Remove omit_extra_lookups

spacy/cli/init_model.py

@@ -41,7 +41,6 @@ def init_model_cli(
     truncate_vectors: int = Opt(0, "--truncate-vectors", "-t", help="Optional number of vectors to truncate to when reading in vectors file"),
     vectors_name: Optional[str] = Opt(None, "--vectors-name", "-vn", help="Optional name for the word vectors, e.g. en_core_web_lg.vectors"),
     model_name: Optional[str] = Opt(None, "--model-name", "-mn", help="Optional name for the model meta"),
-    omit_extra_lookups: bool = Opt(False, "--omit-extra-lookups", "-OEL", help="Don't include extra lookups in model"),
     base_model: Optional[str] = Opt(None, "--base-model", "-b", help="Base model (for languages with custom tokenizers)")
     # fmt: on
 ):
@@ -60,7 +59,6 @@ def init_model_cli(
         truncate_vectors=truncate_vectors,
         vectors_name=vectors_name,
         model_name=model_name,
-        omit_extra_lookups=omit_extra_lookups,
         base_model=base_model,
         silent=False,
     )
@@ -77,7 +75,6 @@ def init_model(
     truncate_vectors: int = 0,
     vectors_name: Optional[str] = None,
     model_name: Optional[str] = None,
-    omit_extra_lookups: bool = False,
     base_model: Optional[str] = None,
     silent: bool = True,
 ) -> Language:
@@ -109,13 +106,6 @@ def init_model(
     with msg.loading("Creating model..."):
         nlp = create_model(lang, lex_attrs, name=model_name, base_model=base_model)
 
-    # Create empty extra lexeme tables so the data from spacy-lookups-data
-    # isn't loaded if these features are accessed
-    if omit_extra_lookups:
-        nlp.vocab.lookups.remove_table("lexeme_cluster")
-        nlp.vocab.lookups.remove_table("lexeme_prob")
-        nlp.vocab.lookups.remove_table("lexeme_settings")
-
     msg.good("Successfully created model")
     if vectors_loc is not None:
         add_vectors(

spacy/cli/train.py

@@ -120,13 +120,6 @@ def train(
         # Load morph rules
         nlp.vocab.morphology.load_morph_exceptions(morph_rules)
 
-    # Create empty extra lexeme tables so the data from spacy-lookups-data
-    # isn't loaded if these features are accessed
-    if config["training"]["omit_extra_lookups"]:
-        nlp.vocab.lookups.remove_table("lexeme_cluster")
-        nlp.vocab.lookups.remove_table("lexeme_prob")
-        nlp.vocab.lookups.remove_table("lexeme_settings")
-
     # Load a pretrained tok2vec model - cf. CLI command 'pretrain'
     if weights_data is not None:
         tok2vec_path = config.get("pretraining", {}).get("tok2vec_model", None)

spacy/default_config.cfg

@@ -47,7 +47,6 @@ score_weights = {"tags_acc": 0.2, "las": 0.4, "ents_f": 0.4}
 # These settings are invalid for the transformer models.
 init_tok2vec = null
 discard_oversize = false
-omit_extra_lookups = false
 batch_by = "sequences"
 raw_text = null
 tag_map = null

spacy/schemas.py

@@ -211,7 +211,6 @@ class ConfigSchemaTraining(BaseModel):
     score_weights: Dict[StrictStr, Union[StrictFloat, StrictInt]] = Field(..., title="Weights of each score type for selecting final model")
     init_tok2vec: Optional[StrictStr] = Field(..., title="Path to pretrained tok2vec weights")
     discard_oversize: StrictBool = Field(..., title="Whether to skip examples longer than batch size")
-    omit_extra_lookups: StrictBool = Field(..., title="Don't include extra lookups in model")
     batch_by: StrictStr = Field(..., title="Batch examples by type")
     raw_text: Optional[StrictStr] = Field(..., title="Raw text")
     tag_map: Optional[StrictStr] = Field(..., title="Path to JSON-formatted tag map")