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Create Dataset and Upload Training Data

Perform the initial steps to train your own Business Entity Recognition custom model to get machine learning entity predictions for the text you submit to the service.
You will learn
  • How to call and test Business Entity Recognition
  • How to access and use the Business Entity Recognition Swagger UI (User Interface)
  • How to create a dataset, upload training data and submit a training job
Juliana-MoraisJuliana MoraisJune 29, 2022
Created by
Juliana-Morais
December 14, 2020
Contributors
Juliana-Morais
tobias61
thecodester

The core functionality of Business Entity Recognition is to automatically detect and highlight any given type of named entity in unstructured text and classify it in accordance with predefined categories.

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.

Check out the tutorial group Use Pre-Trained Machine Learning Models to Process Unstructured Text to try out the Business Entity Recognition pre-trained machine learning models.

  • Step 1

    You will use Swagger UI, via any web browser, to call the Business Entity Recognition service APIs. Swagger UI allows developers to effortlessly interact and try out every single operation an API exposes for easy consumption. For more information, see Swagger UI.

    In the service key you created for Business Entity Recognition in the previous tutorial: Create Service Instance for Business Entity Recognition, you should find (outside the uaa section of the service key) an entry called url (as highlighted in the image below).

    1. To access the Business Entity Recognition Swagger UI, add /api/v1 to the url value, paste it into any web browser and press Enter.

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    2. To be able to use the Swagger UI endpoints, you need to authorize yourself. In the top right corner, click Authorize.

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    3. Get the access_token value created in the previous tutorial: Get OAuth Access Token for Business Entity Recognition Using Any Web Browser, then add bearer in front of it, and enter in the Value field.

      bearer <access_token>
      
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    4. Click Authorize and then click Close.

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  • Step 2

    Use the POST /datasets endpoint to create a dataset that will be used to train a custom model to extract entities from unstructured text.

    1. Click the endpoint name to expand it.

    2. Click Try it out.

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    3. In payload, enter a description for your dataset, "Tutorial dataset", for example.

    4. Click Execute.

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    5. Copy the datasetId from the Response body.

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  • Step 3

    To see the details of your newly created dataset, use the GET /datasets/{datasetId} endpoint.

    1. Click the endpoint name to expand it.

    2. Click Try it out.

    3. Enter the datasetId obtained in the previous step and click Execute.

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      You should receive a response like below. Among the datasetId and the description, you see the documentCount. The number of documents include the training data files that you’ll upload in the next step.

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  • Step 4

    To upload training documents to your dataset, use the POST /datasets/{datasetId}/documents. You may repeat this step to upload multiple documents.

    Please bear in mind that Business Entity Recognition requires your data to be in a specific format to train a model. See data format prerequisites in Training Data Format.

    As an alternative to uploading your own JSON file to the service, you can use the following sample files (right click on the link, then click Save link as to download the files locally):

    Please repeat this step twice and upload one document each time. The more data is available the better predictions the model can give you.

    To upload documents, do the following:

    1. Click the POST /datasets/{datasetId}/documents endpoint name to expand it.

    2. Click Try it out.

    3. Enter the datasetId obtained in step 2 and select one training document for the upload.

    4. Click Execute.

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    5. Copy the documentId from the Response body.

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  • Step 5

    Using the datasetId obtained in the previous step, you can obtain the details of your training documents using the GET /datasets/{datasetId}/documents/{documentId} endpoint.

    1. Click the endpoint name to expand it.

    2. Click Try it out.

    3. Enter the datasetId obtained in step 2.

    4. Enter the documentId obtained in step 4.

    5. Click Execute.

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      You should receive a response, with the document details, like below. This shows you the size of the document in bytes.

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  • Step 6

    Once the training documents are uploaded, you can submit a training job. This triggers the training of a machine learning model based on your uploaded data. For that, use the POST /training/jobs endpoint.

    1. Click the endpoint name to expand it.

    2. Click Try it out.

    3. In payload, enter a modelName for your custom model, "Tutorial_custom_model", for example, and the datasetId obtained in step 2.

      CAUTION:

      Be aware of the following modelName guidelines:​

      • You cannot use the same name as one of the Business Entity Recognition pre-trained models (see Extracted Entities by Pre-trained Model)

      • You cannot use "sap_" as a prefix

      • Start with an alphanumeric character

      • You can include the special characters "-" and "_"

      • You can use a maximum of 64 characters

    4. Click Execute.

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    5. Copy the jobId from the Response body. This allows you to check the status of the training.

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      This indicates that your training job has been successfully submitted.

    Choose the incorrect modelName.

  • Step 7

    To check whether your training already succeeded, you can use the GET /training/jobs/{jobId} endpoint to see the current status of the job.

    CAUTION:

    In the free tier version of the service, only one training job with SUCCEEDED status is allowed. See Free Tier Option Technical Constraints. Use the endpoint DELETE /training/jobs/{jobId} to delete a training job before its status changes to SUCCEEDED.

    1. Click the endpoint name to expand it.

    2. Click Try it out.

    3. Enter the jobId obtained in step 6.

    4. Click Execute.

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      You should receive a response like below. The status RUNNING indicates that the training is still in progress. In case the status is PENDING, then the training has not started yet.

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      You may check the status now and then. Please note that the training may take up to 5 hours. Afterwards, the training status changes to SUCCEEDED. Along with that, you receive all the capabilities of the model, that are the entities the model can recognize.

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