":
    main()

    Where:

  2. Run the created file:

    python3 dataset-create.py

    Result:

    new tuning_dataset=Dataset(id='fds6vl5ttl0n********', folder_id='b1gt6g8ht345********', name='YandexGPT 
Lite tuning', description=None, metadata=None, created_by='ajegtlf2q28a********', created_at=datetime.
datetime(2025, 3, 13, 8, 12, 43), updated_at=datetime.datetime(2025, 3, 13, 8, 13, 17), labels=None, 
allow_data_logging=False, status=<DatasetStatus.READY: 3>, task_type='TextClassificationMulticlass', 
rows=4, size_bytes=5679, validation_errors=())

    Save the new dataset's ID (the id field value): you will need it when fine-tuning the model.

  1. Create a dataset:

    grpcurl \
  -H "Authorization: Bearer <IAM_token>" \
  -d @ \
  llm.api.cloud.yandex.net:443 yandex.cloud.ai.dataset.v1.DatasetService/Create <<EOM
  {
    "folder_id": "<folder_ID>", 
    "name": "My awesome dataset", 
    "task_type": "<classification_type>", 
    "upload_format": "jsonlines"
  }
EOM

    Where:

    • <IAM_token>: IAM token of the service account you got before you started.

    • <folder_ID>: ID of the folder you are creating the dataset in.

    • <classification_type>: Classification type the model will be tuned for using the new dataset. The possible values are:

      • TextClassificationMultilabel: Binary classification or multi-label classification.
      • TextClassificationMulticlass: Multi-class classification.

    Result:

    {
  "datasetId": "fds8hd01tset********",
  "dataset": {
    "datasetId": "fds8hd01tset********",
    "folderId": "b1gt6g8ht345********",
    "name": "My awesome dataset",
    "status": "DRAFT",
    "taskType": "<issue_type_specified_in_the_query>",
    "createdAt": "2025-01-20T14:51:34Z",
    "updatedAt": "2025-01-20T14:51:34Z",
    "createdById": "ajeg2b2et02f********",
    "createdBy": "ajeg2b2et02f********"
  }
}

    Save the new dataset's ID (the datasetId field value): you will need it to upload data to the dataset.

  2. Get a link to upload data into the dataset:

    grpcurl \
  -H "Authorization: Bearer <IAM_token>" \
  -d '{"dataset_id": "<dataset_ID>", "size_bytes": <dataset_size>}' \
  llm.api.cloud.yandex.net:443 yandex.cloud.ai.dataset.v1.DatasetService/GetUploadDraftUrl | jq

    Where:

    • <IAM_token>: IAM token of the service account you got before you started.
    • <dataset_ID>: Dataset ID you saved in the previous step.
    • <dataset_size>: Size in bytes of the file with data for tuning. In the terminal, you can get the file size using the ls -l <file_path> command.

    Result:

    {
  "datasetId": "fdso08c1u1cq********",
  "uploadUrl": "https://storage.yandexcloud.net/ai-fomo-drafts-prod/b1gt6g8ht345********/fdso08c1u1cq********?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20250120T105352Z&X-Amz-SignedHeaders=content-length%3Bhost&X-Amz-Expires=86400&X-Amz-Credential=YCAJE_WuJJ9D1r6huCoc8I3yO%2F20250120%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=611d7951994ae939acf4d32cc0c154c738d02adb2a04707a704f34ca********"
}

    The uploadUrl field of the response contains a link you can use to upload your data into the dataset.

    Tip

    If you did not use jq, replace all \u0026 occurrences with & in the link to use it to upload the dataset.

  3. Upload your data by specifying the link you got in the previous step and the path to the fine-tuning data file:

    curl \
  --request PUT \
  --upload-file <path_to_file> \
  "<link>"

  4. After the data upload is complete, run the dataset validation:

    grpcurl \
  -H "Authorization: Bearer <IAM_token>" \
  -d '{"dataset_id": "<dataset_ID>"}' \
  llm.api.cloud.yandex.net:443 yandex.cloud.ai.dataset.v1.DatasetService/Validate

    Where:

    Result:

    {
  "id": "fdso01v2jdd4********",
  "createdAt": "2025-01-20T11:03:48Z",
  "modifiedAt": "2025-01-20T11:03:48Z"
}

    Save the validation operation ID (id field). You will need it in the next step.

  5. Dataset validation may take some time. To find out validation status and get an error report (if any), send this request:

    grpcurl \
  -H "Authorization: Bearer <IAM_token>" \
  -d '{"operation_id": "<validation_operation_ID>"}' \
  llm.api.cloud.yandex.net:443 yandex.cloud.operation.OperationService/Get

    Where:

    • <IAM_token>: IAM token of the service account you got before you started.
    • <validation_operation_ID>: ID of the validation operation you saved in the previous step.

    Result:

    {
  "id": "fdso01v2jdd4********",
  "createdAt": "2025-01-20T11:03:48Z",
  "modifiedAt": "2025-01-20T11:04:46Z",
  "done": true,
  "response": {
    "@type": "type.googleapis.com/yandex.cloud.ai.dataset.v1.ValidateDatasetResponse",
    "datasetId": "fdso08c1u1cq********",
    "isValid": true
  }
}

    The isValid field is set to true. This means the loaded dataset was validated successfully.

Start tuning

  1. In the management console, select the folder for which your account has the ai.playground.user, ai.datasets.user, and ai.models.editor roles or higher.

  2. From the list of services, select Foundation Models.

  3. In the left-hand panel, click Fine-tuning models.

  4. Click Fine-tune model.

  5. Enter a name and descriptions for the dataset. Follow these naming requirements:

    • It must be from 2 to 63 characters long.
    • It may contain lowercase Latin letters, numbers, and hyphens.
    • It must start with a letter and cannot end with a hyphen.

  6. In the Task field, select Classification.

  7. Select the Type of classification: Multi-class or Multi-label.

  8. Optionally, add or delete the tuning labels. You can use them to split or join resources into logical groups.

  9. In the Model field, select the model you need.

  10. In the Dataset field, click Add.

  11. In the window that opens, go to the Select from existings tab and select the dataset you created earlier.

  12. Click Advanced settings to do advanced fine-tuning setup.

  13. Click Start fine-tuning.

  1. Create a file named start-tuning.py and add the following code to it:

    #!/usr/bin/env python3

from __future__ import annotations
import pathlib
import uuid
from yandex_cloud_ml_sdk import YCloudML


def main():
    sdk = YCloudML(
        folder_id="<folder_ID>",
        auth="<API_key>",
    )

    # Viewing the list of valid datasets
    for dataset in sdk.datasets.list(status="READY", name_pattern="multiclass"):
        print(f"List of existing datasets {dataset=}")

    # Setting the tuning dataset and the base model
    train_dataset = sdk.datasets.get("<dataset_ID>")
    base_model = sdk.models.text_classifiers("yandexgpt-lite")

    # Defining minimum parameters
    # To control more parameters, use `base_model.tune_deferred()`
    # Starting the tuning
    tuned_model = base_model.tune(
        train_dataset, name=str(uuid.uuid4()), classification_type="<classification_type>"
    )
    print(f"Resulting {tuned_model}")

    # You can access the fine-tuned model using the run() method
    classification_result = tuned_model.run("Wow!")
    print(f"{classification_result=}")

    # Or you can save the URI of the fine-tuned model
    # And call the fine-tuned model by its URI
    tuned_uri = tuned_model.uri
    model = sdk.models.text_classifiers(tuned_uri)
    classification_result = model.run("Cool!")
    print(f"{classification_result=}")


if __name__ == "__main__":
    main()

    Where:

  2. Run the created file:

    python3 start-tuning.py

    Result:

    Resulting TextClassifiersModel(uri=cls://b1gt6g8ht345********/yandexgpt-lite/
latest@tamrqj184r59v********, config=TextClassifiersModelConfig(task_description=None, labels=None, 
samples=None))
classification_result=TextClassifiersModelResult(predictions=(TextClassificationLabel(label='anger', 
confidence=0.0026986361481249332), TextClassificationLabel(label='sadness', confidence=0.
0013768149074167013), TextClassificationLabel(label='joy', confidence=0.00017048732843250036), 
TextClassificationLabel(label='fear', confidence=0.9950377345085144), TextClassificationLabel
(label='surprise', confidence=0.0007163778645917773)), model_version='')
classification_result=TextClassifiersModelResult(predictions=(TextClassificationLabel(label='anger', 
confidence=0.00042847482836805284), TextClassificationLabel(label='sadness', confidence=1.
9441255062702112e-05), TextClassificationLabel(label='joy', confidence=2.6669084718378144e-07), 
TextClassificationLabel(label='fear', confidence=0.999543309211731), TextClassificationLabel
(label='surprise', confidence=8.627005627204198e-06)), model_version='')

    Model tuning may take up to one day depending on the dataset size and the system load.

    Use the fine-tuned model's URI you got (the uri field value) when accessing the model.

  3. Fine-tuning metrics are available in TensorBoard format. You can open the downloaded file, for example, in the Yandex DataSphere project:

    metrics_url = new_model.get_metrics_url()
download_tensorboard(metrics_url)

  1. Start tuning:

    grpcurl \
  -H "Authorization: Bearer <IAM_token>" \
  -d @ \
  llm.api.cloud.yandex.net:443 yandex.cloud.ai.tuning.v1.TuningService/Tune <<EOM
  {
    "base_model_uri": "cls://<folder_ID>/yandexgpt-lite/latest",
    "train_datasets": [{"dataset_id": "<dataset_ID>", "weight": 1.0}],
    "name": "my first model",
    "<classification_type>": {}
  } 
EOM

    Where:

    • <IAM_token>: IAM token of the service account you got before you started.

    • <folder_ID>: ID of the folder you are fine-tuning the model in.

    • <dataset_ID>: Dataset ID you saved in the previous step.

    • <classification_type>: Classification type the model will be tuned for. The possible values are:

      • text_classification_multilabel: Binary classification or multi-label classification.
      • text_classification_multiclass: Multi-class classification.

    Result:

    {
  "id": "ftnlljf53kil********",
  "createdAt": "2025-01-20T11:17:33Z",
  "modifiedAt": "2025-01-20T11:17:33Z",
  "metadata": {
    "@type": "type.googleapis.com/yandex.cloud.ai.tuning.v1.TuningMetadata"
  }
}

    You will get the Operation object in response. Save the operation id you get in the response.

  2. Model tuning may take up to one day depending on the dataset size and the system load. To check if the fine-tuning is complete, request the operation status:

    grpcurl \
  -H "Authorization: Bearer <IAM_token>" \
  -d '{"operation_id": "<operation_ID>"}' \
  llm.api.cloud.yandex.net:443 yandex.cloud.operation.OperationService/Get

    Where:

    • <IAM_token>: IAM token of the service account you got before you started.
    • <operation_ID>: Model fine-tuning operation ID you got in the previous step.

    If the fine-tuning process is over, the Operation object will contain the tuned model's URI in the targetModelUri field:

    {
  "id": "ftnc7at9r66t********",
  "createdAt": "2025-01-20T15:41:06Z",
  "modifiedAt": "2025-01-20T15:46:10Z",
  "done": true,
  "metadata": {
    "@type": "type.googleapis.com/yandex.cloud.ai.tuning.v1.TuningMetadata",
    "status": "COMPLETED",
    "tuningTaskId": "ftnc7at9r66t********"
  },
  "response": {
    "@type": "type.googleapis.com/yandex.cloud.ai.tuning.v1.TuningResponse",
    "status": "COMPLETED",
    "targetModelUri": "cls://b1gt6g8ht345********/yandexgpt-lite/latest@tamr8gqhetveg********",
    "tuningTaskId": "ftnc7at9r66t********"
  }
}

    Use the fine-tuned model's URI you got (the targetModelUri field value) when accessing the model.

  3. Fine-tuning metrics are available in TensorBoard format. Get the link to download the file:

    grpcurl \
  -H "Authorization: Bearer <IAM_token>" \
  -d '{"task_id": "<job_ID>"}' \
  llm.api.cloud.yandex.net:443 yandex.cloud.ai.tuning.v1.TuningService/GetMetricsUrl

    You can open the downloaded file, for example, in the Yandex DataSphere project:

Accessing a fine-tuned classifier

Once the model is fine-tuned, save its URI in this format: cls://<base_model_URI>/<version>@<tuning_suffix>. Use the URI to send synchronous requests to your fine-tuned classifier.

See also

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