diff --git a/spacy/default_config.cfg b/spacy/default_config.cfg
index f1786e04b..258b8634a 100644
--- a/spacy/default_config.cfg
+++ b/spacy/default_config.cfg
@@ -1,6 +1,7 @@
 [nlp]
 lang = null
 pipeline = []
+load_vocab_data = true
 
 [nlp.tokenizer]
 @tokenizers = "spacy.Tokenizer.v1"
diff --git a/spacy/language.py b/spacy/language.py
index 6d2ae3dbe..dc6167ef2 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -121,15 +121,18 @@ class Language:
         vocab (Vocab): A `Vocab` object. If `True`, a vocab is created.
         meta (dict): Custom meta data for the Language class. Is written to by
             models to add model meta data.
-        max_length (int) :
-            Maximum number of characters in a single text. The current models
-            may run out memory on extremely long texts, due to large internal
-            allocations. You should segment these texts into meaningful units,
-            e.g. paragraphs, subsections etc, before passing them to spaCy.
-            Default maximum length is 1,000,000 characters (1mb). As a rule of
-            thumb, if all pipeline components are enabled, spaCy's default
-            models currently requires roughly 1GB of temporary memory per
+        max_length (int): Maximum number of characters in a single text. The
+            current models may run out memory on extremely long texts, due to
+            large internal allocations. You should segment these texts into
+            meaningful units, e.g. paragraphs, subsections etc, before passing
+            them to spaCy. Default maximum length is 1,000,000 charas (1mb). As
+            a rule of thumb, if all pipeline components are enabled, spaCy's
+            default models currently requires roughly 1GB of temporary memory per
             100,000 characters in one text.
+        create_tokenizer (Callable): Function that takes the nlp object and
+            returns a tokenizer.
+        create_lemmatizer (Callable): Function that takes the nlp object and
+            returns a lemmatizer.
         RETURNS (Language): The newly constructed object.
         """
         # We're only calling this to import all factories provided via entry
@@ -150,12 +153,12 @@ class Language:
             if not create_lemmatizer:
                 lemma_cfg = {"lemmatizer": self._config["nlp"]["lemmatizer"]}
                 create_lemmatizer = registry.make_from_config(lemma_cfg)["lemmatizer"]
-            # TODO: where does the vocab data come in?
             vocab = create_vocab(
                 self.lang,
                 self.Defaults,
                 lemmatizer=create_lemmatizer(self),
                 vectors_name=vectors_name,
+                load_data=self._config["nlp"]["load_vocab_data"],
             )
         else:
             if (self.lang and vocab.lang) and (self.lang != vocab.lang):
diff --git a/spacy/schemas.py b/spacy/schemas.py
index e55123e14..c6bdd6e9c 100644
--- a/spacy/schemas.py
+++ b/spacy/schemas.py
@@ -224,21 +224,13 @@ class ConfigSchemaTraining(BaseModel):
         arbitrary_types_allowed = True
 
 
-class ConfigSchemaNlpWritingSystem(BaseModel):
-    direction: StrictStr = Field(..., title="The writing direction, e.g. 'rtl'")
-    has_case: StrictBool = Field(..., title="Whether the language has case")
-    has_letters: StrictBool = Field(..., title="Whether the language has letters")
-
-    class Config:
-        extra = "allow"
-
-
 class ConfigSchemaNlp(BaseModel):
     # fmt: off
     lang: StrictStr = Field(..., title="The base language to use")
     pipeline: List[StrictStr] = Field(..., title="The pipeline component names in order")
     tokenizer: Callable = Field(..., title="The tokenizer to use")
     lemmatizer: Callable = Field(..., title="The lemmatizer to use")
+    load_vocab_data: StrictBool = Field(..., title="Whether to load additional vocab data from spacy-lookups-data")
     # fmt: on
 
     class Config:
diff --git a/spacy/util.py b/spacy/util.py
index 18ce7e474..3b6ba0f25 100644
--- a/spacy/util.py
+++ b/spacy/util.py
@@ -188,8 +188,10 @@ def load_model(
     """Load a model from a package or data path.
 
     name (str): Package name or model path.
-    **overrides: Specific overrides, like pipeline components to disable.
-    RETURNS (Language): `Language` class with the loaded model.
+    disable (Iterable[str]): Names of pipeline components to disable.
+    component_cfg (Dict[str, dict]): Config overrides for pipeline components,
+        keyed by component names.
+    RETURNS (Language): The loaded nlp object.
     """
     cfg = component_cfg
     if isinstance(name, str):  # name or string path
diff --git a/spacy/vocab.pyx b/spacy/vocab.pyx
index 56e62834a..2115789e6 100644
--- a/spacy/vocab.pyx
+++ b/spacy/vocab.pyx
@@ -23,10 +23,10 @@ from .lang.norm_exceptions import BASE_NORMS
 from .lang.lex_attrs import LEX_ATTRS, is_stop, get_lang
 
 
-def create_vocab(lang, defaults, lemmatizer=None, vectors_name=None, load_lookups_data=True):
+def create_vocab(lang, defaults, lemmatizer=None, vectors_name=None, load_data=True):
     # If the spacy-lookups-data package is installed, we pre-populate the lookups
     # with lexeme data, if available
-    if load_lookups_data:
+    if load_data:
         tables = ["lexeme_norm", "lexeme_prob", "lexeme_cluster", "lexeme_settings"]
         lookups = load_lookups(lang, tables=tables, strict=False)
     else: