Prever um único rótulo de classificação de texto
Mantenha tudo organizado com as coleções
Salve e categorize o conteúdo com base nas suas preferências.
Recebe a previsão para um único rótulo de classificação de texto usando o método de previsão.
Exemplo de código
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[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],[],[],[],null,["# Predict for text classification single label\n\nGets prediction for text classification single label using the predict method.\n\nCode sample\n-----------\n\n### Python\n\n\nBefore trying this sample, follow the Python setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Python API\nreference documentation](/python/docs/reference/aiplatform/latest).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n from google.cloud import aiplatform\n from google.cloud.aiplatform.gapic.schema import predict\n from google.protobuf import json_format\n from google.protobuf.struct_pb2 import Value\n\n\n def predict_text_classification_single_label_sample(\n project: str,\n endpoint_id: str,\n content: str,\n location: str = \"us-central1\",\n api_endpoint: str = \"us-central1-aiplatform.googleapis.com\",\n ):\n # The AI Platform services require regional API endpoints.\n client_options = {\"api_endpoint\": api_endpoint}\n # Initialize client that will be used to create and send requests.\n # This client only needs to be created once, and can be reused for multiple requests.\n client = aiplatform.gapic.https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.prediction_service.PredictionServiceClient.html(client_options=client_options)\n instance = predict.instance.https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.v1.schema.predict.instance_v1.types.TextClassificationPredictionInstance.html(\n content=content,\n ).to_value()\n instances = [instance]\n parameters_dict = {}\n parameters = json_format.ParseDict(parameters_dict, Value())\n endpoint = client.https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.prediction_service.PredictionServiceClient.html#google_cloud_aiplatform_v1_services_prediction_service_PredictionServiceClient_endpoint_path(\n project=project, location=location, endpoint=endpoint_id\n )\n response = client.https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.prediction_service.PredictionServiceClient.html(\n endpoint=endpoint, instances=instances, parameters=parameters\n )\n print(\"response\")\n print(\" deployed_model_id:\", response.deployed_model_id)\n\n predictions = response.predictions\n for prediction in predictions:\n print(\" prediction:\", dict(prediction))\n\nWhat's next\n-----------\n\n\nTo search and filter code samples for other Google Cloud products, see the\n[Google Cloud sample browser](/docs/samples?product=aiplatform)."]]