diff --git a/.cruft.json b/.cruft.json
index 67f6cef3..990d73f1 100644
--- a/.cruft.json
+++ b/.cruft.json
@@ -1,6 +1,6 @@
 {
   "template": "https://github.com/KennethEnevoldsen/swift-python-cookiecutter",
-  "commit": "e02068889310225ea4f65ea0b203c2949e1597a9",
+  "commit": "85413085032f305896da8bad287a83d53fb0b196",
   "checkout": null,
   "context": {
     "cookiecutter": {
diff --git a/.github/workflows/static_type_checks.yml b/.github/workflows/static_type_checks.yml
index 9975945a..019338c3 100644
--- a/.github/workflows/static_type_checks.yml
+++ b/.github/workflows/static_type_checks.yml
@@ -1,3 +1,10 @@
+# THIS ACTION WILL:
+  # 1. Install dependencies
+  # 2. Run static type checker
+
+# SETUP:
+  # None required except for the Makefile
+
 name: static_type_checks
 
 on:
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 3a1814c5..a06f89fe 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -1,9 +1,11 @@
-# This workflow will:
-# 1) install Python dependencies
-# 2) run make test
+# THIS ACTION WILL:
+  # 1) install Python dependencies
+  # 2) run make test
 
+# SETUP:
+  # None required except for the Makefile
 
-name: Tests
+name: test
 on:
   push:
     branches: [main]
@@ -11,7 +13,7 @@ on:
     branches: [main]
 
 jobs:
-  pytest:
+  test:
     runs-on: ${{ matrix.os }}
     permissions:
       contents: read
diff --git a/docs/evaluation/datasets.py b/docs/evaluation/datasets.py
index 418aea4c..9521da69 100644
--- a/docs/evaluation/datasets.py
+++ b/docs/evaluation/datasets.py
@@ -1,5 +1,7 @@
+from __future__ import annotations
+
 import random
-from typing import Any, Dict, List
+from typing import Any
 
 import augmenty
 import catalogue
@@ -15,7 +17,7 @@
 
 
 @datasets.register("dane")
-def dane() -> Dict[str, List[Example]]:
+def dane() -> dict[str, list[Example]]:
     from dacy.datasets import dane as _dane
 
     train, dev, test = _dane(splits=["train", "dev", "test"])  # type: ignore
@@ -34,7 +36,7 @@ def augment_dataset(
     augmenters: dict,
     n_rep: int = 20,
     split: str = "test",
-) -> List[Example]:
+) -> list[Example]:
     # ensure seed
     random.seed(42)
     np.random.seed(42)
@@ -63,17 +65,17 @@ def augment_dataset(
 
 
 @datasets.register("gender_bias_dane")
-def dane_gender_bias() -> Dict[str, List[Example]]:
+def dane_gender_bias() -> dict[str, list[Example]]:
     return {"test": augment_dataset("dane", augmenters=get_gender_bias_augmenters())}
 
 
 @datasets.register("robustness_dane")
-def dane_robustness() -> Dict[str, List[Example]]:
+def dane_robustness() -> dict[str, list[Example]]:
     return {"test": augment_dataset("dane", augmenters=get_robustness_augmenters())}
 
 
 @datasets.register("dansk")
-def dansk(**kwargs: Any) -> Dict[str, List[Example]]:
+def dansk(**kwargs: Any) -> dict[str, list[Example]]:
     splits = ["train", "dev", "test"]
 
     if not Doc.has_extension("meta"):
@@ -81,7 +83,7 @@ def dansk(**kwargs: Any) -> Dict[str, List[Example]]:
 
     nlp = spacy.blank("da")
 
-    def convert_to_doc(example: Dict) -> Doc:
+    def convert_to_doc(example: dict) -> Doc:
         doc = Doc(nlp.vocab).from_json(example)
         # set metadata
         for k in ["dagw_source", "dagw_domain", "dagw_source_full"]:
diff --git a/docs/evaluation/utils.py b/docs/evaluation/utils.py
index fb78287f..e47b696a 100644
--- a/docs/evaluation/utils.py
+++ b/docs/evaluation/utils.py
@@ -1,7 +1,7 @@
 import json
 import random
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional
+from typing import Any, Callable, Optional
 
 import numpy as np
 import pandas as pd
@@ -15,11 +15,11 @@
 
 
 def bootstrap(
-    examples: List[Example],
+    examples: list[Example],
     n_rep: int = 100,
     n_samples: Optional[int] = None,
     getter: Optional[Callable] = None,
-) -> List[Dict[str, Any]]:
+) -> list[dict[str, Any]]:
     random.seed(42)
     scorer = Scorer()
     scores = []
@@ -35,7 +35,7 @@ def bootstrap(
     return scores
 
 
-def compute_mean_and_ci(scores: List[Dict[str, Any]]) -> Dict[str, Any]:
+def compute_mean_and_ci(scores: list[dict[str, Any]]) -> dict[str, Any]:
     ent_f = [score["ents_f"] for score in scores]
     # filter out None
     ent_f = [x for x in ent_f if x is not None]
@@ -116,7 +116,7 @@ def doc_from_json(json_obj: dict, nlp: Language) -> Doc:
 
 def predictions_to_disk(
     save_path: Path,
-    examples: List[Example],
+    examples: list[Example],
     mdl_name: str,
     time_in_seconds: float,
 ) -> dict:
@@ -199,7 +199,7 @@ def apply_models(
 
 
 def create_dataframe(
-    examples: List[Example],
+    examples: list[Example],
     mdl_name: str,
     decimals: int = 1,
     n_rep: int = 100,
@@ -212,7 +212,7 @@ def create_dataframe(
         "Models": mdl_name,
     }
 
-    def score_to_string(score: Dict[str, Any], decimals: int = 1) -> str:
+    def score_to_string(score: dict[str, Any], decimals: int = 1) -> str:
         if score["mean"] == 0:
             return " "
         return f"{100*score['mean']:.{decimals}f} ({100*score['ci'][0]:.{decimals}f}, {100*score['ci'][1]:.{decimals}f})"
diff --git a/makefile b/makefile
index 4c3c83a3..2d6286f9 100644
--- a/makefile
+++ b/makefile
@@ -9,9 +9,9 @@ static-type-check:
 lint:
 	@echo "--- 🧹 Running linters ---"
 	ruff format .  								# running ruff formatting
-	ruff src/ --fix  						    # running ruff linting
-	ruff tests/ --fix
-	ruff docs/conf.py --fix
+	ruff check src/ --fix  						    # running ruff linting
+	ruff check tests/ --fix
+	ruff check docs/conf.py --fix
 
 test:
 	@echo "--- 🧪 Running tests ---"
diff --git a/papers/DaCy-A-Unified-Framework-for-Danish-NLP/apply_fns/apply_fn_daluke.py b/papers/DaCy-A-Unified-Framework-for-Danish-NLP/apply_fns/apply_fn_daluke.py
index 987c94df..c0654748 100644
--- a/papers/DaCy-A-Unified-Framework-for-Danish-NLP/apply_fns/apply_fn_daluke.py
+++ b/papers/DaCy-A-Unified-Framework-for-Danish-NLP/apply_fns/apply_fn_daluke.py
@@ -1,5 +1,5 @@
 ### pip install daluke==0.0.5
-from typing import Iterable, List
+from typing import Iterable
 
 from daluke import AutoNERDaLUKE, predict_ner
 from spacy.lang.da import Danish
@@ -18,7 +18,7 @@ def apply_daluke(
     examples: Iterable[Example],
     use_spacy: bool = True,
     batch_size: int = 16,
-) -> List[Example]:
+) -> list[Example]:
     docs_y, sentences = list(), list()
     for example in examples:
         # Tokenization using spacy or nltk
diff --git a/papers/DaCy-A-Unified-Framework-for-Danish-NLP/apply_fns/apply_fn_nerda.py b/papers/DaCy-A-Unified-Framework-for-Danish-NLP/apply_fns/apply_fn_nerda.py
index caf2f3cb..46d26f84 100644
--- a/papers/DaCy-A-Unified-Framework-for-Danish-NLP/apply_fns/apply_fn_nerda.py
+++ b/papers/DaCy-A-Unified-Framework-for-Danish-NLP/apply_fns/apply_fn_nerda.py
@@ -1,6 +1,6 @@
 # to download the danlp and nerda you will have to set up a certificate:
 import ssl
-from typing import Iterable, List
+from typing import Iterable, list
 
 from NERDA.precooked import DA_BERT_ML
 from spacy.lang.da import Danish
@@ -17,7 +17,7 @@
 nlp_da = Danish()
 
 
-def apply_nerda(examples: Iterable[Example], use_spacy: bool = True) -> List[Example]:
+def apply_nerda(examples: Iterable[Example], use_spacy: bool = True) -> list[Example]:
     sentences = []
     docs_y = []
     for example in examples:
diff --git a/papers/DaCy-A-Unified-Framework-for-Danish-NLP/apply_fns/apply_fn_utils.py b/papers/DaCy-A-Unified-Framework-for-Danish-NLP/apply_fns/apply_fn_utils.py
index d88200b8..9b6aaede 100644
--- a/papers/DaCy-A-Unified-Framework-for-Danish-NLP/apply_fns/apply_fn_utils.py
+++ b/papers/DaCy-A-Unified-Framework-for-Danish-NLP/apply_fns/apply_fn_utils.py
@@ -1,4 +1,4 @@
-from typing import Callable, Iterable, List
+from typing import Callable, Iterable
 
 from spacy.tokens import Doc, Span
 from spacy.training import Example
@@ -12,12 +12,12 @@ def no_misc_getter(doc, attr):
         yield span
 
 
-def add_iob(doc: Doc, iob: List[str]) -> Doc:
+def add_iob(doc: Doc, iob: list[str]) -> Doc:
     """Add iob tags to Doc.
 
     Args:
         doc (Doc): A SpaCy doc
-        iob (List[str]): a list of tokens on the IOB format
+        iob (list[str]): a list of tokens on the IOB format
 
     Returns:
         Doc: A doc with the spans to the new IOB
diff --git a/pyproject.toml b/pyproject.toml
index 1e0d4055..20be51b5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -48,11 +48,7 @@ repository = "https://github.com/centre-for-humanities-computing/DaCy"
 file = "LICENSE"
 name = "Apache License 2.0"
 [project.optional-dependencies]
-dev = [
-  "cruft>=2.0.0",
-  "pyright>=1.1.339",
-  "ruff>=0.0.270",
-]
+dev = ["cruft>=2.0.0", "pyright>=1.1.339", "ruff==0.7.1"]
 tests = ["pytest>=7.1.2", "pytest-cov>=3.0.0", "pytest-instafail>=0.4.2"]
 docs = [
   "sphinx==5.3.0",
@@ -110,6 +106,40 @@ pythonPlatform = "Darwin"
 
 [tool.ruff]
 # extend-include = ["*.ipynb"]
+
+# Exclude a variety of commonly ignored directories.
+exclude = [
+  ".bzr",
+  ".direnv",
+  ".eggs",
+  ".git",
+  ".hg",
+  ".nox",
+  ".pants.d",
+  ".pytype",
+  ".ruff_cache",
+  ".svn",
+  ".tox",
+  ".venv",
+  "__pypackages__",
+  "_build",
+  "buck-out",
+  "build",
+  "dist",
+  "node_modules",
+  "venv",
+  "__init__.py",
+  ".env",
+  "__pycache__",
+  "dev/**",
+  "training/main/**",
+  "training/ner_fine_grained/**",
+  "papers/DaCy-A-Unified-Framework-for-Danish-NLP/**",
+  "docs/performance_testing_utils/**",
+]
+target-version = "py39"
+
+[tool.ruff.lint]
 # Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
 select = [
   "A",
@@ -150,54 +180,23 @@ ignore = [
   "ANN202",
   "COM812",
 ]
-ignore-init-module-imports = true
 # Allow autofix for all enabled rules (when `--fix`) is provided.
 unfixable = ["ERA"]
-# Exclude a variety of commonly ignored directories.
-exclude = [
-  ".bzr",
-  ".direnv",
-  ".eggs",
-  ".git",
-  ".hg",
-  ".nox",
-  ".pants.d",
-  ".pytype",
-  ".ruff_cache",
-  ".svn",
-  ".tox",
-  ".venv",
-  "__pypackages__",
-  "_build",
-  "buck-out",
-  "build",
-  "dist",
-  "node_modules",
-  "venv",
-  "__init__.py",
-  ".venv",
-  ".env",
-  ".git",
-  "__pycache__",
-  "dev/**",
-  "training/main/**",
-  "training/ner_fine_grained/**",
-  "papers/DaCy-A-Unified-Framework-for-Danish-NLP/**",
-  "docs/performance_testing_utils/**",
-]
+
+
 # Allow unused variables when underscore-prefixed.
 dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
-target-version = "py38"
+
 
 [tool.ruff.lint.pydocstyle]
 convention = "google"
 
-[tool.ruff.flake8-annotations]
+[tool.ruff.lint.flake8-annotations]
 mypy-init-return = true
 suppress-none-returning = true
 
 
-[tool.ruff.mccabe]
+[tool.ruff.lint.mccabe]
 # Unlike Flake8, default to a complexity level of 10.
 max-complexity = 10
 
diff --git a/src/dacy/datasets/dane.py b/src/dacy/datasets/dane.py
index f851caae..088a85e1 100644
--- a/src/dacy/datasets/dane.py
+++ b/src/dacy/datasets/dane.py
@@ -5,7 +5,7 @@
 import sys
 from os import PathLike
 from pathlib import Path
-from typing import List, Optional, Union
+from typing import Optional, Union
 
 from spacy.training.corpus import Corpus
 
@@ -15,19 +15,19 @@
 
 def dane(  # noqa
     save_path: Optional[PathLike] = None,  # type: ignore
-    splits: List[str] = ["train", "dev", "test"],  # noqa  # type: ignore
+    splits: list[str] = ["train", "dev", "test"],  # noqa  # type: ignore
     redownload: bool = False,
     n_sents: int = 1,
     open_unverified_connection: bool = False,
     **kwargs,  # noqa
-) -> Union[List[Corpus], Corpus]:  # type: ignore
+) -> Union[list[Corpus], Corpus]:  # type: ignore
     """Reads the DaNE dataset as a spacy Corpus.
 
     Args:
         save_path (str, optional): Path to the DaNE dataset If it does not contain the
             dataset it is downloaded to the folder. Defaults to None corresponding to
             dacy.where_is_my_dacy() in the datasets subfolder.
-        splits (List[str], optional): Which splits of the dataset should be returned.
+        splits (list[str], optional): Which splits of the dataset should be returned.
             Possible options include "train", "dev", "test", "all". Defaults to
             ["train", "dev", "test"].
         redownload (bool, optional): Should the dataset be redownloaded. Defaults to
@@ -40,7 +40,7 @@ def dane(  # noqa
             whether it already exists. Defaults to False.
 
     Returns:
-        Union[List[Corpus], Corpus]: Returns a SpaCy corpus or a list thereof.
+        Union[list[Corpus], Corpus]: Returns a SpaCy corpus or a list thereof.
 
     Example:
         >>> from dacy.datasets import dane
diff --git a/src/dacy/datasets/names.py b/src/dacy/datasets/names.py
index 4878fb0e..9c3a1ef2 100644
--- a/src/dacy/datasets/names.py
+++ b/src/dacy/datasets/names.py
@@ -1,7 +1,7 @@
 """Helper functions for loading name dictionaries for person augmentation."""
 
 import os
-from typing import Dict, List, Optional
+from typing import Optional
 
 import pandas as pd
 
@@ -11,7 +11,7 @@ def load_names(
     ethnicity: Optional[str] = None,  # type: ignore
     gender: Optional[str] = None,  # type: ignore
     min_prop_gender: float = 0,
-) -> Dict[str, List[str]]:  # type: ignore
+) -> dict[str, list[str]]:  # type: ignore
     """Loads the names lookup table. Danish are from Danmarks statistik (2021).
     Muslim names are from Meldgaard (2005),
     https://nors.ku.dk/publikationer/webpublikationer/muslimske_fornavne/.
@@ -30,7 +30,7 @@ def load_names(
             gender is set. Defaults to 0.
 
     Returns:
-        Dict[str, List[str]]: A dictionary of names containing the keys
+        dict[str, list[str]]: A dictionary of names containing the keys
             "first_name" and "last_name".
     """
     path = os.path.join(  # noqa
@@ -64,11 +64,11 @@ def load_names(
     }
 
 
-def muslim_names() -> Dict[str, List[str]]:  # type: ignore
+def muslim_names() -> dict[str, list[str]]:  # type: ignore
     """Returns a dictionary of Muslim names.
 
     Returns:
-        Dict[str, List[str]]: A dictionary of Muslim names containing the keys
+        dict[str, list[str]]: A dictionary of Muslim names containing the keys
             "first_name" and "last_name". The list is derived from Meldgaard (2005),
             https://nors.ku.dk/publikationer/webpublikationer/muslimske_fornavne/.
 
@@ -81,11 +81,11 @@ def muslim_names() -> Dict[str, List[str]]:  # type: ignore
     return load_names(ethnicity="muslim")
 
 
-def danish_names() -> Dict[str, List[str]]:  # type: ignore
+def danish_names() -> dict[str, list[str]]:  # type: ignore
     """Returns a dictionary of Danish names.
 
     Returns:
-        Dict[str, List[str]]: A dictionary of Danish names containing the keys
+        dict[str, list[str]]: A dictionary of Danish names containing the keys
         "first_name" and "last_name". The list is derived from Danmarks statistik
         (2021).
 
@@ -98,11 +98,11 @@ def danish_names() -> Dict[str, List[str]]:  # type: ignore
     return load_names(ethnicity="danish")
 
 
-def female_names() -> Dict[str, List[str]]:  # type: ignore
+def female_names() -> dict[str, list[str]]:  # type: ignore
     """Returns a dictionary of Danish female names.
 
     Returns:
-        Dict[str, List[str]]: A dictionary of names containing the keys "first_name"
+        dict[str, list[str]]: A dictionary of names containing the keys "first_name"
             and "last_name". The list is derived from Danmarks statistik (2021).
 
     Example:
@@ -114,11 +114,11 @@ def female_names() -> Dict[str, List[str]]:  # type: ignore
     return load_names(ethnicity="danish", gender="female", min_prop_gender=0.5)
 
 
-def male_names() -> Dict[str, List[str]]:  # type: ignore
+def male_names() -> dict[str, list[str]]:  # type: ignore
     """Returns a dictionary of Danish male names.
 
     Returns:
-        Dict[str, List[str]]: A dictionary of names containing the keys "first_name"
+        dict[str, list[str]]: A dictionary of names containing the keys "first_name"
             and "last_name". The list is derived from Danmarks statistik (2021).
 
     Example:
diff --git a/src/dacy/download.py b/src/dacy/download.py
index 7f8a7efe..0713f2da 100644
--- a/src/dacy/download.py
+++ b/src/dacy/download.py
@@ -1,4 +1,5 @@
 """Functions for downloading DaCy models."""
+
 import os
 from importlib.metadata import version
 from pathlib import Path
diff --git a/src/dacy/hate_speech/wrapped_models.py b/src/dacy/hate_speech/wrapped_models.py
index 86383ea0..29168aed 100644
--- a/src/dacy/hate_speech/wrapped_models.py
+++ b/src/dacy/hate_speech/wrapped_models.py
@@ -1,4 +1,4 @@
-from typing import Callable, List, Optional
+from typing import Callable, Optional
 from warnings import warn
 
 from spacy.lang.da import Danish
@@ -75,12 +75,12 @@
 def make_offensive_transformer(
     nlp: Language,
     name: str,
-    model: Model[List[Doc], FullTransformerBatch],
-    set_extra_annotations: Callable[[List[Doc], FullTransformerBatch], None],  # type: ignore
+    model: Model[list[Doc], FullTransformerBatch],
+    set_extra_annotations: Callable[[list[Doc], FullTransformerBatch], None],  # type: ignore
     max_batch_items: int,
     doc_extension_trf_data: str,
     doc_extension_prediction: str,
-    labels: List[str],  # type: ignore
+    labels: list[str],  # type: ignore
 ) -> SequenceClassificationTransformer:
     if not Doc.has_extension("is_offensive"):
         warn(
diff --git a/src/dacy/load.py b/src/dacy/load.py
index 52cedb6e..0d7e2a0d 100644
--- a/src/dacy/load.py
+++ b/src/dacy/load.py
@@ -1,4 +1,5 @@
 """Functionality for loading and locating DaCy models."""
+
 import warnings
 from pathlib import Path
 from typing import Any, Union
diff --git a/src/dacy/score/input_length.py b/src/dacy/score/input_length.py
index 32007a70..b49568d9 100644
--- a/src/dacy/score/input_length.py
+++ b/src/dacy/score/input_length.py
@@ -1,6 +1,7 @@
 """Contains functions for testing the performance of models on varying input
 length."""
-from typing import Callable, List, Union
+
+from typing import Callable, Union
 
 import pandas as pd
 from wasabi import msg
@@ -10,11 +11,11 @@
 
 
 def n_sents_score(
-    n_sents: Union[int, List[int]],  # type: ignore
+    n_sents: Union[int, list[int]],  # type: ignore
     apply_fn: Callable,  # type: ignore
     dataset: str = "dane",
     split: str = "test",
-    score_fn: List[Union[str, Callable]] = ["token", "pos", "ents", "dep"],  # noqa # type: ignore
+    score_fn: list[Union[str, Callable]] = ["token", "pos", "ents", "dep"],  # noqa # type: ignore
     verbose: bool = True,
     **kwargs,  # noqa
 ) -> pd.DataFrame:
@@ -22,7 +23,7 @@ def n_sents_score(
     sentences.
 
     Args:
-        n_sents (Union[int, List[int]]): Number of sentences which the performance
+        n_sents (Union[int, list[int]]): Number of sentences which the performance
             should be applied to.
         apply_fn (Callable):  A wrapper function for the model you wish to score. The
             model should take in a spacy Example and output a tagged version of it.
@@ -30,7 +31,7 @@ def n_sents_score(
             options include "dane". Defaults to "dane".
         split (str, optional): Which splits of the dataset should be used. Possible
             options include "train", "dev", "test", "all". Defaults to "test".
-        score_fn (List[Union[str, Callable]], optional): A scoring function which takes
+        score_fn (list[Union[str, Callable]], optional): A scoring function which takes
             in a list of examples and return a dictionary of the form {"score_name":
             score}. Four potiential strings are valid. "ents" for measuring the
             performance of entity spans. "pos" for measuring the performance of
diff --git a/src/dacy/score/score.py b/src/dacy/score/score.py
index cfa3e320..bc960c10 100644
--- a/src/dacy/score/score.py
+++ b/src/dacy/score/score.py
@@ -1,10 +1,12 @@
 """This includes function for scoring models applied to a SpaCy corpus."""
+
 from __future__ import annotations
 
+from collections.abc import Iterable
 from copy import copy
 from functools import partial
 from time import time  # type: ignore
-from typing import Callable, Iterable
+from typing import Callable
 
 import pandas as pd
 from spacy.language import Language
@@ -63,7 +65,7 @@ def score(  # noqa
             to score. The model should take in a list of spacy Examples
             (Iterable[Example]) and output a tagged version of it (Iterable[Example]). A
             SpaCy pipeline (Language) can be provided as is.
-        score_fn (List[Union[Callable[[Iterable[Example]], dict], str]], optional): A
+        score_fn (list[Union[Callable[[Iterable[Example]], dict], str]], optional): A
             scoring function which takes in a list of examples (Iterable[Example]) and
             return a dictionary of performance scores. Four potiential strings are
             valid. "ents" for measuring the performance of entity spans. "pos" for
@@ -72,7 +74,7 @@ def score(  # noqa
             "dep" for measuring the performance of dependency parsing. "nlp" for
             measuring the performance of all components in the specified nlp pipeline.
             Defaults to ["token", "pos", "ents", "dep"].
-        augmenters (List[Callable[[Language, Example], Iterable[Example]]], optional): A
+        augmenters (list[Callable[[Language, Example], Iterable[Example]]], optional): A
             spaCy style augmenters which should be applied to the corpus or a list
             thereof. defaults to [], indicating no augmenters.
         k (int, optional): Number of times it should run the augmentation and test the
@@ -163,7 +165,7 @@ def __score(augmenter):  # noqa: ANN001
 
         # and collapse list to dict
         for key in scores:  # type: ignore
-            scores[key] = [s[key] if key in s else None for s in scores_ls]  # type: ignore
+            scores[key] = [s.get(key, None) for s in scores_ls]  # type: ignore
 
         scores["k"] = list(range(k))  # type: ignore
 
diff --git a/src/dacy/sentiment/wrapped_models.py b/src/dacy/sentiment/wrapped_models.py
index d4043404..67110532 100644
--- a/src/dacy/sentiment/wrapped_models.py
+++ b/src/dacy/sentiment/wrapped_models.py
@@ -1,4 +1,4 @@
-from typing import Callable, List, Optional
+from typing import Callable, Optional
 from warnings import warn
 
 from spacy.lang.da import Danish
@@ -132,12 +132,12 @@
 def make_emotion_transformer(
     nlp: Language,
     name: str,
-    model: Model[List[Doc], FullTransformerBatch],
-    set_extra_annotations: Callable[[List[Doc], FullTransformerBatch], None],  # type: ignore
+    model: Model[list[Doc], FullTransformerBatch],
+    set_extra_annotations: Callable[[list[Doc], FullTransformerBatch], None],  # type: ignore
     max_batch_items: int,
     doc_extension_trf_data: str,
     doc_extension_prediction: str,
-    labels: List[str],  # type: ignore
+    labels: list[str],  # type: ignore
 ) -> SequenceClassificationTransformer:
     if not Doc.has_extension("emotionally_laden"):
         warn(
diff --git a/tests/test_datasets.py b/tests/test_datasets.py
index 63558a7d..88ca2ffd 100644
--- a/tests/test_datasets.py
+++ b/tests/test_datasets.py
@@ -1,9 +1,10 @@
-import dacy
-from dacy.datasets import dane, female_names, male_names, muslim_names
 from spacy.lang.da import Danish
 from spacy.training import Example
 from spacy.training.corpus import Corpus
 
+import dacy
+from dacy.datasets import dane, female_names, male_names, muslim_names
+
 
 def test_dane():
     train, dev, test = dane(open_unverified_connection=True)  # type: ignore
diff --git a/tests/test_download.py b/tests/test_download.py
index 32ae2f71..fe16ebf6 100644
--- a/tests/test_download.py
+++ b/tests/test_download.py
@@ -1,5 +1,6 @@
-import dacy
 import pytest
+
+import dacy
 from dacy.load import load
 
 
diff --git a/tests/test_hate_speech.py b/tests/test_hate_speech.py
index cec26d8c..09ca6f95 100644
--- a/tests/test_hate_speech.py
+++ b/tests/test_hate_speech.py
@@ -1,6 +1,7 @@
-import dacy
 import spacy
 
+import dacy
+
 
 def test_add_hate_speech_detection():
     nlp = spacy.blank("da")
diff --git a/tests/test_score.py b/tests/test_score.py
index 8d1ce467..54510535 100644
--- a/tests/test_score.py
+++ b/tests/test_score.py
@@ -1,9 +1,10 @@
 import pandas as pd
-from dacy.datasets import dane
-from dacy.score import n_sents_score, score
 from spacy.lang.da import Danish
 from spacy.training.augment import create_lower_casing_augmenter
 
+from dacy.datasets import dane
+from dacy.score import n_sents_score, score
+
 
 def test_score():
     nlp = Danish()
diff --git a/tests/test_sentiment.py b/tests/test_sentiment.py
index 0777c92c..23a5bfe7 100644
--- a/tests/test_sentiment.py
+++ b/tests/test_sentiment.py
@@ -1,6 +1,7 @@
-import dacy
 import spacy
 
+import dacy
+
 
 def test_add_subjectivity():
     nlp = spacy.blank("da")
diff --git a/training/main/scripts/add_readme_metadata.py b/training/main/scripts/add_readme_metadata.py
index cf810dca..9d10a664 100644
--- a/training/main/scripts/add_readme_metadata.py
+++ b/training/main/scripts/add_readme_metadata.py
@@ -5,9 +5,11 @@
 
 """
 
+from __future__ import annotations
+
 import json
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Union
+from typing import Any,  Optional, Union
 
 import typer
 import yaml
@@ -16,7 +18,7 @@
 TEXT_CLASSIFICATION_COMPONENTS = ["textcat", "textcat_multilabel"]
 
 
-def _create_model_card(repo_name: str, repo_dir: Path) -> Dict[str, Any]:
+def _create_model_card(repo_name: str, repo_dir: Path) -> dict[str, Any]:
     meta_path = repo_dir / "meta.json"
     with meta_path.open("r", encoding="utf-8") as f:
         data = json.load(f)
@@ -86,10 +88,10 @@ def _create_model_card(repo_name: str, repo_dir: Path) -> Dict[str, Any]:
 
 
 def _insert_value(
-    metadata: Dict[str, Any],
+    metadata: dict[str, Any],
     name: str,
     value: Optional[Any],
-) -> Dict[str, Any]:
+) -> dict[str, Any]:
     if value is None or value == "":
         return metadata
     metadata[name] = value
@@ -97,10 +99,10 @@ def _insert_value(
 
 
 def _insert_values_as_list(
-    metadata: Dict[str, Any],
+    metadata: dict[str, Any],
     name: str,
     values: Optional[Any],
-) -> Dict[str, List[Any]]:
+) -> dict[str, list[Any]]:
     if values is None:
         return metadata
     if isinstance(values, str):
@@ -111,7 +113,7 @@ def _insert_values_as_list(
     return metadata
 
 
-def _create_metric(name: str, t: str, value: float) -> Dict[str, Union[str, float]]:
+def _create_metric(name: str, t: str, value: float) -> dict[str, Union[str, float]]:
     return {"name": name, "type": t, "value": value}
 
 
@@ -120,14 +122,14 @@ def _create_p_r_f_list(
     precision: float,
     recall: float,
     f_score: float,
-) -> List[Dict[str, Union[str, float]]]:
+) -> list[dict[str, Union[str, float]]]:
     precision = _create_metric(f"{metric_name} Precision", "precision", precision)  # type: ignore
     recall = _create_metric(f"{metric_name} Recall", "recall", recall)  # type: ignore
     f_score = _create_metric(f"{metric_name} F Score", "f_score", f_score)  # type: ignore
     return [precision, recall, f_score]  # type: ignore
 
 
-def _create_model_index(repo_name: str, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+def _create_model_index(repo_name: str, data: dict[str, Any]) -> list[dict[str, Any]]:
     # TODO: add some more metrics here
     model_index = {"name": repo_name}
     results = []
diff --git a/training/main/scripts/combine.py b/training/main/scripts/combine.py
index ed4774f2..9b46c196 100644
--- a/training/main/scripts/combine.py
+++ b/training/main/scripts/combine.py
@@ -7,7 +7,6 @@
 import json
 from collections import defaultdict
 from pathlib import Path
-from typing import Dict, List
 
 import spacy
 from conllu import parse
@@ -142,7 +141,7 @@ def combine_docs(cdt_sentences, ddt_dane):
             sent_id_to_doc_instance[doc._.sent_id] = doc
 
     # combine documents
-    doc_to_be_created: Dict[str, List[str]] = {}
+    doc_to_be_created: dict[str, list[str]] = {}
     sent_id_to_sent = {}
     for sent in cdt_sentences:
         sent_id = sent.metadata["sent_id"]
diff --git a/training/main/scripts/create_kb.py b/training/main/scripts/create_kb.py
index b766aa01..4e734ecc 100644
--- a/training/main/scripts/create_kb.py
+++ b/training/main/scripts/create_kb.py
@@ -5,7 +5,6 @@
 import ssl
 from collections import defaultdict
 from pathlib import Path
-from typing import List
 
 import numpy as np
 import spacy
@@ -24,7 +23,7 @@
 def main(
     trf_name: str,
     save_path_kb: Path = project_path / "assets/knowledge_bases/knowledge_base.kb",
-    langs_to_fetch: List[str] = ["da", "en"],
+    langs_to_fetch: list[str] = ["da", "en"],
 ):
     """Step 1: create the Knowledge Base in spaCy and write it to file"""
     spacy.require_gpu()  # type: ignore
@@ -131,7 +130,7 @@ def _fetch_wikidata_description(qid: str):
     return item.description.get("da", item.description.get("en", ""))  # type: ignore
 
 
-def _fetch_wikidata_aliases(qid, langs: List[str] = ["da", "en"]):
+def _fetch_wikidata_aliases(qid, langs: list[str] = ["da", "en"]):
     """
     Fetch the aliases of a Wikidata item.
     """
@@ -148,7 +147,7 @@ def _fetch_wikidata_aliases(qid, langs: List[str] = ["da", "en"]):
     return aliases
 
 
-def _load_ents_from_data(nlp: Language, splits: List[str] = ["dev", "train"]):
+def _load_ents_from_data(nlp: Language, splits: list[str] = ["dev", "train"]):
     """
     Load in the data from the training set.
     """