diff --git a/e2e/pipelines/test_named_entity_extractor.py b/e2e/pipelines/test_named_entity_extractor.py
index 7e67d06ac3..a51aed9082 100644
--- a/e2e/pipelines/test_named_entity_extractor.py
+++ b/e2e/pipelines/test_named_entity_extractor.py
@@ -14,7 +14,7 @@
 
 
 @pytest.fixture
-def raw_texts():
+def raw_texts() -> list:
     return [
         "My name is Clara and I live in Berkeley, California.",
         "I'm Merlin, the happy pig!",
@@ -24,7 +24,7 @@ def raw_texts():
 
 
 @pytest.fixture
-def hf_annotations():
+def hf_annotations() -> list:
     return [
         [
             NamedEntityAnnotation(entity="PER", start=11, end=16),
@@ -38,7 +38,7 @@ def hf_annotations():
 
 
 @pytest.fixture
-def spacy_annotations():
+def spacy_annotations() -> list:
     return [
         [
             NamedEntityAnnotation(entity="PERSON", start=11, end=16),
@@ -51,14 +51,14 @@ def spacy_annotations():
     ]
 
 
-def test_ner_extractor_init(del_hf_env_vars):
+def test_ner_extractor_init(del_hf_env_vars) -> None:
     extractor = NamedEntityExtractor(backend=NamedEntityExtractorBackend.HUGGING_FACE, model="dslim/bert-base-NER")
     extractor.warm_up()
     assert extractor.initialized
 
 
 @pytest.mark.parametrize("batch_size", [1, 3])
-def test_ner_extractor_hf_backend(raw_texts, hf_annotations, batch_size, del_hf_env_vars):
+def test_ner_extractor_hf_backend(raw_texts, hf_annotations, batch_size, del_hf_env_vars) -> None:
     extractor = NamedEntityExtractor(backend=NamedEntityExtractorBackend.HUGGING_FACE, model="dslim/bert-base-NER")
     extractor.warm_up()
 
@@ -70,7 +70,7 @@ def test_ner_extractor_hf_backend(raw_texts, hf_annotations, batch_size, del_hf_
     not os.environ.get("HF_API_TOKEN", None) and not os.environ.get("HF_TOKEN", None),
     reason="Export an env var called HF_API_TOKEN or HF_TOKEN containing the Hugging Face token to run this test.",
 )
-def test_ner_extractor_hf_backend_private_models(raw_texts, hf_annotations, batch_size):
+def test_ner_extractor_hf_backend_private_models(raw_texts, hf_annotations, batch_size) -> None:
     extractor = NamedEntityExtractor(backend=NamedEntityExtractorBackend.HUGGING_FACE, model="deepset/bert-base-NER")
     extractor.warm_up()
 
@@ -78,7 +78,7 @@ def test_ner_extractor_hf_backend_private_models(raw_texts, hf_annotations, batc
 
 
 @pytest.mark.parametrize("batch_size", [1, 3])
-def test_ner_extractor_spacy_backend(raw_texts, spacy_annotations, batch_size):
+def test_ner_extractor_spacy_backend(raw_texts, spacy_annotations, batch_size) -> None:
     extractor = NamedEntityExtractor(backend=NamedEntityExtractorBackend.SPACY, model="en_core_web_trf")
     extractor.warm_up()
 
@@ -86,7 +86,7 @@ def test_ner_extractor_spacy_backend(raw_texts, spacy_annotations, batch_size):
 
 
 @pytest.mark.parametrize("batch_size", [1, 3])
-def test_ner_extractor_in_pipeline(raw_texts, hf_annotations, batch_size, del_hf_env_vars):
+def test_ner_extractor_in_pipeline(raw_texts, hf_annotations, batch_size, del_hf_env_vars) -> None:
     pipeline = Pipeline()
     pipeline.add_component(
         name="ner_extractor",
@@ -100,7 +100,7 @@ def test_ner_extractor_in_pipeline(raw_texts, hf_annotations, batch_size, del_hf
     _check_predictions(predicted, hf_annotations)
 
 
-def _extract_and_check_predictions(extractor, texts, expected, batch_size):
+def _extract_and_check_predictions(extractor, texts, expected, batch_size) -> None:
     docs = [Document(content=text) for text in texts]
     outputs = extractor.run(documents=docs, batch_size=batch_size)["documents"]
     for original_doc, output_doc in zip(docs, outputs):
@@ -117,7 +117,7 @@ def _extract_and_check_predictions(extractor, texts, expected, batch_size):
     _check_predictions(predicted, expected)
 
 
-def _check_predictions(predicted, expected):
+def _check_predictions(predicted, expected) -> None:
     assert len(predicted) == len(expected)
     for pred, exp in zip(predicted, expected):
         assert len(pred) == len(exp)