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Training an email parser

Updated on May 17, 2024

Add training data to the pxEmailParser model to train the model with examples from your domain. As a result, the model is better at recognizing the signature, email body, and disclaimer in emails that are typical for your business.

  1. In Dev Studio, search for pxEmailParser, and then open the model.
  2. On the pxEmailParser tab, click Save as.
  3. In the Save As Decision Data window, select an open ruleset, and then choose the appropriate label and identifier.
  4. Click Open in Prediction Studio.
  5. For the relevant language model, click DownloadTraining data (zip).
    Training data download
    The Email Parser model is open in Prediction Studio. Training data for English is selected for download.
  6. Open the training file in Excel, and then add rows in the same format as the other rows.
    <START:entityType> denotes the start of an entity. <END> denotes the end of an entity. This model uses paragraph extraction, so ensure that each <START:entityType> is at the beginning of a line and each <END> tag is at the end of a line. Ensure that each tag has a space before and after.
  7. Remove any rows if required.
    If you believe that the model works better with only domain-specific data, then you can choose to remove the default data that comes with the model.
  8. In Prediction Studio, click Models, and then open the relevant model.
  9. Click Create with machine learning.
  10. In the Source selection step, click Upload data source, and then upload the file.
    Training data upload
    In the model update wizard, a file with training data is selected for upload.
  11. Complete the model update with the necessary model parameters.
What to do next: Test the email parser to see if the model processes text as expected. For more information, see Testing an email parser.

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