title: Difference between blank and pretrained models in spacy
body: <p>I am currently trying to train a text classifier using <code>spacy</code> and I got stuck with following question: what is the difference between creating a blank model using <code>spacy.blank('en')</code> and using a pretrained model <code>spacy.load('en_core_web_sm')</code>. Just to see the difference I wrote this code:</p>
  <pre><code>text = &quot;hello everyone, it's a wonderful day today&quot;
  
  nlp1 = spacy.load('en_core_web_sm')
  for token in nlp1(text):
      print(token.text, token.lemma_, token.is_stop, token.pos_)
  </code></pre>
  <p>and it gave me the following result:</p>
  <blockquote>
  <p>hello hello False INTJ</p>
  <p>everyone everyone True PRON</p>
  <p>, , False PUNCT</p>
  <p>it -PRON- True PRON</p>
  <p>'s be True AUX</p>
  <p>a a True DET</p>
  <p>wonderful wonderful False ADJ</p>
  <p>day day False NOUN</p>
  <p>today today False NOUN</p>
  </blockquote>
  <p>Then I tried this (for the same text)</p>
  <pre><code>nlp2 = spacy.blank('en')
  for token in nlp2(text):
      print(token.text, token.lemma_, token.is_stop, token.pos_)
  </code></pre>
  <p>and the result was</p>
  <blockquote>
  <p>hello hello False</p>
  <p>everyone everyone True</p>
  <p>, , False</p>
  <p>it -PRON- True PRON</p>
  <p>'s 's True</p>
  <p>a a True</p>
  <p>wonderful wonderful False</p>
  <p>day day False</p>
  <p>today today False</p>
  </blockquote>
  <p>Not only are the results different (for example, lemma for <code>'s</code> is different) but there are also no POS tagging for most of words in blank model.</p>
  <p>So obviously I need a pretrained model for normalizing my data. But I still don't understand how it should be with my data classifier. Should I 1) create a blank model for training text classifier (using <code>nlp.update()</code>) and load a pretrained model for removing stop words, lemmatization and POS tagging or 2) only load a pretrained model for both: normalizing and training my text classifier?</p>
  <p>Thanks in advance for any advice!</p>