title: How do you build training dataset from scratch for a custom multi-class standfordNLP/Stanza NER tagging model in BIOES/BILOU format?
body: <p>I am utilizing NLP for a custom application and I want to train my own NER tagger model in StanfordNLP currently known as Stanza.</p>
  <p>The default model is limited to very general tags such as LOC, PER, MISC, COUNTRY, TIME etc.</p>
  <p>My custom tags are more specific ex. Food, Sport, Software, Brand. How would I got about formatting data I <strong>scraped</strong> off of the web or from PDF files in BIOES/BILOU format?
  <a href="https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)</a></p>
  <p>Do I necessarily have to tag them manually? or write a script to generate the data in the format shown below:</p>
  <pre><code>Alex S-PER
  is O
  playing O
  basketball I-SPORT
  with O
  Marty B-PER
  . O
  Rick E-PER
  likes O
  to O
  eat O
  Pizza I-FOOD
  in O
  Los B-LOC
  Angeles E-LOC
  </code></pre>
  <p>If so then what tools and libraries can I use in Python?</p>
  <p>Thank you in advance.</p>