title: Plateuing in loss while training NER with spaCy
body: <p>I'm training (from scratch) a new set of entities and doing exactly as described in <a href="https://github.com/explosion/spacy/blob/master/examples/training/train_new_entity_type.py" rel="noreferrer">spaCy tutorial</a>, however, my loss is plateauing and large number of epochs does not help.</p>
  
  <p><strong>My data:</strong></p>
  
  <p>9 different entities, 15000 training data (sentences). The loss after 20 epochs:</p>
  
  <pre><code>Loaded model 'en'
  Losses {'ner': 25461.3508122763}
  Losses {'ner': 17003.450728844182}
  Losses {'ner': 15725.198527784352}
  Losses {'ner': 15315.754479839785}
  Losses {'ner': 14980.468680851985}
  Losses {'ner': 14716.52629194191}
  Losses {'ner': 14346.623731715972}
  Losses {'ner': 14463.972966984807}
  Losses {'ner': 14195.106732198006}
  Losses {'ner': 14058.390174787504}
  Losses {'ner': 13875.850727875884}
  Losses {'ner': 13859.096326599261}
  Losses {'ner': 13614.887464660655}
  Losses {'ner': 13512.779816124807}
  Losses {'ner': 13388.69595626908}
  Losses {'ner': 13496.388241585315}
  Losses {'ner': 13530.602194116611}
  Losses {'ner': 13245.709490846923}
  Losses {'ner': 13219.483523900466}
  Losses {'ner': 13189.088232180386}
  </code></pre>
  
  <p><strong>Question 1:</strong></p>
  
  <p>What is the best way to organise the training data if there are several entities within a single sentence? Should I combine all entities in a list or it is better to train with a single entity?</p>
  
  <p>For example:</p>
  
  <pre><code>("Horses and dogs are too tall and they pretend to care about your feelings", {'entities': [(0, 6, 'ANIMAL'), (11, 15, 'ANIMAL')]})
  </code></pre>
  
  <p>or it is better to split:</p>
  
  <pre><code>("Horses and dogs are too tall and they pretend to care about your feelings", {'entities': [(0, 6, 'ANIMAL')]}),
  
  ("Horses and dogs are too tall and they pretend to care about your feelings", {'entities': [(11, 15, 'ANIMAL')]})
  </code></pre>
  
  <p><strong>Question 2:</strong></p>
  
  <p>Should I include empty sentences too (with no entities)?</p>
  
  <pre><code>("The new electric cars is great!", {'entities': []})
  </code></pre>
  
  <p>Apparently, the model predicts not too bad (f1~0.7), however I am wondering what are the best practices to fine tune the model (apart from using the Prodigy on top of this trained model).</p>