          split_examples = []
          for i in range(len(t.words)):
              if i > 0 and t.sent_starts[i] == 1:
                  s_example.set_token_annotation(ids=s_ids,
                          words=s_words, tags=s_tags, heads=s_heads, deps=s_deps,
                          entities=s_ents, morphs=s_morphs, lemmas=s_lemmas,
                          sent_starts=s_sent_starts, brackets=s_brackets)
                  split_examples.append(s_example)
                  s_example = Example(doc=None, doc_annotation=self.doc_annotation)
                  s_ids, s_words, s_tags, s_heads = [], [], [], []
                  s_deps, s_ents, s_morphs, s_lemmas = [], [], [], []
                  s_sent_starts, s_brackets = [], []
 
</s> remove                 s_ids, s_words, s_tags, s_heads = [], [], [], []
 </s> add                 s_ids, s_words, s_tags, s_pos, s_heads = [], [], [], [], [] </s> remove         s_ids, s_words, s_tags, s_heads = [], [], [], []
        s_deps, s_ents, s_morphs, s_lemmas, s_sent_starts = [], [], [], [], []
 </s> add         s_ids, s_words, s_tags, s_pos, s_morphs = [], [], [], [], []
        s_lemmas, s_heads, s_deps, s_ents, s_sent_starts = [], [], [], [], [] </s> remove                 heads=s_heads, deps=s_deps, entities=s_ents,
                morphs=s_morphs, lemmas=s_lemmas, sent_starts=s_sent_starts,
 </s> add                 pos=s_pos, morphs=s_morphs, lemmas=s_lemmas, heads=s_heads,
                deps=s_deps, entities=s_ents, sent_starts=s_sent_starts, </s> remove                 json_token["lemma"] = token.lemma_
 </s> add  </s> remove         morphs = []
        lemmas = []
 </s> add 
                        words=s_words, tags=s_tags, pos=s_pos, morphs=s_morphs,
                        lemmas=s_lemmas, heads=s_heads, deps=s_deps,
                        entities=s_ents, sent_starts=s_sent_starts,
                        brackets=s_brackets)
          split_examples = []
          for i in range(len(t.words)):
              if i > 0 and t.sent_starts[i] == 1:
                  s_example.set_token_annotation(ids=s_ids,
                        words=s_words, tags=s_tags, pos=s_pos, morphs=s_morphs,
                        lemmas=s_lemmas, heads=s_heads, deps=s_deps,
                        entities=s_ents, sent_starts=s_sent_starts,
                        brackets=s_brackets)
                        words=s_words, tags=s_tags, pos=s_pos, morphs=s_morphs,
                        lemmas=s_lemmas, heads=s_heads, deps=s_deps,
                        entities=s_ents, sent_starts=s_sent_starts,
                        brackets=s_brackets)
                        words=s_words, tags=s_tags, pos=s_pos, morphs=s_morphs,
                        lemmas=s_lemmas, heads=s_heads, deps=s_deps,
                        entities=s_ents, sent_starts=s_sent_starts,
                        brackets=s_brackets)
                  split_examples.append(s_example)
                  s_example = Example(doc=None, doc_annotation=self.doc_annotation)
                  s_ids, s_words, s_tags, s_heads = [], [], [], []
                  s_deps, s_ents, s_morphs, s_lemmas = [], [], [], []
                  s_sent_starts, s_brackets = [], []
keep keep keep keep replace replace replace keep keep keep keep keep
 Add support for pos/morphs/lemmas in training data (#4941)

Add support for pos/morphs/lemmas throughout `GoldParse`, `Example`, and
`docs_to_json()`. 
spacy/gold.pyx