文本分类单个标签的预测
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
使用预测方法获取文本分类单个标签的预测。
代码示例
如未另行说明,那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可,并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情,请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","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)."]]