      predicted = Doc(vocab, words=pred_words)
      example = Example.from_dict(predicted, annots)
      for i, token in enumerate(example.reference):
          assert token.tag_ == annots["tags"][i]
      aligned_tags = example.get_aligned("tag", as_string=True)
      assert aligned_tags == ["NN" for _ in predicted]
  
  
  def test_aligned_tags():
      pred_words = ["Apply", "some", "sunscreen", "unless", "you", "can", "not"]
 
</s> remove     example = Example.from_dict(predicted, annots)
    aligned_tags = example.get_aligned("tag", as_string=True)
    assert aligned_tags == ["VERB", "DET", "NOUN", "SCONJ", "PRON", "VERB", "VERB"]
 </s> add     example1 = Example.from_dict(predicted, annots)
    aligned_tags1 = example1.get_aligned("TAG", as_string=True)
    assert aligned_tags1 == ["VERB", "DET", "NOUN", "SCONJ", "PRON", "VERB", "VERB"]
    # ensure that to_dict works correctly
    example2 = Example.from_dict(predicted, example1.to_dict())
    aligned_tags2 = example2.get_aligned("TAG", as_string=True)
    assert aligned_tags2 == ["VERB", "DET", "NOUN", "SCONJ", "PRON", "VERB", "VERB"] </s> remove     aligned_tags = example.get_aligned("tag", as_string=True)
 </s> add     aligned_tags = example.get_aligned("TAG", as_string=True) </s> remove             for x in eg.get_aligned("sent_start"):
 </s> add             for x in eg.get_aligned("SENT_START"): </s> remove         truths = [eg.get_aligned("tag", as_string=True) for eg in examples]
 </s> add         truths = [eg.get_aligned("TAG", as_string=True) for eg in examples] </s> remove                 "ids": [t.i+1 for t in self.reference],
                "words": [t.text for t in self.reference],
                "tags": [t.tag_ for t in self.reference],
                "lemmas": [t.lemma_ for t in self.reference],
                "pos": [t.pos_ for t in self.reference],
                "morphs": [t.morph_ for t in self.reference],
                "heads": [t.head.i for t in self.reference],
                "deps": [t.dep_ for t in self.reference],
                "sent_starts": [int(bool(t.is_sent_start)) for t in self.reference]
 </s> add                 "ORTH": [t.text for t in self.reference],
                "SPACY": [bool(t.whitespace_) for t in self.reference],
                "TAG": [t.tag_ for t in self.reference],
                "LEMMA": [t.lemma_ for t in self.reference],
                "POS": [t.pos_ for t in self.reference],
                "MORPH": [t.morph_ for t in self.reference],
                "HEAD": [t.head.i for t in self.reference],
                "DEP": [t.dep_ for t in self.reference],
                "SENT_START": [int(bool(t.is_sent_start)) for t in self.reference]
    aligned_tags = example.get_aligned("TAG", as_string=True)
      predicted = Doc(vocab, words=pred_words)
      example = Example.from_dict(predicted, annots)
      for i, token in enumerate(example.reference):
          assert token.tag_ == annots["tags"][i]
    aligned_tags = example.get_aligned("TAG", as_string=True)
      assert aligned_tags == ["NN" for _ in predicted]
  
  
  def test_aligned_tags():
      pred_words = ["Apply", "some", "sunscreen", "unless", "you", "can", "not"]
keep keep keep keep replace keep keep keep keep keep
 Example Dict format consistency (#5858)

* consistently use upper-case IDS in token_annotation format and for get_aligned

* remove ID from to_dict (not used in from_dict either)

* fix test

Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> 
spacy/tests/test_new_example.py