diff --git a/fastembed/common/model_management.py b/fastembed/common/model_management.py
index b20337c..e8f1a56 100644
--- a/fastembed/common/model_management.py
+++ b/fastembed/common/model_management.py
@@ -3,11 +3,11 @@ import time
 import shutil
 import tarfile
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Self
 
 import requests
-from huggingface_hub import snapshot_download
-from huggingface_hub.utils import RepositoryNotFoundError
+from huggingface_hub import snapshot_download # type: ignore
+from huggingface_hub.utils import RepositoryNotFoundError # type: ignore
 from loguru import logger
 from tqdm import tqdm
 
@@ -91,12 +91,12 @@ class ModelManagement:
 
     @classmethod
     def download_files_from_huggingface(
-        cls,
+        cls: type[Self],
         hf_source_repo: str,
         cache_dir: Optional[str] = None,
         extra_patterns: Optional[List[str]] = None,
         local_files_only: bool = False,
-        **kwargs,
+        **kwargs: Any,
     ) -> str:
         """
         Downloads a model from HuggingFace Hub.
@@ -119,16 +119,16 @@ class ModelManagement:
         if extra_patterns is not None:
             allow_patterns.extend(extra_patterns)
 
-        return snapshot_download(
+        return str(snapshot_download(
             repo_id=hf_source_repo,
             allow_patterns=allow_patterns,
             cache_dir=cache_dir,
             local_files_only=local_files_only,
             **kwargs,
-        )
+        ))
 
     @classmethod
-    def decompress_to_cache(cls, targz_path: str, cache_dir: str):
+    def decompress_to_cache(cls: type[Self], targz_path: str, cache_dir: str) -> str:
         """
         Decompresses a .tar.gz file to a cache directory.
 
@@ -211,7 +211,7 @@ class ModelManagement:
 
     @classmethod
     def download_model(
-        cls, model: Dict[str, Any], cache_dir: Path, retries: int = 3, **kwargs
+        cls: type[Self], model: Dict[str, Any], cache_dir: Path, retries: int = 3, **kwargs: Any
     ) -> Path:
         """
         Downloads a model from HuggingFace Hub or Google Cloud Storage.
diff --git a/fastembed/common/onnx_model.py b/fastembed/common/onnx_model.py
index 859416e..cdb19ba 100644
--- a/fastembed/common/onnx_model.py
+++ b/fastembed/common/onnx_model.py
@@ -11,12 +11,13 @@ from typing import (
     Tuple,
     Type,
     TypeVar,
+    Self
 )
 
 import numpy as np
 import onnxruntime as ort
 
-from fastembed.common.types import OnnxProvider
+from fastembed.common.types import OnnxProvider # type: ignore
 from fastembed.parallel_processor import Worker
 
 # Holds type of the embedding result
@@ -39,11 +40,11 @@ class OnnxModel(Generic[T]):
         raise NotImplementedError("Subclasses must implement this method")
 
     def __init__(self) -> None:
-        self.model = None
+        self.model: ort.InferenceSession = None
         self.tokenizer = None
 
     def _preprocess_onnx_input(
-        self, onnx_input: Dict[str, np.ndarray], **kwargs
+        self, onnx_input: Dict[str, np.ndarray], **kwargs: Any
     ) -> Dict[str, np.ndarray]:
         """
         Preprocess the onnx input.
@@ -51,7 +52,7 @@ class OnnxModel(Generic[T]):
         return onnx_input
 
     def _load_onnx_model(
-        self,
+        self: Self,
         model_dir: Path,
         model_file: str,
         threads: Optional[int],
@@ -106,30 +107,36 @@ class OnnxModel(Generic[T]):
     def load_onnx_model(self) -> None:
         raise NotImplementedError("Subclasses must implement this method")
 
-    def onnx_embed(self, *args, **kwargs) -> OnnxOutputContext:
+    def onnx_embed(self: Self, *args: Any, **kwargs: Any) -> OnnxOutputContext:
         raise NotImplementedError("Subclasses must implement this method")
 
 
 class EmbeddingWorker(Worker):
     def init_embedding(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: str,
-        **kwargs,
+        **kwargs: Any,
     ) -> OnnxModel:
         raise NotImplementedError()
 
     def __init__(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: str,
-        **kwargs,
+        **kwargs: Any,
     ):
         self.model = self.init_embedding(model_name, cache_dir, **kwargs)
 
     @classmethod
-    def start(cls, model_name: str, cache_dir: str, **kwargs: Any) -> "EmbeddingWorker":
+    def start(cls: type[Self], **kwargs: Any) -> "EmbeddingWorker":
+        model_name = kwargs.get("model_name", None)
+        if model_name is None:
+            raise ValueError("model_name must be provided")
+        cache_dir = kwargs.get("cache_dir", None)
+        if cache_dir is None:
+            raise ValueError("cache_dir must be provided")
         return cls(model_name=model_name, cache_dir=cache_dir, **kwargs)
 
-    def process(self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
+    def process(self: Self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
         raise NotImplementedError("Subclasses must implement this method")
diff --git a/fastembed/common/preprocessor_utils.py b/fastembed/common/preprocessor_utils.py
index db2432f..1a27f6e 100644
--- a/fastembed/common/preprocessor_utils.py
+++ b/fastembed/common/preprocessor_utils.py
@@ -1,7 +1,7 @@
 import json
 from pathlib import Path
 from typing import Tuple
-from tokenizers import AddedToken, Tokenizer
+from tokenizers import AddedToken, Tokenizer # type: ignore
 
 from fastembed.image.transform.operators import Compose
 
diff --git a/fastembed/common/utils.py b/fastembed/common/utils.py
index cd50dfd..c1d5122 100644
--- a/fastembed/common/utils.py
+++ b/fastembed/common/utils.py
@@ -10,12 +10,12 @@ import re
 from typing import Set
 
 
-def normalize(input_array, p=2, dim=1, eps=1e-12) -> np.ndarray:
+def normalize(input_array: np.ndarray, p: float=2, dim:int=1, eps:float=1e-12) -> np.ndarray:
     # Calculate the Lp norm along the specified dimension
     norm = np.linalg.norm(input_array, ord=p, axis=dim, keepdims=True)
     norm = np.maximum(norm, eps)  # Avoid division by zero
     normalized_array = input_array / norm
-    return normalized_array
+    return normalized_array # type: ignore[no-any-return]
 
 
 def iter_batch(iterable: Union[Iterable, Generator], size: int) -> Iterable:
diff --git a/fastembed/image/image_embedding.py b/fastembed/image/image_embedding.py
index b404cb9..e368c1b 100644
--- a/fastembed/image/image_embedding.py
+++ b/fastembed/image/image_embedding.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Type
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Self
 
 import numpy as np
 
@@ -41,7 +41,7 @@ class ImageEmbedding(ImageEmbeddingBase):
         return result
 
     def __init__(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
@@ -49,7 +49,7 @@ class ImageEmbedding(ImageEmbeddingBase):
         cuda: bool = False,
         device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
-        **kwargs,
+        **kwargs: Any,
     ):
         super().__init__(model_name, cache_dir, threads, **kwargs)
         for EMBEDDING_MODEL_TYPE in self.EMBEDDINGS_REGISTRY:
@@ -73,11 +73,11 @@ class ImageEmbedding(ImageEmbeddingBase):
         )
 
     def embed(
-        self,
+        self: Self,
         images: ImageInput,
         batch_size: int = 16,
         parallel: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[np.ndarray]:
         """
         Encode a list of documents into list of embeddings.
diff --git a/fastembed/image/image_embedding_base.py b/fastembed/image/image_embedding_base.py
index 6a23df1..042a877 100644
--- a/fastembed/image/image_embedding_base.py
+++ b/fastembed/image/image_embedding_base.py
@@ -1,5 +1,5 @@
-from typing import Iterable, Optional
-
+from typing import Iterable, Optional, Self, Any
+from pathlib import Path
 import numpy as np
 
 from fastembed.common.model_management import ModelManagement
@@ -8,11 +8,11 @@ from fastembed.common.types import ImageInput
 
 class ImageEmbeddingBase(ModelManagement):
     def __init__(
-        self,
+        self: Self,
         model_name: str,
-        cache_dir: Optional[str] = None,
+        cache_dir: Optional[str|Path] = None,
         threads: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ):
         self.model_name = model_name
         self.cache_dir = cache_dir
@@ -20,11 +20,11 @@ class ImageEmbeddingBase(ModelManagement):
         self._local_files_only = kwargs.pop("local_files_only", False)
 
     def embed(
-        self,
+        self: Self,
         images: ImageInput,
         batch_size: int = 16,
         parallel: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[np.ndarray]:
         """
         Embeds a list of images into a list of embeddings.
diff --git a/fastembed/image/onnx_embedding.py b/fastembed/image/onnx_embedding.py
index 1390743..e71d176 100644
--- a/fastembed/image/onnx_embedding.py
+++ b/fastembed/image/onnx_embedding.py
@@ -1,7 +1,7 @@
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Type
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Self
 
 import numpy as np
-
+from pathlib import Path
 from fastembed.common import ImageInput, OnnxProvider
 from fastembed.common.onnx_model import OnnxOutputContext
 from fastembed.common.utils import define_cache_dir, normalize
@@ -70,7 +70,7 @@ supported_onnx_models = [
 
 class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[np.ndarray]):
     def __init__(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
@@ -79,12 +79,12 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[np.ndarray]):
         device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
         device_id: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ):
         """
         Args:
             model_name (str): The name of the model to use.
-            cache_dir (str, optional): The path to the cache directory.
+            cache_dir (Path, str, optional): The path to the cache directory.
                                        Can be set using the `FASTEMBED_CACHE_PATH` env variable.
                                        Defaults to `fastembed_cache` in the system's temp directory.
             threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
@@ -115,11 +115,9 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[np.ndarray]):
             self.device_id = device_id
         elif self.device_ids is not None:
             self.device_id = self.device_ids[0]
-        else:
-            self.device_id = None
 
         self.model_description = self._get_model_description(model_name)
-        self.cache_dir = define_cache_dir(cache_dir)
+        self.cache_dir: Path = define_cache_dir(cache_dir)
         self._model_dir = self.download_model(
             self.model_description, self.cache_dir, local_files_only=self._local_files_only
         )
@@ -151,11 +149,11 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[np.ndarray]):
         return supported_onnx_models
 
     def embed(
-        self,
+        self: Self,
         images: ImageInput,
         batch_size: int = 16,
         parallel: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[np.ndarray]:
         """
         Encode a list of images into list of embeddings.
@@ -190,7 +188,7 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[np.ndarray]):
         return OnnxImageEmbeddingWorker
 
     def _preprocess_onnx_input(
-        self, onnx_input: Dict[str, np.ndarray], **kwargs
+        self: Self, onnx_input: Dict[str, np.ndarray], **kwargs: Any
     ) -> Dict[str, np.ndarray]:
         """
         Preprocess the onnx input.
@@ -198,12 +196,12 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[np.ndarray]):
 
         return onnx_input
 
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[np.ndarray]:
+    def _post_process_onnx_output(self: Self, output: OnnxOutputContext) -> Iterable[np.ndarray]:
         return normalize(output.model_output).astype(np.float32)
 
 
 class OnnxImageEmbeddingWorker(ImageEmbeddingWorker):
-    def init_embedding(self, model_name: str, cache_dir: str, **kwargs) -> OnnxImageEmbedding:
+    def init_embedding(self: Self, model_name: str, cache_dir: str, **kwargs: Any) -> OnnxImageEmbedding:
         return OnnxImageEmbedding(
             model_name=model_name,
             cache_dir=cache_dir,
diff --git a/fastembed/image/onnx_image_model.py b/fastembed/image/onnx_image_model.py
index 86dac0e..ccaef23 100644
--- a/fastembed/image/onnx_image_model.py
+++ b/fastembed/image/onnx_image_model.py
@@ -2,7 +2,7 @@ import contextlib
 import os
 from multiprocessing import get_all_start_methods
 from pathlib import Path
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Type
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Type, Self
 
 import numpy as np
 from PIL import Image
@@ -29,7 +29,7 @@ class OnnxImageModel(OnnxModel[T]):
         self.processor = None
 
     def _preprocess_onnx_input(
-        self, onnx_input: Dict[str, np.ndarray], **kwargs
+        self: Self, onnx_input: Dict[str, np.ndarray], **kwargs: Any
     ) -> Dict[str, np.ndarray]:
         """
         Preprocess the onnx input.
@@ -37,7 +37,7 @@ class OnnxImageModel(OnnxModel[T]):
         return onnx_input
 
     def _load_onnx_model(
-        self,
+        self: Self,
         model_dir: Path,
         model_file: str,
         threads: Optional[int],
@@ -58,10 +58,10 @@ class OnnxImageModel(OnnxModel[T]):
     def load_onnx_model(self) -> None:
         raise NotImplementedError("Subclasses must implement this method")
 
-    def _build_onnx_input(self, encoded: np.ndarray) -> Dict[str, np.ndarray]:
+    def _build_onnx_input(self: Self, encoded: np.ndarray) -> Dict[str, np.ndarray]:
         return {node.name: encoded for node in self.model.get_inputs()}
 
-    def onnx_embed(self, images: List[ImageInput], **kwargs) -> OnnxOutputContext:
+    def onnx_embed(self: Self, images: List[ImageInput], **kwargs: Any) -> OnnxOutputContext:
         with contextlib.ExitStack():
             image_files = [
                 Image.open(image) if not isinstance(image, Image.Image) else image
@@ -75,7 +75,7 @@ class OnnxImageModel(OnnxModel[T]):
         return OnnxOutputContext(model_output=embeddings)
 
     def _embed_images(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: str,
         images: ImageInput,
@@ -84,7 +84,7 @@ class OnnxImageModel(OnnxModel[T]):
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
         device_ids: Optional[List[int]] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[T]:
         is_small = False
 
@@ -125,7 +125,7 @@ class OnnxImageModel(OnnxModel[T]):
 
 
 class ImageEmbeddingWorker(EmbeddingWorker):
-    def process(self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
+    def process(self: Self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
         for idx, batch in items:
             embeddings = self.model.onnx_embed(batch)
             yield idx, embeddings
diff --git a/fastembed/image/transform/functional.py b/fastembed/image/transform/functional.py
index 0c8b054..cdac10e 100644
--- a/fastembed/image/transform/functional.py
+++ b/fastembed/image/transform/functional.py
@@ -62,8 +62,8 @@ def center_crop(
 
 def normalize(
     image: np.ndarray,
-    mean=Union[float, np.ndarray],
-    std=Union[float, np.ndarray],
+    mean:Union[float, np.ndarray],
+    std:Union[float, np.ndarray],
 ) -> np.ndarray:
     if not isinstance(image, np.ndarray):
         raise ValueError("image must be a numpy array")
@@ -79,7 +79,7 @@ def normalize(
                 f"mean must have {num_channels} elements if it is an iterable, got {len(mean)}"
             )
     else:
-        mean = [mean] * num_channels
+        mean = np.array([mean] * num_channels)
     mean = np.array(mean, dtype=image.dtype)
 
     if isinstance(std, Sized):
@@ -88,7 +88,7 @@ def normalize(
                 f"std must have {num_channels} elements if it is an iterable, got {len(std)}"
             )
     else:
-        std = [std] * num_channels
+        std = np.array([std] * num_channels)
     std = np.array(std, dtype=image.dtype)
 
     image = ((image.T - mean) / std).T
@@ -96,10 +96,10 @@ def normalize(
 
 
 def resize(
-    image: Image,
+    image: Image.Image,
     size: Union[int, Tuple[int, int]],
     resample: Image.Resampling = Image.Resampling.BILINEAR,
-) -> Image:
+) -> Image.Image:
     if isinstance(size, tuple):
         return image.resize(size, resample)
 
@@ -114,11 +114,14 @@ def resize(
     return image.resize(new_size, resample)
 
 
-def rescale(image: np.ndarray, scale: float, dtype=np.float32) -> np.ndarray:
+def rescale(
+    image: np.ndarray[Union[np.float32, np.int32]],
+    scale: float,
+    dtype: Union[np.dtype[np.float32], np.dtype[np.int32]] = np.float32
+) -> np.ndarray:
     return (image * scale).astype(dtype)
 
-
-def pil2ndarray(image: Union[Image.Image, np.ndarray]):
+def pil2ndarray(image: Union[Image.Image, np.ndarray]) -> np.ndarray:
     if isinstance(image, Image.Image):
         return np.asarray(image).transpose((2, 0, 1))
     return image
diff --git a/fastembed/image/transform/operators.py b/fastembed/image/transform/operators.py
index 2248694..e3f5d7a 100644
--- a/fastembed/image/transform/operators.py
+++ b/fastembed/image/transform/operators.py
@@ -33,8 +33,8 @@ class CenterCrop(Transform):
 
 class Normalize(Transform):
     def __init__(self, mean: Union[float, List[float]], std: Union[float, List[float]]):
-        self.mean = mean
-        self.std = std
+        self.mean = np.array(mean)
+        self.std = np.array(std)
 
     def __call__(self, images: List[np.ndarray]) -> List[np.ndarray]:
         return [normalize(image, mean=self.mean, std=self.std) for image in images]
@@ -100,7 +100,7 @@ class Compose:
         Returns:
             Compose: Image processor.
         """
-        transforms = []
+        transforms: List[Transform] = []
         cls._get_convert_to_rgb(transforms, config)
         cls._get_resize(transforms, config)
         cls._get_center_crop(transforms, config)
@@ -110,11 +110,11 @@ class Compose:
         return cls(transforms=transforms)
 
     @staticmethod
-    def _get_convert_to_rgb(transforms: List[Transform], config: Dict[str, Any]):
+    def _get_convert_to_rgb(transforms: List[Transform], config: Dict[str, Any]) -> None:
         transforms.append(ConvertToRGB())
 
     @staticmethod
-    def _get_resize(transforms: List[Transform], config: Dict[str, Any]):
+    def _get_resize(transforms: List[Transform], config: Dict[str, Any])  -> None:
         mode = config.get("image_processor_type", "CLIPImageProcessor")
         if mode == "CLIPImageProcessor" or mode == "SiglipImageProcessor":
             if config.get("do_resize", False):
@@ -159,7 +159,7 @@ class Compose:
                 )
 
     @staticmethod
-    def _get_center_crop(transforms: List[Transform], config: Dict[str, Any]):
+    def _get_center_crop(transforms: List[Transform], config: Dict[str, Any]) -> None:
         mode = config.get("image_processor_type", "CLIPImageProcessor")
         if mode == "CLIPImageProcessor" or mode == "SiglipImageProcessor":
             if config.get("do_center_crop", False):
@@ -177,16 +177,16 @@ class Compose:
             raise ValueError(f"Preprocessor {mode} is not supported")
 
     @staticmethod
-    def _get_pil2ndarray(transforms: List[Transform], config: Dict[str, Any]):
+    def _get_pil2ndarray(transforms: List[Transform], config: Dict[str, Any]) -> None:
         transforms.append(PILtoNDarray())
 
     @staticmethod
-    def _get_rescale(transforms: List[Transform], config: Dict[str, Any]):
+    def _get_rescale(transforms: List[Transform], config: Dict[str, Any]) -> None:
         if config.get("do_rescale", True):
             rescale_factor = config.get("rescale_factor", 1 / 255)
             transforms.append(Rescale(scale=rescale_factor))
 
     @staticmethod
-    def _get_normalize(transforms: List[Transform], config: Dict[str, Any]):
+    def _get_normalize(transforms: List[Transform], config: Dict[str, Any]) -> None:
         if config.get("do_normalize", False):
             transforms.append(Normalize(mean=config["image_mean"], std=config["image_std"]))
diff --git a/fastembed/late_interaction/colbert.py b/fastembed/late_interaction/colbert.py
index 686595d..452ee08 100644
--- a/fastembed/late_interaction/colbert.py
+++ b/fastembed/late_interaction/colbert.py
@@ -1,5 +1,5 @@
 import string
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union, Self
 
 import numpy as np
 from tokenizers import Encoding
@@ -45,7 +45,7 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[np.ndarray]):
     MASK_TOKEN = "[MASK]"
 
     def _post_process_onnx_output(
-        self, output: OnnxOutputContext, is_doc: bool = True
+        self: Self, output: OnnxOutputContext, is_doc: bool = True
     ) -> Iterable[np.ndarray]:
         if not is_doc:
             return output.model_output.astype(np.float32)
@@ -67,7 +67,7 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[np.ndarray]):
         return output.model_output.astype(np.float32)
 
     def _preprocess_onnx_input(
-        self, onnx_input: Dict[str, np.ndarray], is_doc: bool = True
+        self: Self, onnx_input: Dict[str, np.ndarray], is_doc: bool = True
     ) -> Dict[str, np.ndarray]:
         if is_doc:
             onnx_input["input_ids"][:, 1] = self.DOCUMENT_MARKER_TOKEN_ID
@@ -75,14 +75,14 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[np.ndarray]):
             onnx_input["input_ids"][:, 1] = self.QUERY_MARKER_TOKEN_ID
         return onnx_input
 
-    def tokenize(self, documents: List[str], is_doc: bool = True) -> List[Encoding]:
+    def tokenize(self: Self, documents: List[str], is_doc: bool = True) -> List[Encoding]:
         return (
             self._tokenize_documents(documents=documents)
             if is_doc
             else self._tokenize_query(query=next(iter(documents)))
         )
 
-    def _tokenize_query(self, query: str) -> List[Encoding]:
+    def _tokenize_query(self: Self, query: str) -> List[Encoding]:
         # "@ " is added to a query to be replaced with a special query token
         # make sure that "@ " is considered as a single token
         query = f"@ {query}"
@@ -104,7 +104,7 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[np.ndarray]):
                 self.tokenizer.enable_padding(**prev_padding)
         return encoded
 
-    def _tokenize_documents(self, documents: List[str]) -> List[Encoding]:
+    def _tokenize_documents(self: Self, documents: List[str]) -> List[Encoding]:
         # "@ " is added to a document to be replaced with a special document token
         # make sure that "@ " is considered as a single token
         documents = ["@ " + doc for doc in documents]
@@ -112,7 +112,7 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[np.ndarray]):
         return encoded
 
     @classmethod
-    def list_supported_models(cls) -> List[Dict[str, Any]]:
+    def list_supported_models(cls: type[Self]) -> List[Dict[str, Any]]:
         """Lists the supported models.
 
         Returns:
@@ -121,7 +121,7 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[np.ndarray]):
         return supported_colbert_models
 
     def __init__(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
@@ -130,7 +130,7 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[np.ndarray]):
         device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
         device_id: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ):
         """
         Args:
@@ -182,7 +182,7 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[np.ndarray]):
         if not self.lazy_load:
             self.load_onnx_model()
 
-    def load_onnx_model(self) -> None:
+    def load_onnx_model(self: Self) -> None:
         self._load_onnx_model(
             model_dir=self._model_dir,
             model_file=self.model_description["model_file"],
@@ -199,11 +199,11 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[np.ndarray]):
         }
 
     def embed(
-        self,
+        self: Self,
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[np.ndarray]:
         """
         Encode a list of documents into list of embeddings.
@@ -232,7 +232,7 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[np.ndarray]):
             **kwargs,
         )
 
-    def query_embed(self, query: Union[str, List[str]], **kwargs) -> Iterable[np.ndarray]:
+    def query_embed(self: Self, query: Union[str, List[str]], **kwargs: Any) -> Iterable[np.ndarray]:
         if isinstance(query, str):
             query = [query]
 
@@ -245,12 +245,12 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[np.ndarray]):
             )
 
     @classmethod
-    def _get_worker_class(cls) -> Type[TextEmbeddingWorker]:
+    def _get_worker_class(cls: type[Self]) -> Type[TextEmbeddingWorker]:
         return ColbertEmbeddingWorker
 
 
 class ColbertEmbeddingWorker(TextEmbeddingWorker):
-    def init_embedding(self, model_name: str, cache_dir: str, **kwargs) -> Colbert:
+    def init_embedding(self: Self, model_name: str, cache_dir: str, **kwargs: Any) -> Colbert:
         return Colbert(
             model_name=model_name,
             cache_dir=cache_dir,
diff --git a/fastembed/late_interaction/jina_colbert.py b/fastembed/late_interaction/jina_colbert.py
index 42232ae..1d7d8ff 100644
--- a/fastembed/late_interaction/jina_colbert.py
+++ b/fastembed/late_interaction/jina_colbert.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Type
+from typing import Any, Dict, List, Type, Self
 
 import numpy as np
 
@@ -53,7 +53,7 @@ class JinaColbert(Colbert):
 
 
 class JinaColbertEmbeddingWorker(TextEmbeddingWorker):
-    def init_embedding(self, model_name: str, cache_dir: str, **kwargs) -> JinaColbert:
+    def init_embedding(self: Self, model_name: str, cache_dir: str, **kwargs: Any) -> JinaColbert:
         return JinaColbert(
             model_name=model_name,
             cache_dir=cache_dir,
diff --git a/fastembed/late_interaction/late_interaction_embedding_base.py b/fastembed/late_interaction/late_interaction_embedding_base.py
index 64fba49..5d34250 100644
--- a/fastembed/late_interaction/late_interaction_embedding_base.py
+++ b/fastembed/late_interaction/late_interaction_embedding_base.py
@@ -1,4 +1,4 @@
-from typing import Iterable, Optional, Union
+from typing import Iterable, Optional, Union, Any, Self
 
 import numpy as np
 
@@ -7,11 +7,11 @@ from fastembed.common.model_management import ModelManagement
 
 class LateInteractionTextEmbeddingBase(ModelManagement):
     def __init__(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ):
         self.model_name = model_name
         self.cache_dir = cache_dir
@@ -19,15 +19,15 @@ class LateInteractionTextEmbeddingBase(ModelManagement):
         self._local_files_only = kwargs.pop("local_files_only", False)
 
     def embed(
-        self,
+        self: Self,
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[np.ndarray]:
         raise NotImplementedError()
 
-    def passage_embed(self, texts: Iterable[str], **kwargs) -> Iterable[np.ndarray]:
+    def passage_embed(self: Self, texts: Iterable[str], **kwargs: Any) -> Iterable[np.ndarray]:
         """
         Embeds a list of text passages into a list of embeddings.
 
@@ -43,7 +43,7 @@ class LateInteractionTextEmbeddingBase(ModelManagement):
         yield from self.embed(texts, **kwargs)
 
     def query_embed(
-        self, query: Union[str, Iterable[str]], **kwargs
+        self: Self, query: Union[str, Iterable[str]], **kwargs: Any
     ) -> Iterable[np.ndarray]:
         """
         Embeds queries
diff --git a/fastembed/late_interaction/late_interaction_text_embedding.py b/fastembed/late_interaction/late_interaction_text_embedding.py
index c2f05ba..3a9a9a0 100644
--- a/fastembed/late_interaction/late_interaction_text_embedding.py
+++ b/fastembed/late_interaction/late_interaction_text_embedding.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union, Self
 
 import numpy as np
 
@@ -14,7 +14,7 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
     EMBEDDINGS_REGISTRY: List[Type[LateInteractionTextEmbeddingBase]] = [Colbert, JinaColbert]
 
     @classmethod
-    def list_supported_models(cls) -> List[Dict[str, Any]]:
+    def list_supported_models(cls: type[Self]) -> List[Dict[str, Any]]:
         """
         Lists the supported models.
 
@@ -44,7 +44,7 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
         return result
 
     def __init__(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
@@ -52,7 +52,7 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
         cuda: bool = False,
         device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
-        **kwargs,
+        **kwargs: Any,
     ):
         super().__init__(model_name, cache_dir, threads, **kwargs)
         for EMBEDDING_MODEL_TYPE in self.EMBEDDINGS_REGISTRY:
@@ -76,11 +76,11 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
         )
 
     def embed(
-        self,
+        self: Self,
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[np.ndarray]:
         """
         Encode a list of documents into list of embeddings.
@@ -99,7 +99,7 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
         """
         yield from self.model.embed(documents, batch_size, parallel, **kwargs)
 
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs) -> Iterable[np.ndarray]:
+    def query_embed(self: Self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[np.ndarray]:
         """
         Embeds queries
 
diff --git a/fastembed/sparse/bm25.py b/fastembed/sparse/bm25.py
index ed5d41f..0c6fa04 100644
--- a/fastembed/sparse/bm25.py
+++ b/fastembed/sparse/bm25.py
@@ -2,7 +2,7 @@ import os
 from collections import defaultdict
 from multiprocessing import get_all_start_methods
 from pathlib import Path
-from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union, Self
 
 import mmh3
 import numpy as np
@@ -97,7 +97,7 @@ class Bm25(SparseTextEmbeddingBase):
     """
 
     def __init__(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: Optional[str] = None,
         k: float = 1.2,
@@ -105,7 +105,7 @@ class Bm25(SparseTextEmbeddingBase):
         avg_len: float = 256.0,
         language: str = "english",
         token_max_length: int = 40,
-        **kwargs,
+        **kwargs: Any,
     ):
         super().__init__(model_name, cache_dir, **kwargs)
 
@@ -133,7 +133,7 @@ class Bm25(SparseTextEmbeddingBase):
         self.tokenizer = SimpleTokenizer
 
     @classmethod
-    def list_supported_models(cls) -> List[Dict[str, Any]]:
+    def list_supported_models(cls: type[Self]) -> List[Dict[str, Any]]:
         """Lists the supported models.
 
         Returns:
@@ -142,7 +142,7 @@ class Bm25(SparseTextEmbeddingBase):
         return supported_bm25_models
 
     @classmethod
-    def _load_stopwords(cls, model_dir: Path, language: str) -> List[str]:
+    def _load_stopwords(cls: type[Self], model_dir: Path, language: str) -> List[str]:
         stopwords_path = model_dir / f"{language}.txt"
         if not stopwords_path.exists():
             return []
@@ -151,7 +151,7 @@ class Bm25(SparseTextEmbeddingBase):
             return f.read().splitlines()
 
     def _embed_documents(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: str,
         documents: Union[str, Iterable[str]],
@@ -193,11 +193,11 @@ class Bm25(SparseTextEmbeddingBase):
                     yield record
 
     def embed(
-        self,
+        self: Self,
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[SparseEmbedding]:
         """
         Encode a list of documents into list of embeddings.
@@ -222,7 +222,7 @@ class Bm25(SparseTextEmbeddingBase):
             parallel=parallel,
         )
 
-    def _stem(self, tokens: List[str]) -> List[str]:
+    def _stem(self: Self, tokens: List[str]) -> List[str]:
         stemmed_tokens = []
         for token in tokens:
             if token in self.punctuation:
@@ -241,7 +241,7 @@ class Bm25(SparseTextEmbeddingBase):
         return stemmed_tokens
 
     def raw_embed(
-        self,
+        self: Self,
         documents: List[str],
     ) -> List[SparseEmbedding]:
         embeddings = []
@@ -253,7 +253,7 @@ class Bm25(SparseTextEmbeddingBase):
             embeddings.append(SparseEmbedding.from_dict(token_id2value))
         return embeddings
 
-    def _term_frequency(self, tokens: List[str]) -> Dict[int, float]:
+    def _term_frequency(self: Self, tokens: List[str]) -> Dict[int, float]:
         """Calculate the term frequency part of the BM25 formula.
 
         (
@@ -284,10 +284,10 @@ class Bm25(SparseTextEmbeddingBase):
         return tf_map
 
     @classmethod
-    def compute_token_id(cls, token: str) -> int:
+    def compute_token_id(cls: type[Self], token: str) -> int:
         return abs(mmh3.hash(token))
 
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs) -> Iterable[SparseEmbedding]:
+    def query_embed(self: Self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[SparseEmbedding]:
         """To emulate BM25 behaviour, we don't need to use weights in the query, and
         it's enough to just hash the tokens and assign a weight of 1.0 to them.
         """
@@ -312,22 +312,22 @@ class Bm25(SparseTextEmbeddingBase):
 
 class Bm25Worker(Worker):
     def __init__(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: str,
-        **kwargs,
+        **kwargs: Any,
     ):
         self.model = self.init_embedding(model_name, cache_dir, **kwargs)
 
     @classmethod
-    def start(cls, model_name: str, cache_dir: str, **kwargs: Any) -> "Bm25Worker":
+    def start(cls: type[Self], model_name: str, cache_dir: str, **kwargs: Any) -> "Bm25Worker":
         return cls(model_name=model_name, cache_dir=cache_dir, **kwargs)
 
-    def process(self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
+    def process(self: Self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
         for idx, batch in items:
             onnx_output = self.model.raw_embed(batch)
             yield idx, onnx_output
 
     @staticmethod
-    def init_embedding(model_name: str, cache_dir: str, **kwargs) -> Bm25:
+    def init_embedding(model_name: str, cache_dir: str, **kwargs: Any) -> Bm25:
         return Bm25(model_name=model_name, cache_dir=cache_dir, **kwargs)
diff --git a/fastembed/sparse/bm42.py b/fastembed/sparse/bm42.py
index f06ecb7..a3b9157 100644
--- a/fastembed/sparse/bm42.py
+++ b/fastembed/sparse/bm42.py
@@ -1,7 +1,7 @@
 import math
 import string
 from pathlib import Path
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Type, Union, Self
 
 import mmh3
 import numpy as np
@@ -56,7 +56,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
     ONNX_OUTPUT_NAMES = ["attention_6"]
 
     def __init__(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
@@ -66,7 +66,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
         device_id: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ):
         """
         Args:
@@ -141,7 +141,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         self.special_tokens_ids = set(self.special_token_to_id.values())
         self.stopwords = set(self._load_stopwords(self._model_dir))
 
-    def _filter_pair_tokens(self, tokens: List[Tuple[str, Any]]) -> List[Tuple[str, Any]]:
+    def _filter_pair_tokens(self: Self, tokens: List[Tuple[str, Any]]) -> List[Tuple[str, Any]]:
         result = []
         for token, value in tokens:
             if token in self.stopwords or token in self.punctuation:
@@ -149,7 +149,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
             result.append((token, value))
         return result
 
-    def _stem_pair_tokens(self, tokens: List[Tuple[str, Any]]) -> List[Tuple[str, Any]]:
+    def _stem_pair_tokens(self: Self, tokens: List[Tuple[str, Any]]) -> List[Tuple[str, Any]]:
         result = []
         for token, value in tokens:
             processed_token = self.stemmer.stem_word(token)
@@ -167,7 +167,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         return result
 
     def _reconstruct_bpe(
-        self, bpe_tokens: Iterable[Tuple[int, str]]
+        self: Self, bpe_tokens: Iterable[Tuple[int, str]]
     ) -> List[Tuple[str, List[int]]]:
         result = []
         acc = ""
@@ -195,7 +195,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
 
         return result
 
-    def _rescore_vector(self, vector: Dict[str, float]) -> Dict[int, float]:
+    def _rescore_vector(self: Self, vector: Dict[str, float]) -> Dict[int, float]:
         """
         Orders all tokens in the vector by their importance and generates a new score based on the importance order.
         So that the scoring doesn't depend on absolute values assigned by the model, but on the relative importance.
@@ -213,7 +213,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
 
         return new_vector
 
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[SparseEmbedding]:
+    def _post_process_onnx_output(self: Self, output: OnnxOutputContext) -> Iterable[SparseEmbedding]:
         if output.input_ids is None:
             raise ValueError("input_ids must be provided for document post-processing")
 
@@ -264,11 +264,11 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
             return f.read().splitlines()
 
     def embed(
-        self,
+        self: Self,
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[SparseEmbedding]:
         """
         Encode a list of documents into list of embeddings.
@@ -305,7 +305,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
             result[token_id] = 1.0
         return result
 
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs) -> Iterable[SparseEmbedding]:
+    def query_embed(self: Self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[SparseEmbedding]:
         """
         To emulate BM25 behaviour, we don't need to use smart weights in the query, and
         it's enough to just hash the tokens and assign a weight of 1.0 to them.
@@ -332,7 +332,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
 
 
 class Bm42TextEmbeddingWorker(TextEmbeddingWorker):
-    def init_embedding(self, model_name: str, cache_dir: str, **kwargs) -> Bm42:
+    def init_embedding(self: Self, model_name: str, cache_dir: str, **kwargs: Any) -> Bm42:
         return Bm42(
             model_name=model_name,
             cache_dir=cache_dir,
diff --git a/fastembed/sparse/sparse_embedding_base.py b/fastembed/sparse/sparse_embedding_base.py
index 7a71de2..0943ea3 100644
--- a/fastembed/sparse/sparse_embedding_base.py
+++ b/fastembed/sparse/sparse_embedding_base.py
@@ -1,5 +1,5 @@
 from dataclasses import dataclass
-from typing import Dict, Iterable, Optional, Union
+from typing import Dict, Iterable, Optional, Union, Self, Any
 
 import numpy as np
 
@@ -21,7 +21,7 @@ class SparseEmbedding:
         return {i: v for i, v in zip(self.indices, self.values)}
 
     @classmethod
-    def from_dict(cls, data: Dict[int, float]) -> "SparseEmbedding":
+    def from_dict(cls: type[Self], data: Dict[int, float]) -> "SparseEmbedding":
         if len(data) == 0:
             return cls(values=np.array([]), indices=np.array([]))
         indices, values = zip(*data.items())
@@ -30,11 +30,11 @@ class SparseEmbedding:
 
 class SparseTextEmbeddingBase(ModelManagement):
     def __init__(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ):
         self.model_name = model_name
         self.cache_dir = cache_dir
@@ -42,16 +42,16 @@ class SparseTextEmbeddingBase(ModelManagement):
         self._local_files_only = kwargs.pop("local_files_only", False)
 
     def embed(
-        self,
+        self: Self,
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[SparseEmbedding]:
         raise NotImplementedError()
 
     def passage_embed(
-        self, texts: Iterable[str], **kwargs
+        self: Self, texts: Iterable[str], **kwargs: Any
     ) -> Iterable[SparseEmbedding]:
         """
         Embeds a list of text passages into a list of embeddings.
@@ -68,7 +68,7 @@ class SparseTextEmbeddingBase(ModelManagement):
         yield from self.embed(texts, **kwargs)
 
     def query_embed(
-        self, query: Union[str, Iterable[str]], **kwargs
+        self: Self, query: Union[str, Iterable[str]], **kwargs: Any
     ) -> Iterable[SparseEmbedding]:
         """
         Embeds queries
diff --git a/fastembed/sparse/sparse_text_embedding.py b/fastembed/sparse/sparse_text_embedding.py
index 8701827..528b96e 100644
--- a/fastembed/sparse/sparse_text_embedding.py
+++ b/fastembed/sparse/sparse_text_embedding.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union, Self
 
 from fastembed.common import OnnxProvider
 from fastembed.sparse.bm25 import Bm25
@@ -15,7 +15,7 @@ class SparseTextEmbedding(SparseTextEmbeddingBase):
     EMBEDDINGS_REGISTRY: List[Type[SparseTextEmbeddingBase]] = [SpladePP, Bm42, Bm25]
 
     @classmethod
-    def list_supported_models(cls) -> List[Dict[str, Any]]:
+    def list_supported_models(cls: type[Self]) -> List[Dict[str, Any]]:
         """
         Lists the supported models.
 
@@ -44,7 +44,7 @@ class SparseTextEmbedding(SparseTextEmbeddingBase):
         return result
 
     def __init__(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
@@ -52,7 +52,7 @@ class SparseTextEmbedding(SparseTextEmbeddingBase):
         cuda: bool = False,
         device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
-        **kwargs,
+        **kwargs: Any,
     ):
         super().__init__(model_name, cache_dir, threads, **kwargs)
         if model_name == "prithvida/Splade_PP_en_v1":
@@ -85,11 +85,11 @@ class SparseTextEmbedding(SparseTextEmbeddingBase):
         )
 
     def embed(
-        self,
+        self: Self,
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[SparseEmbedding]:
         """
         Encode a list of documents into list of embeddings.
@@ -108,7 +108,7 @@ class SparseTextEmbedding(SparseTextEmbeddingBase):
         """
         yield from self.model.embed(documents, batch_size, parallel, **kwargs)
 
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs) -> Iterable[SparseEmbedding]:
+    def query_embed(self: Self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[SparseEmbedding]:
         """
         Embeds queries
 
diff --git a/fastembed/sparse/splade_pp.py b/fastembed/sparse/splade_pp.py
index d3b1495..8b47555 100644
--- a/fastembed/sparse/splade_pp.py
+++ b/fastembed/sparse/splade_pp.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union, Self
 
 import numpy as np
 from fastembed.common import OnnxProvider
@@ -64,7 +64,7 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         return supported_splade_models
 
     def __init__(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
@@ -73,7 +73,7 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
         device_id: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ):
         """
         Args:
@@ -132,11 +132,11 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         )
 
     def embed(
-        self,
+        self: Self,
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[SparseEmbedding]:
         """
         Encode a list of documents into list of embeddings.
@@ -171,7 +171,7 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
 
 
 class SpladePPEmbeddingWorker(TextEmbeddingWorker):
-    def init_embedding(self, model_name: str, cache_dir: str, **kwargs) -> SpladePP:
+    def init_embedding(self: Self, model_name: str, cache_dir: str, **kwargs: Any) -> SpladePP:
         return SpladePP(
             model_name=model_name,
             cache_dir=cache_dir,
diff --git a/fastembed/sparse/utils/tokenizer.py b/fastembed/sparse/utils/tokenizer.py
index 88b6c05..2204575 100644
--- a/fastembed/sparse/utils/tokenizer.py
+++ b/fastembed/sparse/utils/tokenizer.py
@@ -5,6 +5,7 @@ from typing import List
 
 
 class SimpleTokenizer:
+    @staticmethod
     def tokenize(text: str) -> List[str]:
         text = re.sub(r"[^\w]", " ", text.lower())
         text = re.sub(r"\s+", " ", text)
diff --git a/fastembed/text/clip_embedding.py b/fastembed/text/clip_embedding.py
index 3f64b9e..50dd154 100644
--- a/fastembed/text/clip_embedding.py
+++ b/fastembed/text/clip_embedding.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, Iterable, List, Type
+from typing import Any, Dict, Iterable, List, Type, Self
 
 import numpy as np
 
@@ -41,10 +41,10 @@ class CLIPOnnxEmbedding(OnnxTextEmbedding):
 
 class CLIPEmbeddingWorker(OnnxTextEmbeddingWorker):
     def init_embedding(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: str,
-        **kwargs,
+        **kwargs: Any,
     ) -> OnnxTextEmbedding:
         return CLIPOnnxEmbedding(
             model_name=model_name,
diff --git a/fastembed/text/e5_onnx_embedding.py b/fastembed/text/e5_onnx_embedding.py
index a3a546d..10770f5 100644
--- a/fastembed/text/e5_onnx_embedding.py
+++ b/fastembed/text/e5_onnx_embedding.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Type
+from typing import Any, Dict, List, Type, Self
 
 import numpy as np
 
@@ -48,7 +48,7 @@ class E5OnnxEmbedding(OnnxTextEmbedding):
         return supported_multilingual_e5_models
 
     def _preprocess_onnx_input(
-        self, onnx_input: Dict[str, np.ndarray], **kwargs
+        self: Self, onnx_input: Dict[str, np.ndarray], **kwargs: Any
     ) -> Dict[str, np.ndarray]:
         """
         Preprocess the onnx input.
@@ -59,10 +59,10 @@ class E5OnnxEmbedding(OnnxTextEmbedding):
 
 class E5OnnxEmbeddingWorker(OnnxTextEmbeddingWorker):
     def init_embedding(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: str,
-        **kwargs,
+        **kwargs: Any,
     ) -> E5OnnxEmbedding:
         return E5OnnxEmbedding(
             model_name=model_name,
diff --git a/fastembed/text/onnx_embedding.py b/fastembed/text/onnx_embedding.py
index ec02b82..a1c6401 100644
--- a/fastembed/text/onnx_embedding.py
+++ b/fastembed/text/onnx_embedding.py
@@ -1,7 +1,7 @@
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union, Self
 
 import numpy as np
-
+from pathlib import Path
 from fastembed.common import OnnxProvider
 from fastembed.common.onnx_model import OnnxOutputContext
 from fastembed.common.utils import define_cache_dir, normalize
@@ -193,16 +193,16 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[np.ndarray]):
         return supported_onnx_models
 
     def __init__(
-        self,
+        self: Self,
         model_name: str = "BAAI/bge-small-en-v1.5",
-        cache_dir: Optional[str] = None,
+        cache_dir: Optional[str|Path] = None,
         threads: Optional[int] = None,
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
         device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
         device_id: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ):
         """
         Args:
@@ -237,11 +237,9 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[np.ndarray]):
             self.device_id = device_id
         elif self.device_ids is not None:
             self.device_id = self.device_ids[0]
-        else:
-            self.device_id = None
 
         self.model_description = self._get_model_description(model_name)
-        self.cache_dir = define_cache_dir(cache_dir)
+        self.cache_dir: Path = define_cache_dir(cache_dir)
         self._model_dir = self.download_model(
             self.model_description, self.cache_dir, local_files_only=self._local_files_only
         )
@@ -250,11 +248,11 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[np.ndarray]):
             self.load_onnx_model()
 
     def embed(
-        self,
+        self: Self,
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[np.ndarray]:
         """
         Encode a list of documents into list of embeddings.
@@ -288,14 +286,14 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[np.ndarray]):
         return OnnxTextEmbeddingWorker
 
     def _preprocess_onnx_input(
-        self, onnx_input: Dict[str, np.ndarray], **kwargs
+        self: Self, onnx_input: Dict[str, np.ndarray], **kwargs: Any
     ) -> Dict[str, np.ndarray]:
         """
         Preprocess the onnx input.
         """
         return onnx_input
 
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[np.ndarray]:
+    def _post_process_onnx_output(self: Self, output: OnnxOutputContext) -> Iterable[np.ndarray]:
         embeddings = output.model_output
         return normalize(embeddings[:, 0]).astype(np.float32)
 
@@ -312,10 +310,10 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[np.ndarray]):
 
 class OnnxTextEmbeddingWorker(TextEmbeddingWorker):
     def init_embedding(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: str,
-        **kwargs,
+        **kwargs: Any,
     ) -> OnnxTextEmbedding:
         return OnnxTextEmbedding(
             model_name=model_name,
diff --git a/fastembed/text/onnx_text_model.py b/fastembed/text/onnx_text_model.py
index 35b430d..3cfd57f 100644
--- a/fastembed/text/onnx_text_model.py
+++ b/fastembed/text/onnx_text_model.py
@@ -1,10 +1,10 @@
 import os
 from multiprocessing import get_all_start_methods
 from pathlib import Path
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Type, Union, Self
 
 import numpy as np
-from tokenizers import Encoding
+from tokenizers import Encoding # type: ignore
 
 from fastembed.common import OnnxProvider
 from fastembed.common.onnx_model import EmbeddingWorker, OnnxModel, OnnxOutputContext, T
@@ -26,10 +26,10 @@ class OnnxTextModel(OnnxModel[T]):
     def __init__(self) -> None:
         super().__init__()
         self.tokenizer = None
-        self.special_token_to_id = {}
+        self.special_token_to_id: dict[str, int] = {}
 
     def _preprocess_onnx_input(
-        self, onnx_input: Dict[str, np.ndarray], **kwargs
+        self: Self, onnx_input: Dict[str, np.ndarray], **kwargs: Any
     ) -> Dict[str, np.ndarray]:
         """
         Preprocess the onnx input.
@@ -53,18 +53,18 @@ class OnnxTextModel(OnnxModel[T]):
             cuda=cuda,
             device_id=device_id,
         )
-        self.tokenizer, self.special_token_to_id = load_tokenizer(model_dir=model_dir)
+        self.tokenizer, self.special_token_to_id = load_tokenizer(model_dir=model_dir) # type: ignore[no-any-return]
 
     def load_onnx_model(self) -> None:
         raise NotImplementedError("Subclasses must implement this method")
 
-    def tokenize(self, documents: List[str], **kwargs) -> List[Encoding]:
-        return self.tokenizer.encode_batch(documents)
+    def tokenize(self: Self, documents: List[str], **kwargs: Any) -> List[Encoding]:
+        return self.tokenizer.encode_batch(documents) # type: ignore[no-any-return]
 
     def onnx_embed(
-        self,
+        self: Self,
         documents: List[str],
-        **kwargs,
+        **kwargs: Any,
     ) -> OnnxOutputContext:
         encoded = self.tokenize(documents, **kwargs)
         input_ids = np.array([e.ids for e in encoded])
@@ -94,7 +94,7 @@ class OnnxTextModel(OnnxModel[T]):
         )
 
     def _embed_documents(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: str,
         documents: Union[str, Iterable[str]],
@@ -103,7 +103,7 @@ class OnnxTextModel(OnnxModel[T]):
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
         device_ids: Optional[List[int]] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[T]:
         is_small = False
 
@@ -144,7 +144,7 @@ class OnnxTextModel(OnnxModel[T]):
 
 
 class TextEmbeddingWorker(EmbeddingWorker):
-    def process(self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
+    def process(self: Self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
         for idx, batch in items:
             onnx_output = self.model.onnx_embed(batch)
             yield idx, onnx_output
diff --git a/fastembed/text/pooled_embedding.py b/fastembed/text/pooled_embedding.py
index f21eb4f..e75a3f2 100644
--- a/fastembed/text/pooled_embedding.py
+++ b/fastembed/text/pooled_embedding.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, Iterable, List, Type
+from typing import Any, Dict, Iterable, List, Type, Self
 
 import numpy as np
 
@@ -57,7 +57,7 @@ class PooledEmbedding(OnnxTextEmbedding):
         sum_embeddings = np.sum(token_embeddings * input_mask_expanded, axis=1)
         sum_mask = np.sum(input_mask_expanded, axis=1)
         pooled_embeddings = sum_embeddings / np.maximum(sum_mask, 1e-9)
-        return pooled_embeddings
+        return pooled_embeddings # type: ignore[no-any-return]
 
     @classmethod
     def list_supported_models(cls) -> List[Dict[str, Any]]:
@@ -79,10 +79,10 @@ class PooledEmbedding(OnnxTextEmbedding):
 
 class PooledEmbeddingWorker(OnnxTextEmbeddingWorker):
     def init_embedding(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: str,
-        **kwargs,
+        **kwargs: Any,
     ) -> OnnxTextEmbedding:
         return PooledEmbedding(
             model_name=model_name,
diff --git a/fastembed/text/pooled_normalized_embedding.py b/fastembed/text/pooled_normalized_embedding.py
index 2843ccf..e386ecf 100644
--- a/fastembed/text/pooled_normalized_embedding.py
+++ b/fastembed/text/pooled_normalized_embedding.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, Iterable, List, Type
+from typing import Any, Dict, Iterable, List, Type, Self
 
 import numpy as np
 
@@ -85,10 +85,10 @@ class PooledNormalizedEmbedding(PooledEmbedding):
 
 class PooledNormalizedEmbeddingWorker(OnnxTextEmbeddingWorker):
     def init_embedding(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: str,
-        **kwargs,
+        **kwargs: Any,
     ) -> OnnxTextEmbedding:
         return PooledNormalizedEmbedding(
             model_name=model_name,
diff --git a/fastembed/text/pooling_embedding.py b/fastembed/text/pooling_embedding.py
new file mode 100644
index 0000000..e69de29
diff --git a/fastembed/text/text_embedding.py b/fastembed/text/text_embedding.py
index 6637e1e..5d68afe 100644
--- a/fastembed/text/text_embedding.py
+++ b/fastembed/text/text_embedding.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union, Self
 
 import numpy as np
 
@@ -51,7 +51,7 @@ class TextEmbedding(TextEmbeddingBase):
         return result
 
     def __init__(
-        self,
+        self: Self,
         model_name: str = "BAAI/bge-small-en-v1.5",
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
@@ -59,7 +59,7 @@ class TextEmbedding(TextEmbeddingBase):
         cuda: bool = False,
         device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
-        **kwargs,
+        **kwargs: Any,
     ):
         super().__init__(model_name, cache_dir, threads, **kwargs)
         for EMBEDDING_MODEL_TYPE in self.EMBEDDINGS_REGISTRY:
@@ -83,11 +83,11 @@ class TextEmbedding(TextEmbeddingBase):
         )
 
     def embed(
-        self,
+        self: Self,
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[np.ndarray]:
         """
         Encode a list of documents into list of embeddings.
diff --git a/fastembed/text/text_embedding_base.py b/fastembed/text/text_embedding_base.py
index 61963fc..be00d0d 100644
--- a/fastembed/text/text_embedding_base.py
+++ b/fastembed/text/text_embedding_base.py
@@ -1,4 +1,4 @@
-from typing import Iterable, Optional, Union
+from typing import Iterable, Optional, Union, Self, Any
 
 import numpy as np
 
@@ -7,11 +7,11 @@ from fastembed.common.model_management import ModelManagement
 
 class TextEmbeddingBase(ModelManagement):
     def __init__(
-        self,
+        self: Self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ):
         self.model_name = model_name
         self.cache_dir = cache_dir
@@ -19,15 +19,15 @@ class TextEmbeddingBase(ModelManagement):
         self._local_files_only = kwargs.pop("local_files_only", False)
 
     def embed(
-        self,
+        self: Self,
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> Iterable[np.ndarray]:
         raise NotImplementedError()
 
-    def passage_embed(self, texts: Iterable[str], **kwargs) -> Iterable[np.ndarray]:
+    def passage_embed(self: Self, texts: Iterable[str], **kwargs: Any) -> Iterable[np.ndarray]:
         """
         Embeds a list of text passages into a list of embeddings.
 
@@ -43,7 +43,7 @@ class TextEmbeddingBase(ModelManagement):
         yield from self.embed(texts, **kwargs)
 
     def query_embed(
-        self, query: Union[str, Iterable[str]], **kwargs
+        self: Self, query: Union[str, Iterable[str]], **kwargs: Any
     ) -> Iterable[np.ndarray]:
         """
         Embeds queries
diff --git a/pyproject.toml b/pyproject.toml
index 2b2329e..8f6e466 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,6 +32,8 @@ pytest = "^7.4.2"
 ruff = ">=0.3.1,<1.0"
 notebook = ">=7.0.2"
 pre-commit = {version = "^3.6.2", python = ">=3.9,<3.12" }
+mypy = "^1.13.0"
+pyright = "^1.1.390"
 
 [tool.poetry.group.docs.dependencies]
 mkdocs-material = "^9.5.10"
@@ -47,3 +49,11 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.ruff]
 line-length = 99
+
+[tool.mypy]
+plugins = []
+strict = true
+disable_error_code = ["type-arg"]
+
+
+
diff --git a/pyrightconfig.json b/pyrightconfig.json
new file mode 100644
index 0000000..41ee35c
--- /dev/null
+++ b/pyrightconfig.json
@@ -0,0 +1,6 @@
+{
+  "include": ["fastembed/"],
+  "exclude": ["tests/", "docs/", "experiments/"],
+  "strict": true,
+  "venvPath": ".venv"
+}