Automatic commit: Move 'cp-aibus-ber-custom-data', 'cp-aibus-ber-swagger-ui' from QA to Production

Author: Juliana-Morais

tutorials/cp-aibus-ber-custom-data/cp-aibus-ber-custom-data.md

@@ -17,6 +17,8 @@ The core functionality of Business Entity Recognition is to automatically detect
 
 In this tutorial, you will learn how to use the service APIs to create a dataset, upload training data, and submit a training job to finally create your own custom machine learning model to classify any given type of named entity, such as mobile number, first name, last name and address, in the texts you will submit to the service in the next tutorial: [Deploy Model and Get Prediction Results](cp-aibus-ber-custom-deploy).
 
+Check out the tutorial group [Use Pre-Trained Machine Learning Models to Process Unstructured Text](group.cp-aibus-business-entity-detect) to try out the Business Entity Recognition pre-trained machine learning models.
+
 ---
 
 [ACCORDION-BEGIN [Step 1: ](Authorize Swagger UI)]

tutorials/cp-aibus-ber-swagger-ui/cp-aibus-ber-swagger-ui.md

@@ -19,12 +19,14 @@ The core functionality of Business Entity Recognition is to automatically detect
 
 Business Entity Recognition is a customizable machine learning service, but you can also use the following pre-trained machine learning models:
 
+- `sap_address_entity`
 - `sap_email_business_entity`
+- `sap_generic_entities`
 - `sap_invoice_header`
 
-See [Extracted Entities by Pre-trained Model](https://help.sap.com/viewer/b43f8f61368d455793a241d2b10baeb2/SHIP/en-US/7eb1408001564d679adcd3bc4796800f.html)
+See [Extracted Entities by Pre-trained Model](https://help.sap.com/docs/Business_Entity_Recognition/b43f8f61368d455793a241d2b10baeb2/7eb1408001564d679adcd3bc4796800f.html) and [Free Service Plan and Trial Account Technical Constraints](https://help.sap.com/docs/Business_Entity_Recognition/b43f8f61368d455793a241d2b10baeb2/c80a45cc7416409eb9bf64667487c375.html).
 
-This tutorial gives you an introduction to the service using a trial account and the available pre-trained machine learning models. If you want to try out the Business Entity Recognition training endpoints to create your own model, go to the next tutorial [Detect and Highlight Entities from Unstructured Text with Custom Models](cp-aibus-ber-swagger-ui-custom).  
+This tutorial gives you an introduction to the service using a trial account and the available pre-trained machine learning models. If you want to try out the Business Entity Recognition training endpoints to create your own model, check out the tutorial group [Use Custom Machine Learning Models to Process Unstructured Text](group.cp-aibus-business-entity-custom).  
 
 ---
 
@@ -40,73 +42,143 @@ In the service key you created for Business Entity Recognition in the previous t
 
 2. To be able to use the Swagger UI endpoints, you need to authorize yourself. In the top right corner, click **Authorize**.
 
-    !![BER](png-files/swagger0.png)
+    !![BER](png-files/swagger.png)
 
 3. Get the `access_token` value created in the previous tutorial: [Get OAuth Access Token for Business Entity Recognition Using Any Web Browser](cp-aibus-ber-web-oauth-token), then add **bearer** in front of it, and enter in the **Value** field.
 
     ```
     bearer <access_token>
     ```
 
-    !![BER](png-files/Authorize.png)
+    !![BER](png-files/authorize.png)
 
 4. Click **Authorize** and then click **Close**.
 
-    !![BER](png-files/Authorize2.png)
+    !![BER](png-files/close.png)
 
 [VALIDATE_1]
 [ACCORDION-END]
 
 
-[ACCORDION-BEGIN [Step 2: ](Post inference result with sap_email_business_entity)]
+[ACCORDION-BEGIN [Step 2: ](Enter inference text for sap_address_entity)]
 
 Use the **POST /inference/jobs** endpoint to submit the text to be extracted and choose the pre-trained machine learning model you want to use to extract entities from the text.
 
 1. Click the endpoint name to expand it.
 
 2. Click **Try it out**.
 
-    !![BER](png-files/post-1.png)
+    !![BER](png-files/post-try.png)
+
+3. In **payload**, enter the `text` you want to extract named entities from, `modelName`, and `modelVersion`. In this `sap_address_entity` pre-trained model example, you can use the following:
+
+    ```JSON
+    {
+      "text":"Warehouse 02 (UK) Ltd c/o Excel Logistics Waindyke Way WF6 1TF. West Yorkshire United Kingdom.",
+      "modelName":"sap_address_entity",
+      "modelVersion":2
+    }
+    ```
+
+4. Click **Execute**.
+
+    !![BER](png-files/post-address-payload.png)
+
+5. Copy the **`id`** from the **Response body** to see the result of the extraction in the next step.
+
+    !![BER](png-files/post-address-id.png)
+
+[DONE]
+[ACCORDION-END]
+
+
+[ACCORDION-BEGIN [Step 3: ](Get inference response from sap_address_entity)]
+
+Use the **GET /inference/jobs/{`jobId`}** endpoint to see the text extraction results and the confidence level of the `sap_address_entity` model.
+
+1. Click the endpoint name to expand it.
+
+2. Click **Try it out**.
+
+    !![BER](png-files/get-try.png)
+
+3. Enter the `jobId` obtained in the previous step and click **Execute**.
+
+    !![BER](png-files/get-address-id.png)
+
+You should receive a response like the following:
+
+!![BER](png-files/get-address-response.png)
+
+In the response, you will find the prediction for the extracted entities. The prediction is made with a probability indicated by the **confidence** field which represents how certain the model is about the value provided. A confidence of 1 means that the model is 100% sure about its prediction.
+
+You have now successfully used the `sap_address_entity` model to get text entity predictions for the unstructured text you submitted to Business Entity Recognition.
+
+You can also use the following **payload** example with the `sap_address_entity` model:
+
+```JSON
+{
+   "text":"STAG GmbH Beriiner Chaussee 29, 39307 Genthin Logistik / Einkauf Tel.: 039 33/821 - 223 Fax: 039 33/ 821 - 259.",
+   "modelName":"sap_address_entity",
+   "modelVersion":2
+}
+```
+
+To try out the `sap_address_entity` model with your own unstructured text, keep in mind the model scope and supported languages, as detailed in [`sap_address_entity`](https://help.sap.com/docs/Business_Entity_Recognition/b43f8f61368d455793a241d2b10baeb2/55ab2ca0f9064aefbc1425246a1b3d1f.html) and [Supported Languages](https://help.sap.com/docs/Business_Entity_Recognition/b43f8f61368d455793a241d2b10baeb2/e710add9d4d743958a3d21b82b3f220d.html).
+
+[DONE]
+[ACCORDION-END]
+
+
+[ACCORDION-BEGIN [Step 4: ](Enter inference text for sap_email_business_entity)]
+
+Use the **POST /inference/jobs** endpoint to submit the text to be extracted and choose the pre-trained machine learning model you want to use to extract entities from the text.
+
+1. Click the endpoint name to expand it.
+
+2. Click **Try it out**.
+
+    !![BER](png-files/post-try.png)
 
 3. In **payload**, enter the `text` you want to extract named entities from, `modelName`, and `modelVersion`. In this `sap_email_business_entity` pre-trained model example, you can use the following:
 
     ```JSON
     {
       "text":"11577210 - FAGOR EDERLAN MEXICO § Dear colleagues, there any way to identify which procurement division has transmitted 1339846 this payment to ABC Ltd. as there is no identification number. Are you able to help? Thank you: Doe, Jane <[email protected]>: 15 May 2019 15:25 To: Doe, John <[email protected]>Cc: Smith, John <[email protected]>: /432216153 328,439.24 Thanks and best Jane. Doe, Jane. Accountant, Procurement Department, Doe Company, S. L. P.Phone+52 444 880 2300. 1114 Doe Company<doe-company.com>",
       "modelName":"sap_email_business_entity",
-      "modelVersion":1
+      "modelVersion":2
     }
     ```
 
 4. Click **Execute**.
 
-    !![BER](png-files/post-2.png)
+    !![BER](png-files/post-email-payload.png)
 
 5. Copy the **`id`** from the **Response body** to see the result of the extraction in the next step.
 
-    !![BER](png-files/post-3.png)
+    !![BER](png-files/post-email-id.png)
 
 [DONE]
 [ACCORDION-END]
 
 
-[ACCORDION-BEGIN [Step 3: ](Get inference result with sap_email_business_entity)]
+[ACCORDION-BEGIN [Step 5: ](Get inference response from sap_email_business_entity)]
 
 Use the **GET /inference/jobs/{`jobId`}** endpoint to see the text extraction results and the confidence level of the `sap_email_business_entity` model.
 
 1. Click the endpoint name to expand it.
 
 2. Click **Try it out**.
 
-    !![BER](png-files/get-1.png)
+    !![BER](png-files/get-try.png)
 
 3. Enter the `jobId` obtained in the previous step and click **Execute**.
 
-    !![BER](png-files/get-2.png)
+    !![BER](png-files/get-email-id.png)
 
 You should receive a response like the following:
 
-!![BER](png-files/get-3.png)
+!![BER](png-files/get-email-response.png)
 
 In the response, you will find the prediction for the extracted entities. The prediction is made with a probability indicated by the **confidence** field which represents how certain the model is about the value provided. A confidence of 1 means that the model is 100% sure about its prediction.
 
@@ -118,25 +190,95 @@ You can also use the following **payload** example with the `sap_email_business_
 {
    "text":"Von: lena nova <[email protected]> Gesendet: Montag, 21. Januar 2019, 19:13 Uhr An: canu, ana <[email protected]>; [email protected] Betreff: RE: M & B EOOD 2639710315 Sehr geehrter Kunde, ich sende Ihnen Informationen für unsere Zahlung. Proforma 198305906 ist ein Teil der Rechnung 4830476570 / 11.01.2019. Rechnungsdatum Betrag in EUR 4830473772 14.12.2018 9,28 € 4830474632 21.12.2018 29,16 € 4830475244 28.12.2018 46,19 € 4830475919 4.1.2019 9,28 € 198305906 10.1.2019 9,29 € 103,20 € 4830476570 11.1.2019 37,13 198305906 10.1.2019 -9,29 € 27,84 Einen schönen Tag noch! Mit freundlichen Grüßen, lena lena nova Verkaufsleiterin M & B EOOD-Computer Center 11, Angel Kanchev Str; 1000, Sofia, Bulgarien Tel.: + 359 2 981 58 57; Mobil: + 359 889 61 13 21 E-Mail: [email protected] Besuchen Sie uns unter: www.computercenter.bg",
    "modelName":"sap_email_business_entity",
+   "modelVersion":2
+}
+```
+
+To try out the `sap_email_business_entity` model with your own unstructured text, keep in mind the model scope and supported languages, as detailed in [`sap_email_business_entity`](https://help.sap.com/docs/Business_Entity_Recognition/b43f8f61368d455793a241d2b10baeb2/8f940cca185144b8baff1fdf31ce2c82.html) and [Supported Languages](https://help.sap.com/docs/Business_Entity_Recognition/b43f8f61368d455793a241d2b10baeb2/e710add9d4d743958a3d21b82b3f220d.html).
+
+[DONE]
+[ACCORDION-END]
+
+
+[ACCORDION-BEGIN [Step 6: ](Enter inference text for sap_generic_entities)]
+
+Use the **POST /inference/jobs** endpoint to submit the text to be extracted and choose the pre-trained machine learning model you want to use to extract entities from the text.
+
+1. Click the endpoint name to expand it.
+
+2. Click **Try it out**.
+
+    !![BER](png-files/post-try.png)
+
+3. In **payload**, enter the `text` you want to extract named entities from, `modelName`, and `modelVersion`. In this `sap_generic_entities` pre-trained model example, you can use the following:
+
+    ```JSON
+    {
+      "text":"In 1988, 31.6% of blacks lived in poverty, compared with 10.1% for whites and 26.8% for Hispanics.",
+      "modelName":"sap_generic_entities",
+      "modelVersion":1
+    }
+    ```
+
+4. Click **Execute**.
+
+    !![BER](png-files/post-generic-payload.png)
+
+5. Copy the **`id`** from the **Response body** to see the result of the extraction in the next step.
+
+    !![BER](png-files/post-generic-id.png)
+
+[DONE]
+[ACCORDION-END]
+
+
+[ACCORDION-BEGIN [Step 7: ](Get inference response from sap_generic_entities)]
+
+Use the **GET /inference/jobs/{`jobId`}** endpoint to see the text extraction results and the confidence level of the `sap_generic_entities` model.
+
+1. Click the endpoint name to expand it.
+
+2. Click **Try it out**.
+
+    !![BER](png-files/get-try.png)
+
+3. Enter the `jobId` obtained in the previous step and click **Execute**.
+
+    !![BER](png-files/get-generic-id.png)
+
+You should receive a response like the following:
+
+!![BER](png-files/get-generic-response.png)
+
+In the response, you will find the prediction for the extracted entities. The prediction is made with a probability indicated by the **confidence** field which represents how certain the model is about the value provided. A confidence of 1 means that the model is 100% sure about its prediction.
+
+You have now successfully used the `sap_generic_entities` model to get text entity predictions for the unstructured text you submitted to Business Entity Recognition.
+
+You can also use the following **payload** example with the `sap_generic_entities` model:
+
+```JSON
+{
+   "text":"Sie liegt sechs Kilometer nordöstlich des Stadtzentrums von Pardubice und gehört zum Okres Pardubice.",
+   "modelName":"sap_generic_entities",
    "modelVersion":1
 }
 ```
 
-To try out the `sap_email_business_entity` model with your own unstructured text, keep in mind the model scope and supported languages, as detailed in [Extracted Entities by Pre-trained Model](https://help.sap.com/viewer/b43f8f61368d455793a241d2b10baeb2/SHIP/en-US/7eb1408001564d679adcd3bc4796800f.html).
+To try out the `sap_generic_entities` model with your own unstructured text, keep in mind the model scope and supported languages, as detailed in [`sap_generic_entities`](https://help.sap.com/docs/Business_Entity_Recognition/b43f8f61368d455793a241d2b10baeb2/d1cc656813fa4737a5df3aa131bed338.html) and [Supported Languages](https://help.sap.com/docs/Business_Entity_Recognition/b43f8f61368d455793a241d2b10baeb2/e710add9d4d743958a3d21b82b3f220d.html).
 
 [DONE]
 [ACCORDION-END]
 
 
-[ACCORDION-BEGIN [Step 4: ](Post inference result with sap_invoice_header)]
+[ACCORDION-BEGIN [Step 8: ](Enter inference text for sap_invoice_header)]
 
 Use the **POST /inference/jobs** endpoint to submit the text to be extracted and choose the pre-trained machine learning model you want to use to extract entities from the text.
 
 1. Click the endpoint name to expand it.
 
 2. Click **Try it out**.
 
-    !![BER](png-files/post-1.png)
+    !![BER](png-files/post-try.png)
 
 3. In **payload**, enter the `text` you want to extract named entities from, `modelName`, and `modelVersion`. In this `sap_invoice_header` pre-trained model example, you can use the following:
 
@@ -150,33 +292,33 @@ Use the **POST /inference/jobs** endpoint to submit the text to be extracted and
 
 4. Click **Execute**.
 
-    !![BER](png-files/post-22.png)
+    !![BER](png-files/post-invoice-payload.png)
 
 5. Copy the **`id`** from the **Response body** to see the result of the extraction in the next step.
 
-    !![BER](png-files/post-33.png)
+    !![BER](png-files/post-invoice-id.png)
 
 [DONE]
 [ACCORDION-END]
 
 
-[ACCORDION-BEGIN [Step 5: ](Get inference result with sap_invoice_header)]
+[ACCORDION-BEGIN [Step 9: ](Get inference response from sap_invoice_header)]
 
 Use the **GET /inference/jobs/{`jobId`}** endpoint to see the text extraction results and the confidence level of the `sap_invoice_header` model.
 
 1. Click the endpoint name to expand it.
 
 2. Click **Try it out**.
 
-    !![BER](png-files/get-1.png)
+    !![BER](png-files/get-try.png)
 
 3. Enter the `jobId` obtained in the previous step and click **Execute**.
 
-    !![BER](png-files/get-22.png)
+    !![BER](png-files/get-invoice-id.png)
 
 You should receive a response like the following:
 
-!![BER](png-files/get-33.png)
+!![BER](png-files/get-invoice-response.png)
 
 You have now successfully used the `sap_invoice_header` model to get text entity predictions for the unstructured text you submitted to Business Entity Recognition.
 
@@ -190,7 +332,7 @@ You can also use the following **payload** example with the `sap_invoice_header`
 }
 ```
 
-To try out the `sap_invoice_header` model with your own unstructured text, keep in mind the model scope and supported languages, as detailed in [Extracted Entities by Pre-trained Model](https://help.sap.com/viewer/b43f8f61368d455793a241d2b10baeb2/SHIP/en-US/7eb1408001564d679adcd3bc4796800f.html).
+To try out the `sap_invoice_header` model with your own unstructured text, keep in mind the model scope and supported languages, as detailed in [`sap_invoice_header`](https://help.sap.com/docs/Business_Entity_Recognition/b43f8f61368d455793a241d2b10baeb2/cb6a6a5b1ad94096a6e1060460d9fcd8.html) and [Supported Languages](https://help.sap.com/docs/Business_Entity_Recognition/b43f8f61368d455793a241d2b10baeb2/e710add9d4d743958a3d21b82b3f220d.html).
 
 Congratulations, you have completed this tutorial.