Computation-based evaluation pipeline

You can evaluate the performance of foundation models and your tuned generative AI models on Vertex AI. The models are evaluated using a set of metrics against an evaluation dataset that you provide. This page explains how computation-based model evaluation through the evaluation pipeline service works, how to create and format the evaluation dataset, and how to perform the evaluation using the Google Cloud console, Vertex AI API, or the Vertex AI SDK for Python.

How computation-based model evaluation works

To evaluate the performance of a model, you first create an evaluation dataset that contains prompt and ground truth pairs. For each pair, the prompt is the input that you want to evaluate, and the ground truth is the ideal response for that prompt. During evaluation, the prompt in each pair of the evaluation dataset is passed to the model to produce an output. The output generated by the model and the ground truth from the evaluation dataset are used to compute the evaluation metrics.

The type of metrics used for evaluation depends on the task that you are evaluating. The following table shows the supported tasks and the metrics used to evaluate each task:

Task Metric
Classification Micro-F1, Macro-F1, Per class F1
Summarization ROUGE-L
Question answering Exact Match
Text generation BLEU, ROUGE-L

Supported models

Model evaluation is supported for the base and tuned versions of text-bison.

Prepare evaluation dataset

The evaluation dataset that's used for model evaluation includes prompt and ground truth pairs that align with the task that you want to evaluate. Your dataset must include a minimum of 1 prompt and ground truth pair and at least 10 pairs for meaningful metrics. The more examples you give, the more meaningful the results.

Dataset format

Your evaluation dataset must be in JSON Lines (JSONL) format where each line contains a single prompt and ground truth pair specified in the input_text and output_text fields, respectively. The input_text field contains the prompt that you want to evaluate, and the output_text field contains the ideal response for the prompt.

The maximum token length for input_text is 8,192, and the maximum token length for output_text is 1,024.

Upload evaluation dataset to Cloud Storage

You can either create a new Cloud Storage bucket or use an existing one to store your dataset file. The bucket must be in the same region as the model.

After your bucket is ready, upload your dataset file to the bucket.

Perform model evaluation

You can evaluate models by using the REST API or the Google Cloud console.

REST

To create a model evaluation job, send a POST request by using the pipelineJobs method.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: The Google Cloud project that runs the pipeline components.
  • PIPELINEJOB_DISPLAYNAME: A display name for the pipelineJob.
  • LOCATION: The region to run the pipeline components. Currently, only us-central1 is supported.
  • DATASET_URI: The Cloud Storage URI of your reference dataset. You can specify one or multiple URIs. This parameter supports wildcards. To learn more about this parameter, see InputConfig.
  • OUTPUT_DIR: The Cloud Storage URI to store evaluation output.
  • MODEL_NAME: Specify a publisher model or a tuned model resource as follows:
    • Publisher model: publishers/google/models/MODEL@MODEL_VERSION

      Example: publishers/google/models/text-bison@001

    • Tuned model: projects/PROJECT_NUMBER/locations/LOCATION/models/ENDPOINT_ID

      Example: projects/123456789012/locations/us-central1/models/1234567890123456789

    The evaluation job doesn't impact any existing deployments of the model or their resources.

  • EVALUATION_TASK: The task that you want to evaluate the model on. The evaluation job computes a set of metrics relevant to that specific task. Acceptable values include the following:
    • summarization
    • question-answering
    • text-generation
    • classification
  • INSTANCES_FORMAT: The format of your dataset. Currently, only jsonl is supported. To learn more about this parameter, see InputConfig.
  • PREDICTIONS_FORMAT: The format of the evaluation output. Currently, only jsonl is supported. To learn more about this parameter, see InputConfig.
  • MACHINE_TYPE: (Optional) The machine type for running the evaluation job. The default value is e2-highmem-16. For a list of supported machine types, see Machine types.
  • SERVICE_ACCOUNT: (Optional) The service account to use for running the evaluation job. To learn how to create a custom service account, see Configure a service account with granular permissions. If unspecified, the Vertex AI Custom Code Service Agent is used.
  • NETWORK: (Optional) The fully qualified name of the Compute Engine network to peer the evaluatiuon job to. The format of the network name is projects/PROJECT_NUMBER/global/networks/NETWORK_NAME. If you specify this field, you need to have a VPC Network Peering for Vertex AI. If left unspecified, the evaluation job is not peered with any network.
  • KEY_NAME: (Optional) The name of the customer-managed encryption key (CMEK). If configured, resources created by the evaluation job is encrypted using the provided encryption key. The format of the key name is projects/PROJECT_ID/locations/REGION/keyRings/KEY_RING/cryptoKeys/KEY. The key needs to be in the same region as the evaluation job.

HTTP method and URL: 

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/pipelineJobs

Request JSON body: 

{
  "displayName": "PIPELINEJOB_DISPLAYNAME",
  "runtimeConfig": {
    "gcsOutputDirectory": "gs://OUTPUT_DIR",
    "parameterValues": {
      "project": "PROJECT_ID",
      "location": "LOCATION",
      "batch_predict_gcs_source_uris": ["gs://DATASET_URI"],
      "batch_predict_gcs_destination_output_uri": "gs://OUTPUT_DIR",
      "model_name": "MODEL_NAME",
      "evaluation_task": "EVALUATION_TASK",
      "batch_predict_instances_format": "INSTANCES_FORMAT",
      "batch_predict_predictions_format: "PREDICTIONS_FORMAT",
      "machine_type": "MACHINE_TYPE",
      "service_account": "SERVICE_ACCOUNT",
      "network": "NETWORK",
      "encryption_spec_key_name": "KEY_NAME"
    }
  },
  "templateUri": "https://us-kfp.pkg.dev/vertex-evaluation/pipeline-templates/evaluation-llm-text-generation-pipeline/1.0.1"
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/pipelineJobs"

PowerShell

Save the request body in a file named request.json, and execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/pipelineJobs" | Select-Object -Expand Content

You should receive a JSON response similar to the following. Note that pipelineSpec has been truncated to save space.

Example curl command

PROJECT_ID=myproject
REGION=us-central1
MODEL_NAME=publishers/google/models/text-bison@001
TEST_DATASET_URI=gs://my-gcs-bucket-uri/dataset.jsonl
OUTPUT_DIR=gs://my-gcs-bucket-uri/output

curl \
-X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
"https://${REGION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${REGION}/pipelineJobs" -d \
$'{
  "displayName": "evaluation-llm-text-generation-pipeline",
  "runtimeConfig": {
    "gcsOutputDirectory": "'${OUTPUT_DIR}'",
    "parameterValues": {
      "project": "'${PROJECT_ID}'",
      "location": "'${REGION}'",
      "batch_predict_gcs_source_uris": ["'${TEST_DATASET_URI}'"],
      "batch_predict_gcs_destination_output_uri": "'${OUTPUT_DIR}'",
      "model_name": "'${MODEL_NAME}'",
    }
  },
  "templateUri": "https://us-kfp.pkg.dev/vertex-evaluation/pipeline-templates/evaluation-llm-text-generation-pipeline/1.0.1"
}'

Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Python API reference documentation. 


from google.auth import default
import vertexai
from vertexai.preview.language_models import (
    EvaluationTextClassificationSpec,
    TextGenerationModel,
)

# Set credentials for the pipeline components used in the evaluation task
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])


def evaluate_model(
    project_id: str,
    location: str,
) -> object:
    """Evaluate the performance of a generative AI model."""

    vertexai.init(project=project_id, location=location, credentials=credentials)

    # Create a reference to a generative AI model
    model = TextGenerationModel.from_pretrained("text-bison@002")

    # Define the evaluation specification for a text classification task
    task_spec = EvaluationTextClassificationSpec(
        ground_truth_data=[
            "gs://cloud-samples-data/ai-platform/generative_ai/llm_classification_bp_input_prompts_with_ground_truth.jsonl"
        ],
        class_names=["nature", "news", "sports", "health", "startups"],
        target_column_name="ground_truth",
    )

    # Evaluate the model
    eval_metrics = model.evaluate(task_spec=task_spec)
    print(eval_metrics)

    return eval_metrics

Console

To create a model evaluation job by using the Google Cloud console, perform the following steps:

  1. In the Google Cloud console, go to the Vertex AI Model Registry page.

    Go to Vertex AI Model Registry

  2. Click the name of the model that you want to evaluate.
  3. In the Evaluate tab, click Create evaluation and configure as follows:
    • Objective: Select the task that you want to evaluate.
    • Target column or field: (Classification only) Enter the target column for prediction. Example: ground_truth.
    • Source path: Enter or select the URI of your evaluation dataset.
    • Output format: Enter the format of the evaluation output. Currently, only jsonl is supported.
    • Cloud Storage path: Enter or select the URI to store evaluation output.
    • Class names: (Classification only) Enter the list of possible class names.
    • Number of compute nodes: Enter the number of compute nodes to run the evaluation job.
    • Machine type: Select a machine type to use for running the evaluation job.
  4. Click Start evaluation

View evaluation results

You can find the evaluation results in the Cloud Storage output directory that you specified when creating the evaluation job. The file is named evaluation_metrics.json.

For tuned models, you can also view evaluation results in the Google Cloud console:

  1. In the Vertex AI section of the Google Cloud console, go to the Vertex AI Model Registry page.

    Go to Vertex AI Model Registry

  2. Click the name of the model to view its evaluation metrics.

  3. In the Evaluate tab, click the name of the evaluation run that you want to view.

What's next