Set rule-based lemmatizers as default (#6076)

For languages without provided models and with lemmatizer rules in
`spacy-lookups-data`, make the rule-based lemmatizer the default:
Bengali, Persian, Norwegian, Swedish

spacy/lang/bn/__init__.py

@@ -1,7 +1,11 @@
+from typing import Optional
+from thinc.api import Model
 from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
 from .punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES, TOKENIZER_INFIXES
 from .stop_words import STOP_WORDS
 from ...language import Language
+from ...lookups import Lookups
+from ...pipeline import Lemmatizer
 
 
 class BengaliDefaults(Language.Defaults):
@@ -17,4 +21,22 @@ class Bengali(Language):
     Defaults = BengaliDefaults
 
 
+@Bengali.factory(
+    "lemmatizer",
+    assigns=["token.lemma"],
+    default_config={"model": None, "mode": "rule", "lookups": None},
+    scores=["lemma_acc"],
+    default_score_weights={"lemma_acc": 1.0},
+)
+def make_lemmatizer(
+    nlp: Language,
+    model: Optional[Model],
+    name: str,
+    mode: str,
+    lookups: Optional[Lookups],
+):
+    lookups = Lemmatizer.load_lookups(nlp.lang, mode, lookups)
+    return Lemmatizer(nlp.vocab, model, name, mode=mode, lookups=lookups)
+
+
 __all__ = ["Bengali"]

spacy/lang/fa/__init__.py

@@ -1,9 +1,13 @@
+from typing import Optional
+from thinc.api import Model
 from .stop_words import STOP_WORDS
 from .lex_attrs import LEX_ATTRS
 from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
 from .punctuation import TOKENIZER_SUFFIXES
 from .syntax_iterators import SYNTAX_ITERATORS
 from ...language import Language
+from ...lookups import Lookups
+from ...pipeline import Lemmatizer
 
 
 class PersianDefaults(Language.Defaults):
@@ -20,4 +24,22 @@ class Persian(Language):
     Defaults = PersianDefaults
 
 
+@Persian.factory(
+    "lemmatizer",
+    assigns=["token.lemma"],
+    default_config={"model": None, "mode": "rule", "lookups": None},
+    scores=["lemma_acc"],
+    default_score_weights={"lemma_acc": 1.0},
+)
+def make_lemmatizer(
+    nlp: Language,
+    model: Optional[Model],
+    name: str,
+    mode: str,
+    lookups: Optional[Lookups],
+):
+    lookups = Lemmatizer.load_lookups(nlp.lang, mode, lookups)
+    return Lemmatizer(nlp.vocab, model, name, mode=mode, lookups=lookups)
+
+
 __all__ = ["Persian"]

spacy/lang/nb/__init__.py

@@ -1,9 +1,13 @@
+from typing import Optional
+from thinc.api import Model
 from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
 from .punctuation import TOKENIZER_PREFIXES, TOKENIZER_INFIXES
 from .punctuation import TOKENIZER_SUFFIXES
 from .stop_words import STOP_WORDS
 from .syntax_iterators import SYNTAX_ITERATORS
 from ...language import Language
+from ...lookups import Lookups
+from ...pipeline import Lemmatizer
 
 
 class NorwegianDefaults(Language.Defaults):
@@ -20,4 +24,22 @@ class Norwegian(Language):
     Defaults = NorwegianDefaults
 
 
+@Norwegian.factory(
+    "lemmatizer",
+    assigns=["token.lemma"],
+    default_config={"model": None, "mode": "rule", "lookups": None},
+    scores=["lemma_acc"],
+    default_score_weights={"lemma_acc": 1.0},
+)
+def make_lemmatizer(
+    nlp: Language,
+    model: Optional[Model],
+    name: str,
+    mode: str,
+    lookups: Optional[Lookups],
+):
+    lookups = Lemmatizer.load_lookups(nlp.lang, mode, lookups)
+    return Lemmatizer(nlp.vocab, model, name, mode=mode, lookups=lookups)
+
+
 __all__ = ["Norwegian"]

spacy/lang/sv/__init__.py

@@ -1,8 +1,13 @@
+from typing import Optional
+from thinc.api import Model
 from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
 from .stop_words import STOP_WORDS
 from .lex_attrs import LEX_ATTRS
 from .syntax_iterators import SYNTAX_ITERATORS
 from ...language import Language
+from ...lookups import Lookups
+from ...pipeline import Lemmatizer
+
 
 # Punctuation stolen from Danish
 from ..da.punctuation import TOKENIZER_INFIXES, TOKENIZER_SUFFIXES
@@ -22,4 +27,22 @@ class Swedish(Language):
     Defaults = SwedishDefaults
 
 
+@Swedish.factory(
+    "lemmatizer",
+    assigns=["token.lemma"],
+    default_config={"model": None, "mode": "rule", "lookups": None},
+    scores=["lemma_acc"],
+    default_score_weights={"lemma_acc": 1.0},
+)
+def make_lemmatizer(
+    nlp: Language,
+    model: Optional[Model],
+    name: str,
+    mode: str,
+    lookups: Optional[Lookups],
+):
+    lookups = Lemmatizer.load_lookups(nlp.lang, mode, lookups)
+    return Lemmatizer(nlp.vocab, model, name, mode=mode, lookups=lookups)
+
+
 __all__ = ["Swedish"]

spacy/tests/lang/test_lemmatizers.py

@@ -8,7 +8,7 @@ from spacy.util import get_lang_class
 # Only include languages with no external dependencies
 # excluded: ru, uk
 # excluded for custom tables: pl
-LANGUAGES = ["el", "en", "fr", "nl"]
+LANGUAGES = ["bn", "el", "en", "fa", "fr", "nb", "nl", "sv"]
 # fmt: on