title: Training and evaluating spaCy model by sentences or paragraphs
body: <h2>Observation:</h2>
  
  <p>Paragraph: <code>I love apple. I eat one banana a day</code><br>
  Sentence: <code>I love apple.</code>, <code>I eat one banana a day</code><br>
  There are two sentences in this paragraph, <code>I love apple</code> and <code>I eat one banana a day</code>. If I put the whole paragraph into spaCy, it'll recognize only one entity, for example, <code>apple</code>, but if I put the sentences in paragraph one by one, spaCy can recognize two entities, <code>apple</code> and <code>banana</code>.(<strong>This is just an example to show my point, the actual recognition result could be different</strong>)  </p>
  
  <h2>Situation:</h2>
  
  <p>After having trained a model by myself, I want to evaluate the recognizing accuracy of my model, there are two ways to pass the text into the spaCy model:<br>
  1. split the paragraph into sentences and pass the sentence one by one    <code>
  for sentence in paragraph:
      doc = nlp(sentence)
      # retrieve the parsing result
  </code>
  2. pass the paragraph at once
  <code>
    doc = nlp(paragraph)
    # retrieve the parsing result
  </code></p>
  
  <h2>Question:</h2>
  
  <ol>
  <li>I'm wondering which way would be better to test the performance of the model? Since I'm sure passing by sentence can always recognize more entities than passing by paragraph.  </li>
  <li>If the second one is better, do I also need to change the way that I trained the model? Currently, I train the spacy model sentence by sentence rather than a paragraph.</li>
  </ol>
  
  <h2>The goal of my project:</h2>
  
  <p>After getting a document, recognize all the entities that I'm interested in the document.</p>
  
  <p>Thanks!</p>