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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](cp-aibus-ber-web-oauth-token), then add **bearer** in front of it, and enter in the **Value** field.
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```
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bearer <access_token>
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```
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4. Click **Authorize** and then click **Close**.
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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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>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):
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>- [Sample Training Data 1](https://raw.githubusercontent.com/SAPDocuments/Tutorials/master/tutorials/cp-aibus-ber-custom-data/data/Tutorial_training_data_1.json)
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>- [Sample Training Data 1](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-ber-custom-data/Tutorial_training_data_1.json)
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>- [Sample Training Data 2](https://raw.githubusercontent.com/SAPDocuments/Tutorials/master/tutorials/cp-aibus-ber-custom-data/data/Tutorial_training_data_2.json)
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>- [Sample Training Data 2](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-ber-custom-data/Tutorial_training_data_2.json)
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>Please repeat this step twice and upload one document each time. The more data is available the better predictions the model can give you.
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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.
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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@@ -37,15 +37,15 @@ You'll use Swagger UI, via any web browser, to call the Data Attribute Recommend
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In the service key you created for Data Attribute Recommendation in the previous tutorial: [Use Free Tier to Set Up Account for Data Attribute Recommendation and Get Service Key](cp-aibus-dar-booster-free-key) or [Use Trial to Set Up Account for Data Attribute Recommendation and Get Service Key](cp-aibus-dar-booster-key), you find a section called `swagger` (as highlighted in the image below) with three entries, called `dm` (data manager), `mm` (model manager) and `inference`. You'll use all three Swagger UIs throughout the tutorials.
For this tutorial, copy the URL of the Swagger UI for `dm` and open it in a browser tab.
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>After finishing this tutorial, keep the Swagger UI for `dm` open to perform the clean up tasks in [Use the Invoice Object Recommendation Business Blueprint to Predict Financial Objects](cp-aibus-dar-swagger-ior-predict).
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1. To be able to use the Swagger UI endpoints, you need to authorize yourself. In the top right corner, click **Authorize**.
2. Get the `access_token` value created in the previous tutorial: [Get OAuth Access Token for Data Attribute Recommendation Using Any Web Browser](cp-aibus-dar-web-oauth-token), then add **Bearer** (with capitalized "B") in front of it, and enter in the **Value** field.
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3. Further below, you find the response of the service. The response includes a representation of dataset schema that was just created. Additionally, the dataset schema received an `id`. Copy it locally as you'll need it in the next step.
2. In the text area, replace the parameter `datasetSchemaId` with the `id` that you copied from the previous step and replace the parameter `name` with an appropriate name for you dataset, `ior_tutorial_dataset`, for example. Then click **Execute** to create the dataset.
3. In the response of the service, you find the `id` of your dataset. Copy it locally as you'll need it in the next steps and also in the next tutorial: [Use the Invoice Object Recommendation Business Blueprint to Train a Machine Learning Model](cp-aibus-dar-swagger-ior-model). Additionally, you find the `status` of the dataset. The status is `NO_DATA` as no data file has been uploaded yet.
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The final step of this tutorial is to upload data to your dataset.
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In this tutorial, you'll use this [dataset](https://github.com/SAPDocuments/Tutorials/raw/master/tutorials/cp-aibus-dar-swagger-ior-upload/data/Dataset_IOR.csv). Right click on the link, then click ***Save link as*** to open the file dialog. In the dialog, replace the file ending `txt` with `csv` as indicated below. Then save the file.
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In this tutorial, you'll use this [dataset](https://raw.githubusercontent.com/sap-tutorials/Tutorials/master/tutorials/cp-aibus-dar-swagger-ior-upload/Dataset_IOR.csv). Right click on the link, then click ***Save link as*** to open the file dialog. In the dialog, replace the file ending `txt` with `csv` as indicated below. Then save the file.
2. Fill the parameter `id` with the `id` of your dataset that you previously copied.
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3. Click **Choose File** below the parameter `Request body`. In the dialog that opens, select the IOR dataset that you just downloaded. Then click **Execute** to upload the data.
In the response, you'll see that the status of your dataset has changed to `VALIDATING`. The service is now validating the data that you have uploaded.
You have successfully uploaded data to your dataset.
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1. Expand the endpoint `GET /datasets/{id}` by clicking on it. Then click **Try it out**.
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2. Fill the parameter `id` with the `id` of your dataset. Click **Execute**.
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3. In the response of the service, you find the status of your dataset. If the status is still `VALIDATING`, check back in a few minutes. If the status is `SUCCEEDED`, your data is valid. In case the status is either `INVALID_DATA` or `VALIDATION_FAILED`, create a new dataset and upload the data once again.
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You have successfully created a dataset and uploaded data. You can now use the dataset to train a machine learning model.
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