diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 7ad1b27b3..ac80b8a10 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -28,7 +28,7 @@ jobs:
         inputs:
           versionSpec: "3.7"
       - script: |
-          pip install flake8==3.5.0
+          pip install flake8==3.9.2
           python -m flake8 spacy --count --select=E901,E999,F821,F822,F823 --show-source --statistics
         displayName: "flake8"
 
diff --git a/requirements.txt b/requirements.txt
index a4d76756c..c38380c54 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -26,5 +26,5 @@ cython>=0.25,<3.0
 pytest>=5.2.0
 pytest-timeout>=1.3.0,<2.0.0
 mock>=2.0.0,<3.0.0
-flake8>=3.5.0,<3.6.0
+flake8>=3.8.0,<3.10.0
 hypothesis>=3.27.0,<7.0.0
diff --git a/spacy/ml/models/entity_linker.py b/spacy/ml/models/entity_linker.py
index 99bb85f32..645b67c62 100644
--- a/spacy/ml/models/entity_linker.py
+++ b/spacy/ml/models/entity_linker.py
@@ -6,6 +6,7 @@ from thinc.api import Model, Maxout, Linear
 from ...util import registry
 from ...kb import KnowledgeBase, Candidate, get_candidates
 from ...vocab import Vocab
+from ...tokens import Span
 
 
 @registry.architectures("spacy.EntityLinker.v1")
@@ -44,5 +45,5 @@ def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
 
 
 @registry.misc("spacy.CandidateGenerator.v1")
-def create_candidates() -> Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]:
+def create_candidates() -> Callable[[KnowledgeBase, Span], Iterable[Candidate]]:
     return get_candidates
diff --git a/spacy/pipeline/entity_linker.py b/spacy/pipeline/entity_linker.py
index a8c80df05..302a1bc10 100644
--- a/spacy/pipeline/entity_linker.py
+++ b/spacy/pipeline/entity_linker.py
@@ -9,7 +9,7 @@ import warnings
 
 from ..kb import KnowledgeBase, Candidate
 from ..ml import empty_kb
-from ..tokens import Doc
+from ..tokens import Doc, Span
 from .pipe import deserialize_config
 from .trainable_pipe import TrainablePipe
 from ..language import Language
@@ -67,7 +67,7 @@ def make_entity_linker(
     incl_prior: bool,
     incl_context: bool,
     entity_vector_length: int,
-    get_candidates: Callable[[KnowledgeBase, "Span"], Iterable[Candidate]],
+    get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
 ):
     """Construct an EntityLinker component.
 
@@ -114,7 +114,7 @@ class EntityLinker(TrainablePipe):
         incl_prior: bool,
         incl_context: bool,
         entity_vector_length: int,
-        get_candidates: Callable[[KnowledgeBase, "Span"], Iterable[Candidate]],
+        get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
     ) -> None:
         """Initialize an entity linker.
 
@@ -127,7 +127,7 @@ class EntityLinker(TrainablePipe):
         incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
         incl_context (bool): Whether or not to include the local context in the model.
         entity_vector_length (int): Size of encoding vectors in the KB.
-        get_candidates (Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]): Function that
+        get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
             produces a list of candidates, given a certain knowledge base and a textual mention.
 
         DOCS: https://spacy.io/api/entitylinker#init
diff --git a/spacy/schemas.py b/spacy/schemas.py
index 92315399d..992e17d70 100644
--- a/spacy/schemas.py
+++ b/spacy/schemas.py
@@ -4,7 +4,7 @@ from enum import Enum
 from pydantic import BaseModel, Field, ValidationError, validator, create_model
 from pydantic import StrictStr, StrictInt, StrictFloat, StrictBool
 from pydantic.main import ModelMetaclass
-from thinc.api import Optimizer, ConfigValidationError
+from thinc.api import Optimizer, ConfigValidationError, Model
 from thinc.config import Promise
 from collections import defaultdict
 import inspect
@@ -17,6 +17,7 @@ if TYPE_CHECKING:
     # This lets us add type hints for mypy etc. without causing circular imports
     from .language import Language  # noqa: F401
     from .training import Example  # noqa: F401
+    from .vocab import Vocab  # noqa: F401
 
 
 # fmt: off
@@ -354,7 +355,7 @@ class ConfigSchemaPretrain(BaseModel):
     batcher: Batcher = Field(..., title="Batcher for the training data")
     component: str = Field(..., title="Component to find the layer to pretrain")
     layer: str = Field(..., title="Layer to pretrain. Whole model if empty.")
-    objective: Callable[["Vocab", "Model"], "Model"] = Field(..., title="A function that creates the pretraining objective.")
+    objective: Callable[["Vocab", Model], Model] = Field(..., title="A function that creates the pretraining objective.")
     # fmt: on
 
     class Config:
diff --git a/spacy/tests/serialize/test_serialize_kb.py b/spacy/tests/serialize/test_serialize_kb.py
index fb04d31a3..1e0ae3c76 100644
--- a/spacy/tests/serialize/test_serialize_kb.py
+++ b/spacy/tests/serialize/test_serialize_kb.py
@@ -3,6 +3,7 @@ from typing import Callable
 from spacy import util
 from spacy.util import ensure_path, registry, load_model_from_config
 from spacy.kb import KnowledgeBase
+from spacy.vocab import Vocab
 from thinc.api import Config
 
 from ..util import make_tempdir
@@ -111,7 +112,7 @@ def test_serialize_subclassed_kb():
     @registry.misc("spacy.CustomKB.v1")
     def custom_kb(
         entity_vector_length: int, custom_field: int
-    ) -> Callable[["Vocab"], KnowledgeBase]:
+    ) -> Callable[[Vocab], KnowledgeBase]:
         def custom_kb_factory(vocab):
             kb = SubKnowledgeBase(
                 vocab=vocab,