Tidy up and auto-format

spacy/__init__.py

@@ -5,7 +5,7 @@ import sys
 # set library-specific custom warning handling before doing anything else
 from .errors import setup_default_warnings
 
-setup_default_warnings()
+setup_default_warnings()  # noqa: E402
 
 # These are imported as part of the API
 from thinc.api import prefer_gpu, require_gpu, require_cpu  # noqa: F401

spacy/language.py

@@ -1447,7 +1447,7 @@ class Language:
     ) -> Iterator[Tuple[Doc, _AnyContext]]:
         ...
 
-    def pipe(
+    def pipe(  # noqa: F811
         self,
         texts: Iterable[str],
         *,

spacy/tests/doc/test_creation.py

@@ -69,4 +69,4 @@ def test_create_with_heads_and_no_deps(vocab):
     words = "I like ginger".split()
     heads = list(range(len(words)))
     with pytest.raises(ValueError):
-        doc = Doc(vocab, words=words, heads=heads)
+        Doc(vocab, words=words, heads=heads)

spacy/tests/parser/test_ner.py

@@ -329,8 +329,8 @@ def test_ner_constructor(en_vocab):
     }
     cfg = {"model": DEFAULT_NER_MODEL}
     model = registry.resolve(cfg, validate=True)["model"]
-    ner_1 = EntityRecognizer(en_vocab, model, **config)
-    ner_2 = EntityRecognizer(en_vocab, model)
+    EntityRecognizer(en_vocab, model, **config)
+    EntityRecognizer(en_vocab, model)
 
 
 def test_ner_before_ruler():

spacy/tests/parser/test_parse.py

@@ -224,8 +224,8 @@ def test_parser_constructor(en_vocab):
     }
     cfg = {"model": DEFAULT_PARSER_MODEL}
     model = registry.resolve(cfg, validate=True)["model"]
-    parser_1 = DependencyParser(en_vocab, model, **config)
-    parser_2 = DependencyParser(en_vocab, model)
+    DependencyParser(en_vocab, model, **config)
+    DependencyParser(en_vocab, model)
 
 
 @pytest.mark.parametrize("pipe_name", ["parser", "beam_parser"])

spacy/tests/pipeline/test_annotates_on_update.py

@@ -74,7 +74,7 @@ def test_annotates_on_update():
     nlp.add_pipe("assert_sents")
 
     # When the pipeline runs, annotations are set
-    doc = nlp("This is a sentence.")
+    nlp("This is a sentence.")
 
     examples = []
     for text in ["a a", "b b", "c c"]:

spacy/tests/pipeline/test_lemmatizer.py

@@ -110,4 +110,4 @@ def test_lemmatizer_serialize(nlp):
     assert doc2[0].lemma_ == "cope"
 
     # Make sure that lemmatizer cache can be pickled
-    b = pickle.dumps(lemmatizer2)
+    pickle.dumps(lemmatizer2)

spacy/tests/pipeline/test_pipe_methods.py

@@ -52,7 +52,7 @@ def test_cant_add_pipe_first_and_last(nlp):
         nlp.add_pipe("new_pipe", first=True, last=True)
 
 
-@pytest.mark.parametrize("name", ["my_component"])
+@pytest.mark.parametrize("name", ["test_get_pipe"])
 def test_get_pipe(nlp, name):
     with pytest.raises(KeyError):
         nlp.get_pipe(name)
@@ -62,7 +62,7 @@ def test_get_pipe(nlp, name):
 
 @pytest.mark.parametrize(
     "name,replacement,invalid_replacement",
-    [("my_component", "other_pipe", lambda doc: doc)],
+    [("test_replace_pipe", "other_pipe", lambda doc: doc)],
 )
 def test_replace_pipe(nlp, name, replacement, invalid_replacement):
     with pytest.raises(ValueError):
@@ -435,8 +435,8 @@ def test_update_with_annotates():
 
         return component
 
-    c1 = Language.component(f"{name}1", func=make_component(f"{name}1"))
-    c2 = Language.component(f"{name}2", func=make_component(f"{name}2"))
+    Language.component(f"{name}1", func=make_component(f"{name}1"))
+    Language.component(f"{name}2", func=make_component(f"{name}2"))
 
     components = set([f"{name}1", f"{name}2"])
 

spacy/tests/regression/test_issue5001-5500.py

@@ -69,9 +69,12 @@ def test_issue5082():
 
 
 def test_issue5137():
-    @Language.factory("my_component")
+    factory_name = "test_issue5137"
+    pipe_name = "my_component"
+
+    @Language.factory(factory_name)
     class MyComponent:
-        def __init__(self, nlp, name="my_component", categories="all_categories"):
+        def __init__(self, nlp, name=pipe_name, categories="all_categories"):
             self.nlp = nlp
             self.categories = categories
             self.name = name
@@ -86,13 +89,13 @@ def test_issue5137():
             pass
 
     nlp = English()
-    my_component = nlp.add_pipe("my_component")
+    my_component = nlp.add_pipe(factory_name, name=pipe_name)
     assert my_component.categories == "all_categories"
     with make_tempdir() as tmpdir:
         nlp.to_disk(tmpdir)
-        overrides = {"components": {"my_component": {"categories": "my_categories"}}}
+        overrides = {"components": {pipe_name: {"categories": "my_categories"}}}
         nlp2 = spacy.load(tmpdir, config=overrides)
-        assert nlp2.get_pipe("my_component").categories == "my_categories"
+        assert nlp2.get_pipe(pipe_name).categories == "my_categories"
 
 
 def test_issue5141(en_vocab):

spacy/tests/regression/test_issue7001-8000.py

@@ -0,0 +1,281 @@
+from spacy.cli.evaluate import print_textcats_auc_per_cat, print_prf_per_type
+from spacy.lang.en import English
+from spacy.training import Example
+from spacy.tokens.doc import Doc
+from spacy.vocab import Vocab
+from spacy.kb import KnowledgeBase
+from spacy.pipeline._parser_internals.arc_eager import ArcEager
+from spacy.util import load_config_from_str, load_config
+from spacy.cli.init_config import fill_config
+from thinc.api import Config
+from wasabi import msg
+
+from ..util import make_tempdir
+
+
+def test_issue7019():
+    scores = {"LABEL_A": 0.39829102, "LABEL_B": 0.938298329382, "LABEL_C": None}
+    print_textcats_auc_per_cat(msg, scores)
+    scores = {
+        "LABEL_A": {"p": 0.3420302, "r": 0.3929020, "f": 0.49823928932},
+        "LABEL_B": {"p": None, "r": None, "f": None},
+    }
+    print_prf_per_type(msg, scores, name="foo", type="bar")
+
+
+CONFIG_7029 = """
+[nlp]
+lang = "en"
+pipeline = ["tok2vec", "tagger"]
+
+[components]
+
+[components.tok2vec]
+factory = "tok2vec"
+
+[components.tok2vec.model]
+@architectures = "spacy.Tok2Vec.v1"
+
+[components.tok2vec.model.embed]
+@architectures = "spacy.MultiHashEmbed.v1"
+width = ${components.tok2vec.model.encode:width}
+attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
+rows = [5000,2500,2500,2500]
+include_static_vectors = false
+
+[components.tok2vec.model.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v1"
+width = 96
+depth = 4
+window_size = 1
+maxout_pieces = 3
+
+[components.tagger]
+factory = "tagger"
+
+[components.tagger.model]
+@architectures = "spacy.Tagger.v1"
+nO = null
+
+[components.tagger.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode:width}
+upstream = "*"
+"""
+
+
+def test_issue7029():
+    """Test that an empty document doesn't mess up an entire batch."""
+    TRAIN_DATA = [
+        ("I like green eggs", {"tags": ["N", "V", "J", "N"]}),
+        ("Eat blue ham", {"tags": ["V", "J", "N"]}),
+    ]
+    nlp = English.from_config(load_config_from_str(CONFIG_7029))
+    train_examples = []
+    for t in TRAIN_DATA:
+        train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+    optimizer = nlp.initialize(get_examples=lambda: train_examples)
+    for i in range(50):
+        losses = {}
+        nlp.update(train_examples, sgd=optimizer, losses=losses)
+    texts = ["first", "second", "third", "fourth", "and", "then", "some", ""]
+    docs1 = list(nlp.pipe(texts, batch_size=1))
+    docs2 = list(nlp.pipe(texts, batch_size=4))
+    assert [doc[0].tag_ for doc in docs1[:-1]] == [doc[0].tag_ for doc in docs2[:-1]]
+
+
+def test_issue7055():
+    """Test that fill-config doesn't turn sourced components into factories."""
+    source_cfg = {
+        "nlp": {"lang": "en", "pipeline": ["tok2vec", "tagger"]},
+        "components": {
+            "tok2vec": {"factory": "tok2vec"},
+            "tagger": {"factory": "tagger"},
+        },
+    }
+    source_nlp = English.from_config(source_cfg)
+    with make_tempdir() as dir_path:
+        # We need to create a loadable source pipeline
+        source_path = dir_path / "test_model"
+        source_nlp.to_disk(source_path)
+        base_cfg = {
+            "nlp": {"lang": "en", "pipeline": ["tok2vec", "tagger", "ner"]},
+            "components": {
+                "tok2vec": {"source": str(source_path)},
+                "tagger": {"source": str(source_path)},
+                "ner": {"factory": "ner"},
+            },
+        }
+        base_cfg = Config(base_cfg)
+        base_path = dir_path / "base.cfg"
+        base_cfg.to_disk(base_path)
+        output_path = dir_path / "config.cfg"
+        fill_config(output_path, base_path, silent=True)
+        filled_cfg = load_config(output_path)
+    assert filled_cfg["components"]["tok2vec"]["source"] == str(source_path)
+    assert filled_cfg["components"]["tagger"]["source"] == str(source_path)
+    assert filled_cfg["components"]["ner"]["factory"] == "ner"
+    assert "model" in filled_cfg["components"]["ner"]
+
+
+def test_issue7056():
+    """Test that the Unshift transition works properly, and doesn't cause
+    sentence segmentation errors."""
+    vocab = Vocab()
+    ae = ArcEager(
+        vocab.strings, ArcEager.get_actions(left_labels=["amod"], right_labels=["pobj"])
+    )
+    doc = Doc(vocab, words="Severe pain , after trauma".split())
+    state = ae.init_batch([doc])[0]
+    ae.apply_transition(state, "S")
+    ae.apply_transition(state, "L-amod")
+    ae.apply_transition(state, "S")
+    ae.apply_transition(state, "S")
+    ae.apply_transition(state, "S")
+    ae.apply_transition(state, "R-pobj")
+    ae.apply_transition(state, "D")
+    ae.apply_transition(state, "D")
+    ae.apply_transition(state, "D")
+    assert not state.eol()
+
+
+def test_partial_links():
+    # Test that having some entities on the doc without gold links, doesn't crash
+    TRAIN_DATA = [
+        (
+            "Russ Cochran his reprints include EC Comics.",
+            {
+                "links": {(0, 12): {"Q2146908": 1.0}},
+                "entities": [(0, 12, "PERSON")],
+                "sent_starts": [1, -1, 0, 0, 0, 0, 0, 0],
+            },
+        )
+    ]
+    nlp = English()
+    vector_length = 3
+    train_examples = []
+    for text, annotation in TRAIN_DATA:
+        doc = nlp(text)
+        train_examples.append(Example.from_dict(doc, annotation))
+
+    def create_kb(vocab):
+        # create artificial KB
+        mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
+        mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
+        mykb.add_alias("Russ Cochran", ["Q2146908"], [0.9])
+        return mykb
+
+    # Create and train the Entity Linker
+    entity_linker = nlp.add_pipe("entity_linker", last=True)
+    entity_linker.set_kb(create_kb)
+    optimizer = nlp.initialize(get_examples=lambda: train_examples)
+    for i in range(2):
+        losses = {}
+        nlp.update(train_examples, sgd=optimizer, losses=losses)
+
+    # adding additional components that are required for the entity_linker
+    nlp.add_pipe("sentencizer", first=True)
+    patterns = [
+        {"label": "PERSON", "pattern": [{"LOWER": "russ"}, {"LOWER": "cochran"}]},
+        {"label": "ORG", "pattern": [{"LOWER": "ec"}, {"LOWER": "comics"}]},
+    ]
+    ruler = nlp.add_pipe("entity_ruler", before="entity_linker")
+    ruler.add_patterns(patterns)
+
+    # this will run the pipeline on the examples and shouldn't crash
+    results = nlp.evaluate(train_examples)
+    assert "PERSON" in results["ents_per_type"]
+    assert "PERSON" in results["nel_f_per_type"]
+    assert "ORG" in results["ents_per_type"]
+    assert "ORG" not in results["nel_f_per_type"]
+
+
+def test_issue7065():
+    text = "Kathleen Battle sang in Mahler 's Symphony No. 8 at the Cincinnati Symphony Orchestra 's May Festival."
+    nlp = English()
+    nlp.add_pipe("sentencizer")
+    ruler = nlp.add_pipe("entity_ruler")
+    patterns = [
+        {
+            "label": "THING",
+            "pattern": [
+                {"LOWER": "symphony"},
+                {"LOWER": "no"},
+                {"LOWER": "."},
+                {"LOWER": "8"},
+            ],
+        }
+    ]
+    ruler.add_patterns(patterns)
+
+    doc = nlp(text)
+    sentences = [s for s in doc.sents]
+    assert len(sentences) == 2
+    sent0 = sentences[0]
+    ent = doc.ents[0]
+    assert ent.start < sent0.end < ent.end
+    assert sentences.index(ent.sent) == 0
+
+
+def test_issue7065_b():
+    # Test that the NEL doesn't crash when an entity crosses a sentence boundary
+    nlp = English()
+    vector_length = 3
+    nlp.add_pipe("sentencizer")
+    text = "Mahler 's Symphony No. 8 was beautiful."
+    entities = [(0, 6, "PERSON"), (10, 24, "WORK")]
+    links = {
+        (0, 6): {"Q7304": 1.0, "Q270853": 0.0},
+        (10, 24): {"Q7304": 0.0, "Q270853": 1.0},
+    }
+    sent_starts = [1, -1, 0, 0, 0, 0, 0, 0, 0]
+    doc = nlp(text)
+    example = Example.from_dict(
+        doc, {"entities": entities, "links": links, "sent_starts": sent_starts}
+    )
+    train_examples = [example]
+
+    def create_kb(vocab):
+        # create artificial KB
+        mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
+        mykb.add_entity(entity="Q270853", freq=12, entity_vector=[9, 1, -7])
+        mykb.add_alias(
+            alias="No. 8",
+            entities=["Q270853"],
+            probabilities=[1.0],
+        )
+        mykb.add_entity(entity="Q7304", freq=12, entity_vector=[6, -4, 3])
+        mykb.add_alias(
+            alias="Mahler",
+            entities=["Q7304"],
+            probabilities=[1.0],
+        )
+        return mykb
+
+    # Create the Entity Linker component and add it to the pipeline
+    entity_linker = nlp.add_pipe("entity_linker", last=True)
+    entity_linker.set_kb(create_kb)
+    # train the NEL pipe
+    optimizer = nlp.initialize(get_examples=lambda: train_examples)
+    for i in range(2):
+        losses = {}
+        nlp.update(train_examples, sgd=optimizer, losses=losses)
+
+    # Add a custom rule-based component to mimick NER
+    patterns = [
+        {"label": "PERSON", "pattern": [{"LOWER": "mahler"}]},
+        {
+            "label": "WORK",
+            "pattern": [
+                {"LOWER": "symphony"},
+                {"LOWER": "no"},
+                {"LOWER": "."},
+                {"LOWER": "8"},
+            ],
+        },
+    ]
+    ruler = nlp.add_pipe("entity_ruler", before="entity_linker")
+    ruler.add_patterns(patterns)
+    # test the trained model - this should not throw E148
+    doc = nlp(text)
+    assert doc

spacy/tests/regression/test_issue7019.py

@@ -1,12 +0,0 @@
-from spacy.cli.evaluate import print_textcats_auc_per_cat, print_prf_per_type
-from wasabi import msg
-
-
-def test_issue7019():
-    scores = {"LABEL_A": 0.39829102, "LABEL_B": 0.938298329382, "LABEL_C": None}
-    print_textcats_auc_per_cat(msg, scores)
-    scores = {
-        "LABEL_A": {"p": 0.3420302, "r": 0.3929020, "f": 0.49823928932},
-        "LABEL_B": {"p": None, "r": None, "f": None},
-    }
-    print_prf_per_type(msg, scores, name="foo", type="bar")

spacy/tests/regression/test_issue7029.py

@@ -1,66 +0,0 @@
-from spacy.lang.en import English
-from spacy.training import Example
-from spacy.util import load_config_from_str
-
-
-CONFIG = """
-[nlp]
-lang = "en"
-pipeline = ["tok2vec", "tagger"]
-
-[components]
-
-[components.tok2vec]
-factory = "tok2vec"
-
-[components.tok2vec.model]
-@architectures = "spacy.Tok2Vec.v1"
-
-[components.tok2vec.model.embed]
-@architectures = "spacy.MultiHashEmbed.v1"
-width = ${components.tok2vec.model.encode:width}
-attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
-rows = [5000,2500,2500,2500]
-include_static_vectors = false
-
-[components.tok2vec.model.encode]
-@architectures = "spacy.MaxoutWindowEncoder.v1"
-width = 96
-depth = 4
-window_size = 1
-maxout_pieces = 3
-
-[components.tagger]
-factory = "tagger"
-
-[components.tagger.model]
-@architectures = "spacy.Tagger.v1"
-nO = null
-
-[components.tagger.model.tok2vec]
-@architectures = "spacy.Tok2VecListener.v1"
-width = ${components.tok2vec.model.encode:width}
-upstream = "*"
-"""
-
-
-TRAIN_DATA = [
-    ("I like green eggs", {"tags": ["N", "V", "J", "N"]}),
-    ("Eat blue ham", {"tags": ["V", "J", "N"]}),
-]
-
-
-def test_issue7029():
-    """Test that an empty document doesn't mess up an entire batch."""
-    nlp = English.from_config(load_config_from_str(CONFIG))
-    train_examples = []
-    for t in TRAIN_DATA:
-        train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
-    optimizer = nlp.initialize(get_examples=lambda: train_examples)
-    for i in range(50):
-        losses = {}
-        nlp.update(train_examples, sgd=optimizer, losses=losses)
-    texts = ["first", "second", "third", "fourth", "and", "then", "some", ""]
-    docs1 = list(nlp.pipe(texts, batch_size=1))
-    docs2 = list(nlp.pipe(texts, batch_size=4))
-    assert [doc[0].tag_ for doc in docs1[:-1]] == [doc[0].tag_ for doc in docs2[:-1]]

spacy/tests/regression/test_issue7055.py

@@ -1,40 +0,0 @@
-from spacy.cli.init_config import fill_config
-from spacy.util import load_config
-from spacy.lang.en import English
-from thinc.api import Config
-
-from ..util import make_tempdir
-
-
-def test_issue7055():
-    """Test that fill-config doesn't turn sourced components into factories."""
-    source_cfg = {
-        "nlp": {"lang": "en", "pipeline": ["tok2vec", "tagger"]},
-        "components": {
-            "tok2vec": {"factory": "tok2vec"},
-            "tagger": {"factory": "tagger"},
-        },
-    }
-    source_nlp = English.from_config(source_cfg)
-    with make_tempdir() as dir_path:
-        # We need to create a loadable source pipeline
-        source_path = dir_path / "test_model"
-        source_nlp.to_disk(source_path)
-        base_cfg = {
-            "nlp": {"lang": "en", "pipeline": ["tok2vec", "tagger", "ner"]},
-            "components": {
-                "tok2vec": {"source": str(source_path)},
-                "tagger": {"source": str(source_path)},
-                "ner": {"factory": "ner"},
-            },
-        }
-        base_cfg = Config(base_cfg)
-        base_path = dir_path / "base.cfg"
-        base_cfg.to_disk(base_path)
-        output_path = dir_path / "config.cfg"
-        fill_config(output_path, base_path, silent=True)
-        filled_cfg = load_config(output_path)
-    assert filled_cfg["components"]["tok2vec"]["source"] == str(source_path)
-    assert filled_cfg["components"]["tagger"]["source"] == str(source_path)
-    assert filled_cfg["components"]["ner"]["factory"] == "ner"
-    assert "model" in filled_cfg["components"]["ner"]

spacy/tests/regression/test_issue7056.py

@@ -1,24 +0,0 @@
-from spacy.tokens.doc import Doc
-from spacy.vocab import Vocab
-from spacy.pipeline._parser_internals.arc_eager import ArcEager
-
-
-def test_issue7056():
-    """Test that the Unshift transition works properly, and doesn't cause
-    sentence segmentation errors."""
-    vocab = Vocab()
-    ae = ArcEager(
-        vocab.strings, ArcEager.get_actions(left_labels=["amod"], right_labels=["pobj"])
-    )
-    doc = Doc(vocab, words="Severe pain , after trauma".split())
-    state = ae.init_batch([doc])[0]
-    ae.apply_transition(state, "S")
-    ae.apply_transition(state, "L-amod")
-    ae.apply_transition(state, "S")
-    ae.apply_transition(state, "S")
-    ae.apply_transition(state, "S")
-    ae.apply_transition(state, "R-pobj")
-    ae.apply_transition(state, "D")
-    ae.apply_transition(state, "D")
-    ae.apply_transition(state, "D")
-    assert not state.eol()

spacy/tests/regression/test_issue7062.py

@@ -1,54 +0,0 @@
-from spacy.kb import KnowledgeBase
-from spacy.training import Example
-from spacy.lang.en import English
-
-
-# fmt: off
-TRAIN_DATA = [
-    ("Russ Cochran his reprints include EC Comics.",
-        {"links": {(0, 12): {"Q2146908": 1.0}},
-         "entities": [(0, 12, "PERSON")],
-         "sent_starts": [1, -1, 0, 0, 0, 0, 0, 0]})
-]
-# fmt: on
-
-
-def test_partial_links():
-    # Test that having some entities on the doc without gold links, doesn't crash
-    nlp = English()
-    vector_length = 3
-    train_examples = []
-    for text, annotation in TRAIN_DATA:
-        doc = nlp(text)
-        train_examples.append(Example.from_dict(doc, annotation))
-
-    def create_kb(vocab):
-        # create artificial KB
-        mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
-        mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
-        mykb.add_alias("Russ Cochran", ["Q2146908"], [0.9])
-        return mykb
-
-    # Create and train the Entity Linker
-    entity_linker = nlp.add_pipe("entity_linker", last=True)
-    entity_linker.set_kb(create_kb)
-    optimizer = nlp.initialize(get_examples=lambda: train_examples)
-    for i in range(2):
-        losses = {}
-        nlp.update(train_examples, sgd=optimizer, losses=losses)
-
-    # adding additional components that are required for the entity_linker
-    nlp.add_pipe("sentencizer", first=True)
-    patterns = [
-        {"label": "PERSON", "pattern": [{"LOWER": "russ"}, {"LOWER": "cochran"}]},
-        {"label": "ORG", "pattern": [{"LOWER": "ec"}, {"LOWER": "comics"}]},
-    ]
-    ruler = nlp.add_pipe("entity_ruler", before="entity_linker")
-    ruler.add_patterns(patterns)
-
-    # this will run the pipeline on the examples and shouldn't crash
-    results = nlp.evaluate(train_examples)
-    assert "PERSON" in results["ents_per_type"]
-    assert "PERSON" in results["nel_f_per_type"]
-    assert "ORG" in results["ents_per_type"]
-    assert "ORG" not in results["nel_f_per_type"]

spacy/tests/regression/test_issue7065.py

@@ -1,97 +0,0 @@
-from spacy.kb import KnowledgeBase
-from spacy.lang.en import English
-from spacy.training import Example
-
-
-def test_issue7065():
-    text = "Kathleen Battle sang in Mahler 's Symphony No. 8 at the Cincinnati Symphony Orchestra 's May Festival."
-    nlp = English()
-    nlp.add_pipe("sentencizer")
-    ruler = nlp.add_pipe("entity_ruler")
-    patterns = [
-        {
-            "label": "THING",
-            "pattern": [
-                {"LOWER": "symphony"},
-                {"LOWER": "no"},
-                {"LOWER": "."},
-                {"LOWER": "8"},
-            ],
-        }
-    ]
-    ruler.add_patterns(patterns)
-
-    doc = nlp(text)
-    sentences = [s for s in doc.sents]
-    assert len(sentences) == 2
-    sent0 = sentences[0]
-    ent = doc.ents[0]
-    assert ent.start < sent0.end < ent.end
-    assert sentences.index(ent.sent) == 0
-
-
-def test_issue7065_b():
-    # Test that the NEL doesn't crash when an entity crosses a sentence boundary
-    nlp = English()
-    vector_length = 3
-    nlp.add_pipe("sentencizer")
-
-    text = "Mahler 's Symphony No. 8 was beautiful."
-    entities = [(0, 6, "PERSON"), (10, 24, "WORK")]
-    links = {
-        (0, 6): {"Q7304": 1.0, "Q270853": 0.0},
-        (10, 24): {"Q7304": 0.0, "Q270853": 1.0},
-    }
-    sent_starts = [1, -1, 0, 0, 0, 0, 0, 0, 0]
-    doc = nlp(text)
-    example = Example.from_dict(
-        doc, {"entities": entities, "links": links, "sent_starts": sent_starts}
-    )
-    train_examples = [example]
-
-    def create_kb(vocab):
-        # create artificial KB
-        mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
-        mykb.add_entity(entity="Q270853", freq=12, entity_vector=[9, 1, -7])
-        mykb.add_alias(
-            alias="No. 8",
-            entities=["Q270853"],
-            probabilities=[1.0],
-        )
-        mykb.add_entity(entity="Q7304", freq=12, entity_vector=[6, -4, 3])
-        mykb.add_alias(
-            alias="Mahler",
-            entities=["Q7304"],
-            probabilities=[1.0],
-        )
-        return mykb
-
-    # Create the Entity Linker component and add it to the pipeline
-    entity_linker = nlp.add_pipe("entity_linker", last=True)
-    entity_linker.set_kb(create_kb)
-
-    # train the NEL pipe
-    optimizer = nlp.initialize(get_examples=lambda: train_examples)
-    for i in range(2):
-        losses = {}
-        nlp.update(train_examples, sgd=optimizer, losses=losses)
-
-    # Add a custom rule-based component to mimick NER
-    patterns = [
-        {"label": "PERSON", "pattern": [{"LOWER": "mahler"}]},
-        {
-            "label": "WORK",
-            "pattern": [
-                {"LOWER": "symphony"},
-                {"LOWER": "no"},
-                {"LOWER": "."},
-                {"LOWER": "8"},
-            ],
-        },
-    ]
-    ruler = nlp.add_pipe("entity_ruler", before="entity_linker")
-    ruler.add_patterns(patterns)
-
-    # test the trained model - this should not throw E148
-    doc = nlp(text)
-    assert doc

spacy/tests/serialize/test_serialize_pipeline.py

@@ -60,12 +60,6 @@ def taggers(en_vocab):
 
 @pytest.mark.parametrize("Parser", test_parsers)
 def test_serialize_parser_roundtrip_bytes(en_vocab, Parser):
-    config = {
-        "update_with_oracle_cut_size": 100,
-        "beam_width": 1,
-        "beam_update_prob": 1.0,
-        "beam_density": 0.0,
-    }
     cfg = {"model": DEFAULT_PARSER_MODEL}
     model = registry.resolve(cfg, validate=True)["model"]
     parser = Parser(en_vocab, model)

spacy/tests/test_cli.py

@@ -440,7 +440,7 @@ def test_init_config(lang, pipeline, optimize, pretraining):
     assert isinstance(config, Config)
     if pretraining:
         config["paths"]["raw_text"] = "my_data.jsonl"
-    nlp = load_model_from_config(config, auto_fill=True)
+    load_model_from_config(config, auto_fill=True)
 
 
 def test_model_recommendations():

spacy/tests/test_models.py

@@ -211,7 +211,7 @@ def test_empty_docs(model_func, kwargs):
 
 
 def test_init_extract_spans():
-    model = extract_spans().initialize()
+    extract_spans().initialize()
 
 
 def test_extract_spans_span_indices():