anchor.diff:  
   +table(["File name", "Variables", "Description"])
       +row
  -        +cell #[+src(gh()) stop_words.py]
  +        +cell #[+src(gh("spacy-dev-resources", "templates/new_language/stop_words.py")) stop_words.py]
           +cell #[code STOP_WORDS] (set)
           +cell
               |  List of most common words. Matching tokens will return #[code True]
               |  for #[code is_stop].
   
       +row
  -        +cell #[+src(gh()) tokenizer_exceptions.py]
  +        +cell #[+src(gh("spacy-dev-resources", "templates/new_language/tokenizer_exceptions.py")) tokenizer_exceptions.py]
           +cell #[code TOKENIZER_EXCEPTIONS] (dict), #[code TOKEN_MATCH] (regex)
           +cell
               |  Special-case rules for the tokenizer, for example, contractions
               |  and abbreviations containing punctuation.
   
       +row
  -        +cell #[+src(gh()) punctuation.py]
  +        +cell #[+src(gh("spaCy", "spacy/lang/punctuation.py")) punctuation.py]
           +cell
               |  #[code TOKENIZER_PREFIXES], #[code TOKENIZER_SUFFIXES],
               |  #[code TOKENIZER_INFIXES] (dicts)
           +cell Regular expressions for splitting tokens, e.g. on punctuation.
   
       +row
  -        +cell #[+src(gh()) lex_attrs.py]
  +        +cell #[+src(gh("spacy-dev-resources", "templates/new_language/lex_attrs.py")) lex_attrs.py]
           +cell #[code LEX_ATTRS] (dict)
           +cell
               |  Functions for setting lexical attributes on tokens, e.g.
               |  #[code is_punct] or #[code like_num].
   
       +row
  -        +cell #[+src(gh()) tag_map.py]
  +        +cell #[+src(gh("spacy-dev-resources", "templates/new_language/tag_map.py")) tag_map.py]
           +cell #[code TAG_MAP] (dict)
           +cell
               |  Dictionary mapping strings in your tag set to
  
positive.code:     |  newlines, and added as a multiline string.
  
  +aside("What does spaCy consider a stop word?")
      |  There's no particularly principal logic behind what words should be
      |  added to the stop list. Make a list that you think might be useful
      |  to people and is likely to be unsurprising. As a rule of thumb, words
      |  that are very rare are unlikely to be useful stop words.
  
negative.code:     +row
          +cell #[+src(gh()) morph_rules.py]
          +cell #[code MORPH_RULES] (dict)
          +cell Exception rules for morphological analysis of irregular words.
  
      +row
          +cell #[+src(gh("spacy-dev-resources", "templates/new_language/lemmatizer.py")) lemmatizer.py]
          +cell #[code LOOKUP] (dict)
          +cell
              |  Lookup-based lemmatization table. If more lemmatizer data is
  