Create & Configure Fields

Adding Fields

Fields cannot be deleted or renamed, so please think carefully before adding new fields. If, however, there are fields that you later decide you do not want to use for training an ML model, you can always hide them using the Hidden checkbox in the Edit Field window.

Click here for details about fields, their meaning, and when to use them.

Note: A maximum of 40 fields can be created.

Column Fields

A line item Description or Unit Price on an invoice document would be examples of Column fields.

1. Click + in the table section at the top of the page to add a new Column field. The Create Column Field window is displayed.
2. In the Enter Unique Field Name field, fill in a unique name for the field. The field does not accept uppercase letters.
3. Click Create. The Edit Field window is displayed.
4. From the Content Type drop-down, select the content type.
5. From the Scoring drop-down, select the measure used to determine accuracy when running evaluations of model predictions.
6. Click the Hotkey field and press a key on your keyboard to automatically populate it.
7. Fill in the hex code of the desired field color in the Color field.
8. Select the Multi line checkbox if the field to be checked against might span across multiple text lines, such as addresses or descriptions. If this option is not selected, only the first line is returned.
9. Select the Split items checkbox if you want this field to be used as a delimiter between line items or rows in a table. Any line on which this field appears is considered to be a new line item or row in the table. Most commonly, this is used on Line Amount fields on Invoice line items.
10. Select the Hidden checkbox if you do not want this field to be part of exported datasets.
11. Click Save to save your settings.

Regular Fields

These are fields which appear only once on a given document. A line item Invoice Number or Total Amount on an invoice document would be examples of Column fields.

1. Click + on the right pane in the Regular Fields section. The Create Regular Field window is displayed.
2. Fill in a unique name for the field in the Enter Unique Field Name field. The field does not accept uppercase letters.
3. Click Create. The Edit Field window is displayed.
4. Select the content type from the Content Type drop-down.
5. Select the post-processing mechanism in case the model predicts more than one instance of a field on a given page from the Post processing drop-down.
6. Click the Hotkey field and press a key on your keyboard to automatically populate it.
7. In the Color field, fill in the hex code of the desired field color o
8. From the Multi page drop-down, select the data retrieval strategy. This is used in case that fields appear on a few different pages of a multi-page document. This option defines how the model decides which one to return.
9. From the Scoring drop-down, select the measure used to determine accuracy when running evaluations of model predictions.
10. Select the Multi line checkbox if the field to be checked against might span across multiple text lines, such as addresses or descriptions. If this option is not selected, only the first line is returned.
11. Select the Hidden checkbox if you do not want this field to be part of exported datasets.
12. Click Save to save your settings.

Classification Fields

Data points which refer to a document as a whole. For instance, the Expense Type of a receipt (Food, Hotel, Airline, Transportation) or the Currency of an invoice (USD, EUR, JPY) would be examples of Classification fields.

1. Click + on the right pane in the Classification Fields section. The Create Classification Field window is displayed.
2. Fill in a unique name for the field in the Enter Unique Field Name field. The field does not accept uppercase letters.
3. Click Create. The Edit Field window is displayed.
4. In the text area, fill in the list of classes and type the names as a comma separated list.
5. Click Save to save your settings.
   Important: Contrary to Regular and Column fields, Classification fields are not Re-trained. For example for Currency field, if you retrain the Invoices model on a dataset containing only USD and INR invoices, then the resulting model will only be able to recognize those two currencies.

Field Descriptions

Management Bar

Displayed at the top of the page in Data Manager. Enables you to perform multiple operations: navigate in between documents, delete a document, filter documents, run AI model predictions, import and export documents.

Field	Description
→	Navigate in between documents that match the active filter. In between the two arrows a counter is displayed. It illustrates the number of the current document out of the total number of documents that match the active filter.
Delete / Recover	Delete or recover a document.
Filter Drop-Down	Filter documents. This filter applies to exported data as well. The following options are available: train-validate-set -test-set -deleted -labeled -unlabeled -<batch_name>
Predict	Run AI model predictions and display the results.
Import	Import a new document to be labeled.
Export	Export labeled data. The active filter applies to the exported data.
[DocumentName]	The name of the currently active document.
[UserName]	The username of the currently active user.
Log Out	Log out of Data Manager. Logging out also clears the cookies.
Help	Displays the Data Manager help menu.

Create Field Window

Enables you to configure the name of the field to be added.

Field	Description
Enter Unique Field Name	The name of the field. Can only contain lowercase letters, numbers, underscore “_” and dash “-“.

Edit Field Window

Enables you to configure regular and column field.

Field	Description
Content Type	The content type of a field. The following options are available: string – appropriate for company names or addresses, as well as payment terms, or for any other field where the RPA developer prefers to build the parsing or formatting logic manually, in the RPA workflow. number – appropriate for amounts or quantities, with intelligent parsing of the decimal/thousands separators. date – the model parses, formats and unifies the output in a yyyy-mm-dd format. phone - appropriate for phone numbers. id-no – appropriate for alphanumeric codes, numbers of IDs, it is similar to the string content type, but includes cleaning of any characters coming before a “:”. If the id number you need to extract might contain “:” characters, please use string as content type instead, to avoid data loss.
Post Processing	Only displayed for regular fields. The post-processing mechanism. The following options are available: first span – if model predicts more than one instance of a field on a given page, the model returns the first one. largest value – if model predicts more than one instance of a field on a given page, the model returns the largest numeric value. This is only displayed for content of type number and is appropriate for Total Amount fields. longest value – if model predicts more than one instance of a field on a given page, the model returns the value consisting of the longest string of characters.
Hotkey	The shortcut key for the field.
Color	The color for the field.
Multi Page	The data return strategy in case a field appears on multiple pages in a document. The following options are available: highest confidence - the default choice for string,phone, and number content types. first occurrence - the default choice for id-no and data content types. last occurrence - longest string - only displayed for content of type string. - shortest string - only displayed for content of type string. - highest numeric value - only displayed for content of type number. - lowest numeric value - only displayed for content of type number.
Scoring	Can only be configured for content of type string. All other content types use an Exact Match scoring strategy. The measure used to determine accuracy when running evaluations of model predictions. exact match – a prediction is only deemed to be correct (score of 1) if it exactly matches the true value. If it differs by even a single character, then it is deemed to be incorrect (score of 0). levenshtein – a prediction is deemed to be partially correct according to the Levenshtein distance between the prediction and the true value. If a 10-letter value is predicted correctly except for the last 2 characters, then the score of that prediction will be 0.8.
Multi Line	Select this checkbox for fields which may span across multiple lines, such as addresses or descriptions. Otherwise, only the first line is returned.
Split Items	Only displayed for column fields. Select this checkbox if you want this field to be used as a delimiter between line items or rows in a table. Any line on which this field appears is considered to be a new line item or row in the table. Most commonly this is used on Line Amount fields on Invoice line items.
Hidden	Select this checkbox if you do not want this field to be part of exported datasets.

Data Manager Help Menu

The Labeling Controls section displays the controls to be used when handling data.

The Document Shortcuts section displays the shortcuts used to perform various operations such as navigation and UI scaling.

The Configuration section displays details about the instance configuration as performed during installation.

The Error Reporting section enables you to view recently generated logs.