TextAnalyticsClient 클래스

문서 일괄 처리에 대한 감정을 분석합니다. show_opinion_mining 사용하여 오피니언 마이닝을 켭니다.

각 감정 클래스(양수, 부정 및 중립)의 감정 점수와 문서 내의 각 문장에 대한 감정 예측 및 감정 점수를 반환합니다.

서비스 데이터 제한은 를 참조 https://aka.ms/azsdk/textanalytics/data-limits 하세요.

버전 v3.1의 새로운 기능: show_opinion_mining, disable_service_logs 및 string_index_type 키워드(keyword) 인수입니다.

analyze_sentiment(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, disable_service_logs: bool | None = None, language: str | None = None, model_version: str | None = None, show_opinion_mining: bool | None = None, show_stats: bool | None = None, string_index_type: str | None = None, **kwargs: Any) -> List[AnalyzeSentimentResult | DocumentError]

매개 변수

반환

원래 문서가 전달된 순서대로 및 DocumentError 의 AnalyzeSentimentResult 결합된 목록입니다.

반환 형식

예외

예제

문서 일괄 처리로 감정을 분석합니다.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))

   documents = [
       """I had the best day of my life. I decided to go sky-diving and it made me appreciate my whole life so much more.
       I developed a deep-connection with my instructor as well, and I feel as if I've made a life-long friend in her.""",
       """This was a waste of my time. All of the views on this drop are extremely boring, all I saw was grass. 0/10 would
       not recommend to any divers, even first timers.""",
       """This was pretty good! The sights were ok, and I had fun with my instructors! Can't complain too much about my experience""",
       """I only have one word for my experience: WOW!!! I can't believe I have had such a wonderful skydiving company right
       in my backyard this whole time! I will definitely be a repeat customer, and I want to take my grandmother skydiving too,
       I know she'll love it!"""
   ]


   result = text_analytics_client.analyze_sentiment(documents, show_opinion_mining=True)
   docs = [doc for doc in result if not doc.is_error]

   print("Let's visualize the sentiment of each of these documents")
   for idx, doc in enumerate(docs):
       print(f"Document text: {documents[idx]}")
       print(f"Overall sentiment: {doc.sentiment}")

장기 실행 추상화 요약 작업을 시작합니다.

추상적 요약에 대한 개념적 논의는 서비스 설명서를 참조하세요. https://video2.skills-academy.com/azure/cognitive-services/language-service/summarization/overview

버전 2023-04-01의 새로운 기능: begin_abstract_summary 클라이언트 메서드입니다.

begin_abstract_summary(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, continuation_token: str | None = None, disable_service_logs: bool | None = None, display_name: str | None = None, language: str | None = None, polling_interval: int | None = None, show_stats: bool | None = None, model_version: str | None = None, string_index_type: str | None = None, sentence_count: int | None = None, **kwargs: Any) -> TextAnalysisLROPoller[ItemPaged[AbstractiveSummaryResult | DocumentError]]

매개 변수

반환

TextAnalysisLROPoller의 instance. 이 개체에서 result()를 호출하여 및 DocumentError의 AbstractiveSummaryResult 다른 유형의 페이저블을 반환합니다.

반환 형식

예외

예제

문서 일괄 처리에서 추상화 요약을 수행합니다.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint,
       credential=AzureKeyCredential(key),
   )

   document = [
       "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic, "
       "human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI Cognitive "
       "Services, I have been working with a team of amazing scientists and engineers to turn this quest into a "
       "reality. In my role, I enjoy a unique perspective in viewing the relationship among three attributes of "
       "human cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z). At the "
       "intersection of all three, there's magic-what we call XYZ-code as illustrated in Figure 1-a joint "
       "representation to create more powerful AI that can speak, hear, see, and understand humans better. "
       "We believe XYZ-code will enable us to fulfill our long-term vision: cross-domain transfer learning, "
       "spanning modalities and languages. The goal is to have pretrained models that can jointly learn "
       "representations to support a broad range of downstream AI tasks, much in the way humans do today. "
       "Over the past five years, we have achieved human performance on benchmarks in conversational speech "
       "recognition, machine translation, conversational question answering, machine reading comprehension, "
       "and image captioning. These five breakthroughs provided us with strong signals toward our more ambitious "
       "aspiration to produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
       "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a foundational "
       "component of this aspiration, if grounded with external knowledge sources in the downstream AI tasks."
   ]

   poller = text_analytics_client.begin_abstract_summary(document)
   abstract_summary_results = poller.result()
   for result in abstract_summary_results:
       if result.kind == "AbstractiveSummarization":
           print("Summaries abstracted:")
           [print(f"{summary.text}\n") for summary in result.summaries]
       elif result.is_error is True:
           print("...Is an error with code '{}' and message '{}'".format(
               result.error.code, result.error.message
           ))

장기 실행 작업을 시작하여 문서 일괄 처리에 대해 다양한 텍스트 분석 작업을 수행합니다.

더 큰 문서를 분석하거나 여러 텍스트 분석 작업을 하나의 호출로 결합하려는 경우 이 함수를 사용하는 것이 좋습니다. 그렇지 않으면 작업 특정 엔드포인트(예 analyze_sentiment: )를 사용하는 것이 좋습니다.

버전 v3.1의 새로운 기능: begin_analyze_actions 클라이언트 메서드입니다.

버전 2022-05-01의 새로운 기능: RecognizeCustomEntitiesAction, SingleLabelClassifyAction, MultiLabelClassifyAction 및 AnalyzeHealthcareEntitiesAction 입력 옵션 및 해당 RecognizeCustomEntitiesResult, ClassifyDocumentResult 및 AnalyzeHealthcareEntitiesResult 결과 개체

버전 2023-04-01의 새로운 기능: ExtractiveSummaryAction 및 AbstractiveSummaryAction 입력 옵션 및 해당 ExtractiveSummaryResult 및 AbstractiveSummaryResult 결과 개체입니다.

begin_analyze_actions(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], actions: List[RecognizeEntitiesAction | RecognizeLinkedEntitiesAction | RecognizePiiEntitiesAction | ExtractKeyPhrasesAction | AnalyzeSentimentAction | RecognizeCustomEntitiesAction | SingleLabelClassifyAction | MultiLabelClassifyAction | AnalyzeHealthcareEntitiesAction | ExtractiveSummaryAction | AbstractiveSummaryAction], *, continuation_token: str | None = None, display_name: str | None = None, language: str | None = None, polling_interval: int | None = None, show_stats: bool | None = None, **kwargs: Any) -> TextAnalysisLROPoller[ItemPaged[List[RecognizeEntitiesResult | RecognizeLinkedEntitiesResult | RecognizePiiEntitiesResult | ExtractKeyPhrasesResult | AnalyzeSentimentResult | RecognizeCustomEntitiesResult | ClassifyDocumentResult | AnalyzeHealthcareEntitiesResult | ExtractiveSummaryResult | AbstractiveSummaryResult | DocumentError]]]

매개 변수

반환

TextAnalysisLROPoller의 instance. 폴러 개체에서 result() 를 호출하여 페이지 형식이 다른 목록 목록을 반환합니다. 이 목록 목록은 먼저 입력한 문서에 따라 정렬된 다음 입력한 작업에 따라 정렬됩니다. 예를 들어 문서 입력 ["Hello", "world"] 및 actions RecognizeEntitiesAction 및 AnalyzeSentimentAction이 목록 목록을 반복할 때 먼저 "Hello" 문서의 RecognizeEntitiesResult 작업 결과를 반복하고 "Hello"의 를 얻은 다음 "Hello"의 를 AnalyzeSentimentResult 가져옵니다. 그러면 "world"의 및 AnalyzeSentimentResult 가 RecognizeEntitiesResult 표시됩니다.

반환 형식

예외

예제

장기 실행 작업을 시작하여 문서 일괄 처리에 대해 다양한 텍스트 분석 작업을 수행합니다.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import (
       TextAnalyticsClient,
       RecognizeEntitiesAction,
       RecognizeLinkedEntitiesAction,
       RecognizePiiEntitiesAction,
       ExtractKeyPhrasesAction,
       AnalyzeSentimentAction,
   )

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint,
       credential=AzureKeyCredential(key),
   )

   documents = [
       'We went to Contoso Steakhouse located at midtown NYC last week for a dinner party, and we adore the spot! '
       'They provide marvelous food and they have a great menu. The chief cook happens to be the owner (I think his name is John Doe) '
       'and he is super nice, coming out of the kitchen and greeted us all.'
       ,

       'We enjoyed very much dining in the place! '
       'The Sirloin steak I ordered was tender and juicy, and the place was impeccably clean. You can even pre-order from their '
       'online menu at www.contososteakhouse.com, call 312-555-0176 or send email to order@contososteakhouse.com! '
       'The only complaint I have is the food didn\'t come fast enough. Overall I highly recommend it!'
   ]

   poller = text_analytics_client.begin_analyze_actions(
       documents,
       display_name="Sample Text Analysis",
       actions=[
           RecognizeEntitiesAction(),
           RecognizePiiEntitiesAction(),
           ExtractKeyPhrasesAction(),
           RecognizeLinkedEntitiesAction(),
           AnalyzeSentimentAction(),
       ],
   )

   document_results = poller.result()
   for doc, action_results in zip(documents, document_results):
       print(f"\nDocument text: {doc}")
       for result in action_results:
           if result.kind == "EntityRecognition":
               print("...Results of Recognize Entities Action:")
               for entity in result.entities:
                   print(f"......Entity: {entity.text}")
                   print(f".........Category: {entity.category}")
                   print(f".........Confidence Score: {entity.confidence_score}")
                   print(f".........Offset: {entity.offset}")

           elif result.kind == "PiiEntityRecognition":
               print("...Results of Recognize PII Entities action:")
               for pii_entity in result.entities:
                   print(f"......Entity: {pii_entity.text}")
                   print(f".........Category: {pii_entity.category}")
                   print(f".........Confidence Score: {pii_entity.confidence_score}")

           elif result.kind == "KeyPhraseExtraction":
               print("...Results of Extract Key Phrases action:")
               print(f"......Key Phrases: {result.key_phrases}")

           elif result.kind == "EntityLinking":
               print("...Results of Recognize Linked Entities action:")
               for linked_entity in result.entities:
                   print(f"......Entity name: {linked_entity.name}")
                   print(f".........Data source: {linked_entity.data_source}")
                   print(f".........Data source language: {linked_entity.language}")
                   print(
                       f".........Data source entity ID: {linked_entity.data_source_entity_id}"
                   )
                   print(f".........Data source URL: {linked_entity.url}")
                   print(".........Document matches:")
                   for match in linked_entity.matches:
                       print(f"............Match text: {match.text}")
                       print(f"............Confidence Score: {match.confidence_score}")
                       print(f"............Offset: {match.offset}")
                       print(f"............Length: {match.length}")

           elif result.kind == "SentimentAnalysis":
               print("...Results of Analyze Sentiment action:")
               print(f"......Overall sentiment: {result.sentiment}")
               print(
                   f"......Scores: positive={result.confidence_scores.positive}; \
                   neutral={result.confidence_scores.neutral}; \
                   negative={result.confidence_scores.negative} \n"
               )

           elif result.is_error is True:
               print(
                   f"...Is an error with code '{result.error.code}' and message '{result.error.message}'"
               )

       print("------------------------------------------")

의료 엔터티를 분석하고 문서 일괄 처리에서 이러한 엔터티 간의 관계를 식별합니다.

엔터티는 UMLS, CHV, MSH 등과 같은 기존 기술 자료에서 찾을 수 있는 참조와 연결됩니다.

우리는 또한 엔터티 간에 발견 된 관계를 추출, 예를 들어 "주제했다 100 이부프로펜의 mg", 우리는 사이의 관계를 추출 할 것 "100 mg" 복용량과 "이부프로펜" 약물.

버전 v3.1의 새로운 기능: begin_analyze_healthcare_entities 클라이언트 메서드입니다.

버전 2022-05-01의 새로운 기능: display_name 키워드(keyword) 인수입니다.

begin_analyze_healthcare_entities(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, continuation_token: str | None = None, disable_service_logs: bool | None = None, display_name: str | None = None, language: str | None = None, model_version: str | None = None, polling_interval: int | None = None, show_stats: bool | None = None, string_index_type: str | None = None, **kwargs: Any) -> AnalyzeHealthcareEntitiesLROPoller[ItemPaged[AnalyzeHealthcareEntitiesResult | DocumentError]]

매개 변수

반환

AnalyzeHealthcareEntitiesLROPoller의 instance. 이 개체에서 result()를 호출하여 및 DocumentError의 AnalyzeHealthcareEntitiesResult 다른 유형의 페이저블을 반환합니다.

반환 형식

예외

예제

문서 일괄 처리에서 의료 엔터티를 인식합니다.

   import os
   import typing
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient, HealthcareEntityRelation

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint,
       credential=AzureKeyCredential(key),
   )

   documents = [
       """
       Patient needs to take 100 mg of ibuprofen, and 3 mg of potassium. Also needs to take
       10 mg of Zocor.
       """,
       """
       Patient needs to take 50 mg of ibuprofen, and 2 mg of Coumadin.
       """
   ]

   poller = text_analytics_client.begin_analyze_healthcare_entities(documents)
   result = poller.result()

   docs = [doc for doc in result if not doc.is_error]

   print("Let's first visualize the outputted healthcare result:")
   for doc in docs:
       for entity in doc.entities:
           print(f"Entity: {entity.text}")
           print(f"...Normalized Text: {entity.normalized_text}")
           print(f"...Category: {entity.category}")
           print(f"...Subcategory: {entity.subcategory}")
           print(f"...Offset: {entity.offset}")
           print(f"...Confidence score: {entity.confidence_score}")
           if entity.data_sources is not None:
               print("...Data Sources:")
               for data_source in entity.data_sources:
                   print(f"......Entity ID: {data_source.entity_id}")
                   print(f"......Name: {data_source.name}")
           if entity.assertion is not None:
               print("...Assertion:")
               print(f"......Conditionality: {entity.assertion.conditionality}")
               print(f"......Certainty: {entity.assertion.certainty}")
               print(f"......Association: {entity.assertion.association}")
       for relation in doc.entity_relations:
           print(f"Relation of type: {relation.relation_type} has the following roles")
           for role in relation.roles:
               print(f"...Role '{role.name}' with entity '{role.entity.text}'")
       print("------------------------------------------")

   print("Now, let's get all of medication dosage relations from the documents")
   dosage_of_medication_relations = [
       entity_relation
       for doc in docs
       for entity_relation in doc.entity_relations if entity_relation.relation_type == HealthcareEntityRelation.DOSAGE_OF_MEDICATION
   ]

장기 실행 추출 요약 작업을 시작합니다.

추출 요약에 대한 개념적 논의는 서비스 설명서를 참조하세요. https://video2.skills-academy.com/azure/cognitive-services/language-service/summarization/overview

버전 2023-04-01의 새로운 기능: begin_extract_summary 클라이언트 메서드입니다.

begin_extract_summary(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, continuation_token: str | None = None, disable_service_logs: bool | None = None, display_name: str | None = None, language: str | None = None, polling_interval: int | None = None, show_stats: bool | None = None, model_version: str | None = None, string_index_type: str | None = None, max_sentence_count: int | None = None, order_by: Literal['Rank', 'Offset'] | None = None, **kwargs: Any) -> TextAnalysisLROPoller[ItemPaged[ExtractiveSummaryResult | DocumentError]]

매개 변수

반환

TextAnalysisLROPoller의 instance. 이 개체에서 result()를 호출하여 및 DocumentError의 ExtractiveSummaryResult 다른 유형의 페이저블을 반환합니다.

반환 형식

예외

예제

문서 일괄 처리에서 추출 요약을 수행합니다.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint,
       credential=AzureKeyCredential(key),
   )

   document = [
       "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic, "
       "human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI Cognitive "
       "Services, I have been working with a team of amazing scientists and engineers to turn this quest into a "
       "reality. In my role, I enjoy a unique perspective in viewing the relationship among three attributes of "
       "human cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z). At the "
       "intersection of all three, there's magic-what we call XYZ-code as illustrated in Figure 1-a joint "
       "representation to create more powerful AI that can speak, hear, see, and understand humans better. "
       "We believe XYZ-code will enable us to fulfill our long-term vision: cross-domain transfer learning, "
       "spanning modalities and languages. The goal is to have pretrained models that can jointly learn "
       "representations to support a broad range of downstream AI tasks, much in the way humans do today. "
       "Over the past five years, we have achieved human performance on benchmarks in conversational speech "
       "recognition, machine translation, conversational question answering, machine reading comprehension, "
       "and image captioning. These five breakthroughs provided us with strong signals toward our more ambitious "
       "aspiration to produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
       "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a foundational "
       "component of this aspiration, if grounded with external knowledge sources in the downstream AI tasks."
   ]

   poller = text_analytics_client.begin_extract_summary(document)
   extract_summary_results = poller.result()
   for result in extract_summary_results:
       if result.kind == "ExtractiveSummarization":
           print("Summary extracted: \n{}".format(
               " ".join([sentence.text for sentence in result.sentences]))
           )
       elif result.is_error is True:
           print("...Is an error with code '{}' and message '{}'".format(
               result.error.code, result.error.message
           ))

장기 실행 사용자 지정 다중 레이블 분류 작업을 시작합니다.

사용자 지정 기능의 지역 지원 및 문서를 분류하는 모델을 학습시키는 방법에 대한 자세한 내용은 https://aka.ms/azsdk/textanalytics/customfunctionalities

버전 2022-05-01의 새로운 기능: begin_multi_label_classify 클라이언트 메서드입니다.

begin_multi_label_classify(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], project_name: str, deployment_name: str, *, continuation_token: str | None = None, disable_service_logs: bool | None = None, display_name: str | None = None, language: str | None = None, polling_interval: int | None = None, show_stats: bool | None = None, **kwargs: Any) -> TextAnalysisLROPoller[ItemPaged[ClassifyDocumentResult | DocumentError]]

매개 변수

반환

TextAnalysisLROPoller의 instance. 이 개체에서 result()를 호출하여 및 DocumentError의 ClassifyDocumentResult 다른 유형의 페이저블을 반환합니다.

반환 형식

예외

예제

문서 일괄 처리에서 다중 레이블 분류를 수행합니다.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]
   project_name = os.environ["MULTI_LABEL_CLASSIFY_PROJECT_NAME"]
   deployment_name = os.environ["MULTI_LABEL_CLASSIFY_DEPLOYMENT_NAME"]
   path_to_sample_document = os.path.abspath(
       os.path.join(
           os.path.abspath(__file__),
           "..",
           "./text_samples/custom_classify_sample.txt",
       )
   )

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint,
       credential=AzureKeyCredential(key),
   )

   with open(path_to_sample_document) as fd:
       document = [fd.read()]

   poller = text_analytics_client.begin_multi_label_classify(
       document,
       project_name=project_name,
       deployment_name=deployment_name
   )

   document_results = poller.result()
   for doc, classification_result in zip(document, document_results):
       if classification_result.kind == "CustomDocumentClassification":
           classifications = classification_result.classifications
           print(f"\nThe movie plot '{doc}' was classified as the following genres:\n")
           for classification in classifications:
               print("'{}' with confidence score {}.".format(
                   classification.category, classification.confidence_score
               ))
       elif classification_result.is_error is True:
           print("Movie plot '{}' has an error with code '{}' and message '{}'".format(
               doc, classification_result.error.code, classification_result.error.message
           ))

장기 실행 사용자 지정 명명된 엔터티 인식 작업을 시작합니다.

사용자 지정 기능의 지역 지원 및 사용자 지정 엔터티를 인식하도록 모델을 학습하는 방법에 대한 자세한 내용은 https://aka.ms/azsdk/textanalytics/customentityrecognition

버전 2022-05-01의 새로운 기능: begin_recognize_custom_entities 클라이언트 메서드입니다.

begin_recognize_custom_entities(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], project_name: str, deployment_name: str, *, continuation_token: str | None = None, disable_service_logs: bool | None = None, display_name: str | None = None, language: str | None = None, polling_interval: int | None = None, show_stats: bool | None = None, string_index_type: str | None = None, **kwargs: Any) -> TextAnalysisLROPoller[ItemPaged[RecognizeCustomEntitiesResult | DocumentError]]

매개 변수

반환

TextAnalysisLROPoller의 instance. 이 개체에서 result()를 호출하여 및 DocumentError의 RecognizeCustomEntitiesResult 다른 유형의 페이저블을 반환합니다.

반환 형식

예외

예제

문서 일괄 처리에서 사용자 지정 엔터티를 인식합니다.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]
   project_name = os.environ["CUSTOM_ENTITIES_PROJECT_NAME"]
   deployment_name = os.environ["CUSTOM_ENTITIES_DEPLOYMENT_NAME"]
   path_to_sample_document = os.path.abspath(
       os.path.join(
           os.path.abspath(__file__),
           "..",
           "./text_samples/custom_entities_sample.txt",
       )
   )

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint,
       credential=AzureKeyCredential(key),
   )

   with open(path_to_sample_document) as fd:
       document = [fd.read()]

   poller = text_analytics_client.begin_recognize_custom_entities(
       document,
       project_name=project_name,
       deployment_name=deployment_name
   )

   document_results = poller.result()
   for custom_entities_result in document_results:
       if custom_entities_result.kind == "CustomEntityRecognition":
           for entity in custom_entities_result.entities:
               print(
                   "Entity '{}' has category '{}' with confidence score of '{}'".format(
                       entity.text, entity.category, entity.confidence_score
                   )
               )
       elif custom_entities_result.is_error is True:
           print("...Is an error with code '{}' and message '{}'".format(
               custom_entities_result.error.code, custom_entities_result.error.message
               )
           )

장기 실행 사용자 지정 단일 레이블 분류 작업을 시작합니다.

사용자 지정 기능의 지역 지원 및 문서를 분류하는 모델을 학습시키는 방법에 대한 자세한 내용은 다음을 참조하세요. https://aka.ms/azsdk/textanalytics/customfunctionalities

버전 2022-05-01의 새로운 기능: begin_single_label_classify 클라이언트 메서드입니다.

begin_single_label_classify(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], project_name: str, deployment_name: str, *, continuation_token: str | None = None, disable_service_logs: bool | None = None, display_name: str | None = None, language: str | None = None, polling_interval: int | None = None, show_stats: bool | None = None, **kwargs: Any) -> TextAnalysisLROPoller[ItemPaged[ClassifyDocumentResult | DocumentError]]

매개 변수

반환

TextAnalysisLROPoller의 instance. 이 개체에서 result()를 호출하여 및 DocumentError의 ClassifyDocumentResult 다른 유형의 페이저블을 반환합니다.

반환 형식

예외

예제

문서 일괄 처리에서 단일 레이블 분류를 수행합니다.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]
   project_name = os.environ["SINGLE_LABEL_CLASSIFY_PROJECT_NAME"]
   deployment_name = os.environ["SINGLE_LABEL_CLASSIFY_DEPLOYMENT_NAME"]
   path_to_sample_document = os.path.abspath(
       os.path.join(
           os.path.abspath(__file__),
           "..",
           "./text_samples/custom_classify_sample.txt",
       )
   )

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint,
       credential=AzureKeyCredential(key),
   )

   with open(path_to_sample_document) as fd:
       document = [fd.read()]

   poller = text_analytics_client.begin_single_label_classify(
       document,
       project_name=project_name,
       deployment_name=deployment_name
   )

   document_results = poller.result()
   for doc, classification_result in zip(document, document_results):
       if classification_result.kind == "CustomDocumentClassification":
           classification = classification_result.classifications[0]
           print("The document text '{}' was classified as '{}' with confidence score {}.".format(
               doc, classification.category, classification.confidence_score)
           )
       elif classification_result.is_error is True:
           print("Document text '{}' has an error with code '{}' and message '{}'".format(
               doc, classification_result.error.code, classification_result.error.message
           ))

클라이언트에서 연 소켓을 닫습니다. 클라이언트를 컨텍스트 관리자로 사용할 때는 이 메서드를 호출할 필요가 없습니다.

close() -> None

예외

문서 일괄 처리에 대한 언어를 검색합니다.

검색된 언어와 0과 1 사이의 숫자 점수를 반환합니다. 1에 가까운 점수는 식별된 언어가 true임을 100% 확실하게 나타냅니다. 사용 가능한 언어 목록은 를 참조 https://aka.ms/talangs 하세요.

서비스 데이터 제한은 를 참조 https://aka.ms/azsdk/textanalytics/data-limits 하세요.

버전 v3.1의 새로운 기능: disable_service_logs 키워드(keyword) 인수입니다.

detect_language(documents: List[str] | List[DetectLanguageInput] | List[Dict[str, str]], *, country_hint: str | None = None, disable_service_logs: bool | None = None, model_version: str | None = None, show_stats: bool | None = None, **kwargs: Any) -> List[DetectLanguageResult | DocumentError]

매개 변수

반환

원래 문서가 전달된 순서대로 및 DocumentError 의 DetectLanguageResult 결합된 목록입니다.

반환 형식

예외

예제

문서 일괄 처리에서 언어 검색

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))
   documents = [
       """
       The concierge Paulette was extremely helpful. Sadly when we arrived the elevator was broken, but with Paulette's help we barely noticed this inconvenience.
       She arranged for our baggage to be brought up to our room with no extra charge and gave us a free meal to refurbish all of the calories we lost from
       walking up the stairs :). Can't say enough good things about my experience!
       """,
       """
       最近由于工作压力太大，我们决定去富酒店度假。那儿的温泉实在太舒服了，我跟我丈夫都完全恢复了工作前的青春精神！加油！
       """
   ]

   result = text_analytics_client.detect_language(documents)
   reviewed_docs = [doc for doc in result if not doc.is_error]

   print("Let's see what language each review is in!")

   for idx, doc in enumerate(reviewed_docs):
       print("Review #{} is in '{}', which has ISO639-1 name '{}'\n".format(
           idx, doc.primary_language.name, doc.primary_language.iso6391_name
       ))

문서 일괄 처리에서 핵심 구를 추출합니다.

입력 텍스트의 핵심 구를 나타내는 문자열 목록을 반환합니다. 예를 들어 "음식이 맛있었고 훌륭한 직원이 있었다"는 입력 텍스트의 경우 API는 "음식"과 "훌륭한 직원"이라는 기본 대화 포인트를 반환합니다.

서비스 데이터 제한은 를 참조 https://aka.ms/azsdk/textanalytics/data-limits 하세요.

버전 v3.1의 새로운 기능: disable_service_logs 키워드(keyword) 인수입니다.

extract_key_phrases(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, disable_service_logs: bool | None = None, language: str | None = None, model_version: str | None = None, show_stats: bool | None = None, **kwargs: Any) -> List[ExtractKeyPhrasesResult | DocumentError]

매개 변수

반환

원래 문서가 전달된 순서대로 및 DocumentError 의 ExtractKeyPhrasesResult 결합된 목록입니다.

반환 형식

예외

예제

문서 일괄 처리에서 핵심 구를 추출합니다.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))
   articles = [
       """
       Washington, D.C. Autumn in DC is a uniquely beautiful season. The leaves fall from the trees
       in a city chock-full of forests, leaving yellow leaves on the ground and a clearer view of the
       blue sky above...
       """,
       """
       Redmond, WA. In the past few days, Microsoft has decided to further postpone the start date of
       its United States workers, due to the pandemic that rages with no end in sight...
       """,
       """
       Redmond, WA. Employees at Microsoft can be excited about the new coffee shop that will open on campus
       once workers no longer have to work remotely...
       """
   ]

   result = text_analytics_client.extract_key_phrases(articles)
   for idx, doc in enumerate(result):
       if not doc.is_error:
           print("Key phrases in article #{}: {}".format(
               idx + 1,
               ", ".join(doc.key_phrases)
           ))

문서 일괄 처리에 대한 엔터티를 인식합니다.

텍스트의 엔터티를 사람, 장소, 조직, 날짜/시간, 수량, 백분율, 통화 등으로 식별하고 분류합니다. 지원되는 엔터티 형식 목록은 다음을 검사.https://aka.ms/taner

서비스 데이터 제한은 를 참조 https://aka.ms/azsdk/textanalytics/data-limits 하세요.

버전 v3.1의 새로운 기능: disable_service_logs 및 string_index_type 키워드(keyword) 인수입니다.

recognize_entities(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, disable_service_logs: bool | None = None, language: str | None = None, model_version: str | None = None, show_stats: bool | None = None, string_index_type: str | None = None, **kwargs: Any) -> List[RecognizeEntitiesResult | DocumentError]

매개 변수

반환

원래 문서가 전달된 순서대로 및 DocumentError 의 RecognizeEntitiesResult 결합된 목록입니다.

반환 형식

예외

예제

문서 일괄 처리에서 엔터티를 인식합니다.

   import os
   import typing
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))
   reviews = [
       """I work for Foo Company, and we hired Contoso for our annual founding ceremony. The food
       was amazing and we all can't say enough good words about the quality and the level of service.""",
       """We at the Foo Company re-hired Contoso after all of our past successes with the company.
       Though the food was still great, I feel there has been a quality drop since their last time
       catering for us. Is anyone else running into the same problem?""",
       """Bar Company is over the moon about the service we received from Contoso, the best sliders ever!!!!"""
   ]

   result = text_analytics_client.recognize_entities(reviews)
   result = [review for review in result if not review.is_error]
   organization_to_reviews: typing.Dict[str, typing.List[str]] = {}

   for idx, review in enumerate(result):
       for entity in review.entities:
           print(f"Entity '{entity.text}' has category '{entity.category}'")
           if entity.category == 'Organization':
               organization_to_reviews.setdefault(entity.text, [])
               organization_to_reviews[entity.text].append(reviews[idx])

   for organization, reviews in organization_to_reviews.items():
       print(
           "\n\nOrganization '{}' has left us the following review(s): {}".format(
               organization, "\n\n".join(reviews)
           )
       )

문서 일괄 처리에 대해 잘 알려진 기술 자료 연결된 엔터티를 인식합니다.

텍스트에 있는 각 엔터티의 ID를 식별 및 구분합니다(예: 화성이라는 단어가 행성을 참조하는지 아니면 로마 전쟁의 신을 가리키는지 결정). 인식된 엔터티는 위키백과 같은 잘 알려진 기술 자료 URL과 연결됩니다.

서비스 데이터 제한은 를 참조 https://aka.ms/azsdk/textanalytics/data-limits 하세요.

버전 v3.1의 새로운 기능: disable_service_logs 및 string_index_type 키워드(keyword) 인수입니다.

recognize_linked_entities(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, disable_service_logs: bool | None = None, language: str | None = None, model_version: str | None = None, show_stats: bool | None = None, string_index_type: str | None = None, **kwargs: Any) -> List[RecognizeLinkedEntitiesResult | DocumentError]

매개 변수

반환

원래 문서가 전달된 순서대로 및 DocumentError 의 RecognizeLinkedEntitiesResult 결합된 목록입니다.

반환 형식

예외

예제

문서 일괄 처리에서 연결된 엔터티를 인식합니다.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))
   documents = [
       """
       Microsoft was founded by Bill Gates with some friends he met at Harvard. One of his friends,
       Steve Ballmer, eventually became CEO after Bill Gates as well. Steve Ballmer eventually stepped
       down as CEO of Microsoft, and was succeeded by Satya Nadella.
       Microsoft originally moved its headquarters to Bellevue, Washington in January 1979, but is now
       headquartered in Redmond.
       """
   ]

   result = text_analytics_client.recognize_linked_entities(documents)
   docs = [doc for doc in result if not doc.is_error]

   print(
       "Let's map each entity to it's Wikipedia article. I also want to see how many times each "
       "entity is mentioned in a document\n\n"
   )
   entity_to_url = {}
   for doc in docs:
       for entity in doc.entities:
           print("Entity '{}' has been mentioned '{}' time(s)".format(
               entity.name, len(entity.matches)
           ))
           if entity.data_source == "Wikipedia":
               entity_to_url[entity.name] = entity.url

문서 일괄 처리에 대한 개인 정보가 포함된 엔터티를 인식합니다.

문서의 개인 정보 엔터티("SSN", "은행 계좌" 등)의 목록을 반환합니다. 지원되는 엔터티 형식 목록은 검사 https://aka.ms/azsdk/language/pii

서비스 데이터 제한은 를 참조 https://aka.ms/azsdk/textanalytics/data-limits 하세요.

버전 v3.1의 새로운 기능: recognize_pii_entities 클라이언트 메서드입니다.

recognize_pii_entities(documents: List[str] | List[TextDocumentInput] | List[Dict[str, str]], *, categories_filter: List[str | PiiEntityCategory] | None = None, disable_service_logs: bool | None = None, domain_filter: str | PiiEntityDomain | None = None, language: str | None = None, model_version: str | None = None, show_stats: bool | None = None, string_index_type: str | None = None, **kwargs: Any) -> List[RecognizePiiEntitiesResult | DocumentError]

매개 변수

반환

원래 문서가 전달된 순서대로 및 DocumentError 의 RecognizePiiEntitiesResult 결합된 목록입니다.

반환 형식

예외

예제

문서 일괄 처리에서 개인 식별 가능한 정보 엔터티를 인식합니다.

   import os
   from azure.core.credentials import AzureKeyCredential
   from azure.ai.textanalytics import TextAnalyticsClient

   endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
   key = os.environ["AZURE_LANGUAGE_KEY"]

   text_analytics_client = TextAnalyticsClient(
       endpoint=endpoint, credential=AzureKeyCredential(key)
   )
   documents = [
       """Parker Doe has repaid all of their loans as of 2020-04-25.
       Their SSN is 859-98-0987. To contact them, use their phone number
       555-555-5555. They are originally from Brazil and have Brazilian CPF number 998.214.865-68"""
   ]

   result = text_analytics_client.recognize_pii_entities(documents)
   docs = [doc for doc in result if not doc.is_error]

   print(
       "Let's compare the original document with the documents after redaction. "
       "I also want to comb through all of the entities that got redacted"
   )
   for idx, doc in enumerate(docs):
       print(f"Document text: {documents[idx]}")
       print(f"Redacted document text: {doc.redacted_text}")
       for entity in doc.entities:
           print("...Entity '{}' with category '{}' got redacted".format(
               entity.text, entity.category
           ))