Merge pull request #46 from thediris/patch-3

udpate Python TextAnalytics quickstart to adhere to sdk changes, and reflect that api v3.0 is not preview anymore

Author: aahill

articles/cognitive-services/text-analytics/includes/quickstarts/python-sdk.md

@@ -8,7 +8,7 @@ ms.author: aahi
 
 <a name="HOLTop"></a>
 
-#### [Version 3.0-preview](#tab/version-3)
+#### [Version 3.0](#tab/version-3)
 
 [v3 Reference documentation](https://aka.ms/azsdk-python-textanalytics-ref-docs) | [v3 Library source code](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics) | [v3 Package (PiPy)](https://pypi.org/project/azure-ai-textanalytics/) | [v3 Samples](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples)
 
@@ -32,7 +32,7 @@ ms.author: aahi
 
 After installing Python, you can install the client library with:
 
-#### [Version 3.0-preview](#tab/version-3)
+#### [Version 3.0](#tab/version-3)
 
 ```console
 pip install azure-ai-textanalytics
@@ -66,7 +66,7 @@ endpoint = "<paste-your-text-analytics-endpoint-here>"
 
 ## Object model
 
-#### [Version 3.0-preview](#tab/version-3)
+#### [Version 3.0](#tab/version-3)
 
 The Text Analytics client is a `TextAnalyticsClient` object that authenticates to Azure using your key. The client provides several methods for analyzing text as a batch. 
 
@@ -95,7 +95,7 @@ These code snippets show you how to do the following tasks with the Text Analyti
 
 ## Authenticate the client
 
-#### [Version 3.0-preview](#tab/version-3)
+#### [Version 3.0](#tab/version-3)
 
 Create a function to instantiate the `TextAnalyticsClient` object with your `key` AND `endpoint` created above. Then create a new client. 
 
@@ -124,7 +124,7 @@ Create a function to instantiate the `TextAnalyticsClient` object with your `key
 
 ## Sentiment analysis
 
-#### [Version 3.0-preview](#tab/version-3)
+#### [Version 3.0](#tab/version-3)
 
 Create a new function called `sentiment_analysis_example()` that takes the client as an argument, then calls the `analyze_sentiment()` function. The returned response object will contain the sentiment label and score of the entire input document, as well as a sentiment analysis for each sentence.
 
@@ -141,7 +141,7 @@ def sentiment_analysis_example(client):
         response.confidence_scores.negative,
     ))
     for idx, sentence in enumerate(response.sentences):
-        print("[Length: {}]".format(sentence.grapheme_length))
+        print("Sentence: {}".format(sentence.text))
         print("Sentence {} sentiment: {}".format(idx+1, sentence.sentiment))
         print("Sentence score:\nPositive={0:.2f}\nNeutral={1:.2f}\nNegative={2:.2f}\n".format(
             sentence.confidence_scores.positive,
@@ -158,14 +158,14 @@ sentiment_analysis_example(client)
 Document Sentiment: positive
 Overall scores: positive=1.00; neutral=0.00; negative=0.00 
 
-[Length: 30]
+Sentence: I had the best day of my life.
 Sentence 1 sentiment: positive
 Sentence score:
 Positive=1.00
 Neutral=0.00
 Negative=0.00
 
-[Length: 30]
+Sentence: I wish you were there with me.
 Sentence 2 sentiment: neutral
 Sentence score:
 Positive=0.21
@@ -192,7 +192,7 @@ Document ID: 4 , Sentiment Score: 1.00
 
 ## Language detection
 
-#### [Version 3.0-preview](#tab/version-3)
+#### [Version 3.0](#tab/version-3)
 
 Create a new function called `language_detection_example()` that takes the client as an argument, then calls the `detect_language()` function. The returned response object will contain the detected language in `primary_language` if successful, and an `error` if not.
 
@@ -237,10 +237,10 @@ Document ID: 3 , Language: Chinese_Simplified
 
 ## Named Entity recognition (NER)
 
-#### [Version 3.0-preview](#tab/version-3)
+#### [Version 3.0](#tab/version-3)
 
 > [!NOTE]
-> In version `3.0-preview`: 
+> In version `3.0`: 
 > * Entity linking is a separate request than NER.
 
 Create a new function called `entity_recognition_example` that takes the client as an argument, then calls the `recognize_entities()` function and iterates through the results. The returned response object will contain the list of detected entities in `entity` if successful, and an `error` if not. For each detected entity, print its Category and Sub-Category if exists.
@@ -255,7 +255,7 @@ def entity_recognition_example(client):
         print("Named Entities:\n")
         for entity in result.entities:
             print("\tText: \t", entity.text, "\tCategory: \t", entity.category, "\tSubCategory: \t", entity.subcategory,
-                    "\n\tLength: \t", entity.grapheme_length, "\tConfidence Score: \t", round(entity.confidence_score, 2), "\n")
+                    "\n\tConfidence Score: \t", round(entity.confidence_score, 2), "\n")
 
     except Exception as err:
         print("Encountered exception. {}".format(err))
@@ -267,11 +267,14 @@ entity_recognition_example(client)
 ```console
 Named Entities:
 
-    Text:    Seattle        Category:        Location       SubCategory:     GPE
-    Length:          7      Confidence Score:        0.92
+        Text:    trip   Category:        Event  SubCategory:     None
+        Confidence Score:        0.61
 
-    Text:    last week      Category:        DateTime       SubCategory:     DateRange
-    Length:          9      Confidence Score:        0.8
+        Text:    Seattle        Category:        Location       SubCategory:     GPE
+        Confidence Score:        0.82
+
+        Text:    last week      Category:        DateTime       SubCategory:     DateRange
+        Confidence Score:        0.8
 ```
 
 ## Entity Linking
@@ -296,7 +299,7 @@ def entity_linking_example(client):
             print("\tMatches:")
             for match in entity.matches:
                 print("\t\tText:", match.text)
-                print("\t\tConfidence Score: {0:.2f}".format(match.confidence_score), "\tLength: {}\n".format(match.grapheme_length))
+                print("\t\tConfidence Score: {0:.2f}".format(match.confidence_score))
 
     except Exception as err:
         print("Encountered exception. {}".format(err))
@@ -308,47 +311,40 @@ entity_linking_example(client)
 ```console
 Linked Entities:
 
-    Name:  Altair 8800     Id:  Altair 8800     Url:  https://en.wikipedia.org/wiki/Altair_8800 
-    Data Source:  Wikipedia
-    Matches:
-        Text: Altair 8800
-        Confidence Score: 0.00     Length: 11
-
-    Name:  Bill Gates     Id:  Bill Gates     Url:  https://en.wikipedia.org/wiki/Bill_Gates 
-    Data Source:  Wikipedia
-    Matches:
-        Text: Bill Gates
-        Confidence Score: 0.00     Length: 10
-
-        Text: Gates
-        Confidence Score: 0.00     Length: 5
-
-    Name:  Paul Allen     Id:  Paul Allen     Url:  https://en.wikipedia.org/wiki/Paul_Allen 
-    Data Source:  Wikipedia
-    Matches:
-        Text: Paul Allen
-        Confidence Score: 0.00     Length: 10
-
-    Name:  Microsoft     Id:  Microsoft     Url:  https://en.wikipedia.org/wiki/Microsoft 
-    Data Source:  Wikipedia
-    Matches:
-        Text: Microsoft
-        Confidence Score: 0.00     Length: 9
-
-        Text: Microsoft
-        Confidence Score: 0.00     Length: 9
-
-    Name:  April 4     Id:  April 4     Url:  https://en.wikipedia.org/wiki/April_4 
-    Data Source:  Wikipedia
-    Matches:
-        Text: April 4
-        Confidence Score: 0.00     Length: 7
-
-    Name:  BASIC     Id:  BASIC     Url:  https://en.wikipedia.org/wiki/BASIC 
-    Data Source:  Wikipedia
-    Matches:
-        Text: BASIC
-        Confidence Score: 0.00     Length: 5
+        Name:  Altair 8800      Id:  Altair 8800        Url:  https://en.wikipedia.org/wiki/Altair_8800
+        Data Source:  Wikipedia
+        Matches:
+                Text: Altair 8800
+                Confidence Score: 0.88
+        Name:  Bill Gates       Id:  Bill Gates         Url:  https://en.wikipedia.org/wiki/Bill_Gates
+        Data Source:  Wikipedia
+        Matches:
+                Text: Bill Gates
+                Confidence Score: 0.63
+                Text: Gates
+                Confidence Score: 0.63
+        Name:  Paul Allen       Id:  Paul Allen         Url:  https://en.wikipedia.org/wiki/Paul_Allen
+        Data Source:  Wikipedia
+        Matches:
+                Text: Paul Allen
+                Confidence Score: 0.60
+        Name:  Microsoft        Id:  Microsoft  Url:  https://en.wikipedia.org/wiki/Microsoft
+        Data Source:  Wikipedia
+        Matches:
+                Text: Microsoft
+                Confidence Score: 0.55
+                Text: Microsoft
+                Confidence Score: 0.55
+        Name:  April 4  Id:  April 4    Url:  https://en.wikipedia.org/wiki/April_4
+        Data Source:  Wikipedia
+        Matches:
+                Text: April 4
+                Confidence Score: 0.32
+        Name:  BASIC    Id:  BASIC      Url:  https://en.wikipedia.org/wiki/BASIC
+        Data Source:  Wikipedia
+        Matches:
+                Text: BASIC
+                Confidence Score: 0.33
 ```
 
 #### [Version 2.1](#tab/version-2)
@@ -404,7 +400,7 @@ Document ID: 2
 ## Key phrase extraction
 
 
-#### [Version 3.0-preview](#tab/version-3)
+#### [Version 3.0](#tab/version-3)
 
 Create a new function called `key_phrase_extraction_example()` that takes the client as an argument, then calls the `extract_key_phrases()` function. The result will contain the list of detected key phrases in `key_phrases` if successful, and an `error` if not. Print any detected key phrases.