TextAnalyticsClient Sınıf

Bir grup belge için yaklaşımı analiz edin. show_opinion_mining ile fikir madenciliği özelliğini açın.

Belge ve içindeki her cümle için yaklaşım tahminlerinin yanı sıra her yaklaşım sınıfı için yaklaşım puanlarını (Pozitif, Negatif ve Nötr) döndürür.

Hizmet veri sınırları için bkz https://aka.ms/azsdk/textanalytics/data-limits .

v3.1 sürümündeki yenilikler: show_opinion_mining, disable_service_logs ve string_index_type anahtar sözcük bağımsız değişkenleri.

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]

Parametreler

Döndürülenler

özgün belgelerin geçiriliş sırasına göre ve DocumentError birleşik listesiAnalyzeSentimentResult.

Dönüş türü

Özel durumlar

Örnekler

Bir grup belgeyle yaklaşımı analiz edin.

   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}")

Uzun süre çalışan bir soyut özetleme işlemi başlatın.

Soyut özetlemenin kavramsal bir tartışması için hizmet belgelerine bakın: https://video2.skills-academy.com/azure/cognitive-services/language-service/summarization/overview

Sürüm 2023-04-01'de yeni: begin_abstract_summary istemci yöntemi.

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]]

Parametreler

Döndürülenler

TextAnalysisLROPoller örneği. ve DocumentErrorheterojen bir sayfalanabilir değeri döndürmek için bu nesnede result() öğesini çağırınAbstractiveSummaryResult.

Dönüş türü

Özel durumlar

Örnekler

Bir grup belge üzerinde soyut özetleme gerçekleştirin.

   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
           ))

Bir dizi belge üzerinde çeşitli metin çözümleme eylemleri gerçekleştirmek için uzun süre çalışan bir işlem başlatın.

Daha büyük belgeleri analiz etmek ve / veya birden çok metin çözümleme eylemini tek bir çağrıda birleştirmek istiyorsanız bu işlevi kullanmanızı öneririz. Aksi takdirde, eyleme özgü uç noktaları kullanmanızı öneririz, örneğin analyze_sentiment.

Sürüm v3.1'de yeni: begin_analyze_actions istemci yöntemi.

Sürüm 2022-05-01'de yeni: RecognizeCustomEntitiesAction, SingleLabelClassifyAction, MultiLabelClassifyAction ve AnalyzeHealthcareEntitiesAction giriş seçenekleri ve ilgili RecognizeCustomEntitiesResult, ClassifyDocumentResult ve AnalyzeHealthcareEntitiesResult sonuç nesneleri

Sürüm 2023-04-01'de yeni: ExtractiveSummaryAction ve AbstractiveSummaryAction giriş seçenekleri ve karşılık gelen ExtractiveSummaryResult ve AbstractiveSummaryResult sonuç nesneleri.

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]]]

Parametreler

Döndürülenler

TextAnalysisLROPoller örneği. Listelerden oluşan sayfalanabilir heterojen bir liste döndürmek için poller nesnesinde result() öğesini çağırın. Bu liste listesi önce girdiğiniz belgelere göre sıralanır, sonra da girdiğiniz eylemlere göre sıralanır. Örneğin, ["Hello", "world"] ve eylemleri RecognizeEntitiesAction ve AnalyzeSentimentActionbelgeleriniz varsa, liste listesinde yineleme yaparken önce "Hello" belgesinin RecognizeEntitiesResult eylem sonuçlarını yineler ve ardından AnalyzeSentimentResult "Hello" ifadesini alırsınız. O zaman "dünya"nın ve'ini RecognizeEntitiesResultAnalyzeSentimentResult elde edersiniz.

Dönüş türü

Özel durumlar

Örnekler

Bir dizi belge üzerinde çeşitli metin çözümleme eylemleri gerçekleştirmek için uzun süre çalışan bir işlem başlatın.

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

Bir grup belge içinde sağlık varlıklarını analiz edin ve bu varlıklar arasındaki ilişkileri belirleyin.

Varlıklar UMLS, CHV, MSH gibi mevcut bilgi bankalarında bulunabilen başvurularla ilişkilendirilir.

Varlıklar arasında bulunan ilişkileri de ayıklıyoruz, örneğin "Konu 100 mg ibuprofen aldı", "100 mg" dozajı ile "ibuprofen" ilacı arasındaki ilişkiyi ayıklarız.

Sürüm v3.1'de yeni: begin_analyze_healthcare_entities istemci yöntemi.

Sürüm 2022-05-01'de yeni: display_name anahtar sözcük bağımsız değişkeni.

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]]

Parametreler

Döndürülenler

AnalyzeHealthcareEntitiesLROPoller örneği. ve DocumentErrorheterojen bir disk belleği döndürebilmek için bu nesnede result() öğesini çağırınAnalyzeHealthcareEntitiesResult.

Dönüş türü

Özel durumlar

Örnekler

Bir grup belge içinde sağlık varlıklarını tanıma.

   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
   ]

Uzun süre çalışan bir ayıklayıcı özetleme işlemi başlatın.

Ayıklayıcı özetlemenin kavramsal bir tartışması için hizmet belgelerine bakın: https://video2.skills-academy.com/azure/cognitive-services/language-service/summarization/overview

Sürüm 2023-04-01'de yeni: begin_extract_summary istemci yöntemi.

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]]

Parametreler

Döndürülenler

TextAnalysisLROPoller örneği. ve DocumentErrorheterojen bir disk belleği döndürebilmek için bu nesnede result() öğesini çağırınExtractiveSummaryResult.

Dönüş türü

Özel durumlar

Örnekler

Bir grup belge üzerinde ayıklayıcı özetleme gerçekleştirin.

   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
           ))

Uzun süre çalışan özel bir çoklu etiket sınıflandırma işlemi başlatın.

Özel özelliklerin bölgesel desteği ve belgelerinizi sınıflandırmak için model eğitme hakkında bilgi için bkz. https://aka.ms/azsdk/textanalytics/customfunctionalities

Sürüm 2022-05-01'de yeni: begin_multi_label_classify istemci yöntemi.

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]]

Parametreler

Döndürülenler

TextAnalysisLROPoller örneği. ve DocumentErrorheterojen bir disk belleği döndürebilmek için bu nesnede result() öğesini çağırınClassifyDocumentResult.

Dönüş türü

Özel durumlar

Örnekler

Bir grup belge üzerinde çok etiketli sınıflandırma gerçekleştirin.

   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
           ))

Uzun süre çalışan özel adlandırılmış varlık tanıma işlemi başlatın.

Özel özelliklerin bölgesel desteği ve modeli özel varlıkları tanıyacak şekilde eğitmek için bkz. https://aka.ms/azsdk/textanalytics/customentityrecognition

Sürüm 2022-05-01'de yeni: begin_recognize_custom_entities istemci yöntemi.

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]]

Parametreler

Döndürülenler

TextAnalysisLROPoller örneği. ve DocumentErrorheterojen bir sayfalanabilir değeri döndürmek için bu nesnede result() öğesini çağırınRecognizeCustomEntitiesResult.

Dönüş türü

Özel durumlar

Örnekler

Bir grup belgedeki özel varlıkları tanıma.

   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
               )
           )

Uzun süre çalışan özel bir tek etiket sınıflandırma işlemi başlatın.

Özel özelliklerin bölgesel desteği ve belgelerinizi sınıflandırmak için modeli eğitmek için bkz. https://aka.ms/azsdk/textanalytics/customfunctionalities

Sürüm 2022-05-01'de yeni: begin_single_label_classify istemci yöntemi.

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]]

Parametreler

Döndürülenler

TextAnalysisLROPoller örneği. ve DocumentErrorheterojen bir sayfalanabilir değeri döndürmek için bu nesnede result() öğesini çağırınClassifyDocumentResult.

Dönüş türü

Özel durumlar

Örnekler

Bir grup belge üzerinde tek etiketli sınıflandırma gerçekleştirin.

   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
           ))

İstemci tarafından açılan yuvaları kapatın. İstemciyi bağlam yöneticisi olarak kullanırken bu yöntemi çağırmak gereksizdir.

close() -> None

Özel durumlar

Bir grup belge için dili algılama.

Algılanan dili ve sıfır ile bir arasında sayısal bir puan döndürür. Bire yakın puanlar, tanımlanan dilin doğru olduğundan %100 emin olduğunu gösterir. Etkin dillerin listesi için bkz https://aka.ms/talangs .

Hizmet veri sınırları için bkz https://aka.ms/azsdk/textanalytics/data-limits .

Sürüm v3.1'de yeni: disable_service_logs anahtar sözcük bağımsız değişkeni.

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]

Parametreler

Döndürülenler

özgün belgelerin geçiriliş sırasına göre ve DocumentError birleşik listesiDetectLanguageResult.

Dönüş türü

Özel durumlar

Örnekler

Bir grup belgedeki dili algılama.

   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
       ))

Bir grup belgeden anahtar ifadeleri ayıklayın.

Giriş metnindeki anahtar ifadeleri belirten dizelerin listesini döndürür. Örneğin, "Yemek lezzetliydi ve harika personel vardı" giriş metni için API ana konuşma noktalarını döndürür: "yemek" ve "harika personel"

Hizmet veri sınırları için bkz https://aka.ms/azsdk/textanalytics/data-limits .

Sürüm v3.1'de yeni: disable_service_logs anahtar sözcük bağımsız değişkeni.

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]

Parametreler

Döndürülenler

özgün belgelerin geçiriliş sırasına göre ve DocumentError birleşik listesiExtractKeyPhrasesResult.

Dönüş türü

Özel durumlar

Örnekler

Bir grup belgedeki anahtar ifadeleri ayıklayın.

   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)
           ))

Bir grup belge için varlıkları tanıma.

Metninizdeki varlıkları kişiler, yerler, kuruluşlar, tarih/saat, miktarlar, yüzdeler, para birimleri ve daha fazlası olarak tanımlar ve kategorilere ayırır. Desteklenen varlık türlerinin listesi için şunları denetleyin: https://aka.ms/taner

Hizmet veri sınırları için bkz https://aka.ms/azsdk/textanalytics/data-limits .

v3.1 sürümündeki yenilikler: disable_service_logs ve string_index_type anahtar sözcük bağımsız değişkenleri.

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]

Parametreler

Döndürülenler

özgün belgelerin geçiriliş sırasına göre ve DocumentError birleşik listesiRecognizeEntitiesResult.

Dönüş türü

Özel durumlar

Örnekler

Bir grup belgedeki varlıkları tanıma.

   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)
           )
       )

Bir grup belge için iyi bilinen bir bilgi bankası bağlı varlıkları tanıyın.

Metinde bulunan her varlığın kimliğini tanımlar ve kesinliklerini belirler (örneğin, Mars sözcüğünün bir oluşumunun gezegene mi yoksa Roma savaş tanrısına mı başvurduğuna karar verme). Tanınan varlıklar Wikipedia gibi tanınmış bir bilgi bankası URL'leriyle ilişkilendirilir.

Hizmet veri sınırları için bkz https://aka.ms/azsdk/textanalytics/data-limits .

v3.1 sürümündeki yenilikler: disable_service_logs ve string_index_type anahtar sözcük bağımsız değişkenleri.

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]

Parametreler

Döndürülenler

özgün belgelerin geçiriliş sırasına göre ve DocumentError birleşik listesiRecognizeLinkedEntitiesResult.

Dönüş türü

Özel durumlar

Örnekler

Bir grup belgedeki bağlantılı varlıkları tanıma.

   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

Bir grup belge için kişisel bilgiler içeren varlıkları tanıma.

Belgedeki kişisel bilgi varlıklarının ("SSN", "Banka Hesabı" vb.) listesini döndürür. Desteklenen varlık türlerinin listesi için https://aka.ms/azsdk/language/pii

Hizmet veri sınırları için bkz https://aka.ms/azsdk/textanalytics/data-limits .

Sürüm v3.1'de yeni: recognize_pii_entities istemci yöntemi.

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]

Parametreler

Döndürülenler

özgün belgelerin geçiriliş sırasına göre ve DocumentError birleşik listesiRecognizePiiEntitiesResult.

Dönüş türü

Özel durumlar

Örnekler

Bir grup belgedeki kişisel bilgi varlıklarını tanıma.

   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
           ))