diff --git a/model2vec/distill/distillation.py b/model2vec/distill/distillation.py
index ce587d7..35af79c 100644
--- a/model2vec/distill/distillation.py
+++ b/model2vec/distill/distillation.py
@@ -3,26 +3,21 @@
 import logging
 import os
 import re
-from typing import Literal, Union
+from typing import Optional, cast
 
 import numpy as np
 from huggingface_hub import model_info
-from sklearn.decomposition import PCA
-from tokenizers import Tokenizer
 from transformers import AutoModel, AutoTokenizer, PreTrainedModel, PreTrainedTokenizerFast
 
-from model2vec.distill.inference import create_embeddings
-from model2vec.distill.tokenizer import replace_vocabulary
+from model2vec.distill.inference import PCADimType, create_embeddings, post_process_embeddings
 from model2vec.distill.utils import select_optimal_device
 from model2vec.model import StaticModel
 from model2vec.quantization import DType, quantize_embeddings
+from model2vec.tokenizer import clean_and_create_vocabulary, replace_vocabulary, turn_tokens_into_ids
 
 logger = logging.getLogger(__name__)
 
 
-PCADimType = Union[int, None, float, Literal["auto"]]
-
-
 def distill_from_model(
     model: PreTrainedModel,
     tokenizer: PreTrainedTokenizerFast,
@@ -60,6 +55,7 @@ def distill_from_model(
     :param quantize_to: The data type to quantize to. Can be any of the DType enum members or their string equivalents.
     :param use_subword: DEPRECATED: If this is not set to None, we show a warning. It doesn't do anything.
     :return: A StaticModel
+    :raises: ValueError if the vocabulary is empty after preprocessing.
 
     """
     if use_subword is not None:
@@ -74,35 +70,51 @@ def distill_from_model(
         vocabulary = []
 
     device = select_optimal_device(device)
-    # Make a base list of tokens.
-    subword_vocab: dict[str, int] = tokenizer.get_vocab()
-    subword_tokens: list[str] = [k for k, _ in sorted(subword_vocab.items(), key=lambda x: x[1])]
 
     n_tokens_before = len(vocabulary)
-    # Clean the vocabulary by removing duplicate tokens and tokens that are in the subword vocabulary.
-    cleaned_vocabulary = _clean_vocabulary(tokenizer.backend_tokenizer, vocabulary, subword_tokens)
-    n_tokens_after = len(cleaned_vocabulary)
-    logger.info(
-        f"Adding {n_tokens_after} tokens to the vocabulary. Removed {n_tokens_before - n_tokens_after} tokens during preprocessing."
+    # Clean the vocabulary by removing duplicate tokens and tokens that are in the internal vocabulary.
+    all_tokens, backend_tokenizer = clean_and_create_vocabulary(
+        tokenizer, vocabulary, token_remove_regex=token_remove_regex
     )
+    n_tokens_after = len([token for token in all_tokens if not token.is_internal])
+    if n_tokens_before:
+        logger.info(
+            f"Adding {n_tokens_after} tokens to the vocabulary. Removed {n_tokens_before - n_tokens_after} tokens during preprocessing."
+        )
 
-    # Create the embeddings.
-    all_tokens, embeddings = create_embeddings(
-        model=model,
-        tokenizer=tokenizer,
-        tokens=cleaned_vocabulary,
-        device=device,
-        token_remove_regex=token_remove_regex,
-    )
+    if not all_tokens:
+        raise ValueError("The vocabulary is empty after preprocessing. Please check your token_remove_pattern.")
+
+    unk_token = cast(Optional[str], tokenizer.special_tokens_map.get("unk_token"))
+    pad_token = cast(Optional[str], tokenizer.special_tokens_map.get("pad_token"))
+
+    # Weird if to satsify mypy
+    if pad_token is None:
+        if unk_token is not None:
+            pad_token = unk_token
+            logger.warning(
+                "The pad token is not set. Setting it to the unk token. This is a workaround for models that don't have a pad token."
+            )
+        else:
+            pad_token = unk_token or all_tokens[0].form
+            logger.warning(
+                "The pad token is not set. Setting it to the first token in the vocabulary. This is a workaround for models that don't have a pad token."
+            )
 
-    unk_token = tokenizer.special_tokens_map.get("unk_token")
-    pad_token = tokenizer.special_tokens_map.get("pad_token")
-    # Add the cleaned vocabulary to the tokenizer.
+    # Replace the vocabulary in the tokenizer with the new vocabulary.
     backend_tokenizer = replace_vocabulary(backend_tokenizer, all_tokens, unk_token=unk_token, pad_token=pad_token)
 
-    # Post process the embeddings by applying PCA and Zipf weighting.
-    embeddings = _post_process_embeddings(np.asarray(embeddings), pca_dims, sif_coefficient=sif_coefficient)
+    logger.info(f"Creating embeddings for {len(all_tokens)} tokens")
+    # Convert tokens to IDs
+    token_ids = turn_tokens_into_ids(all_tokens, tokenizer, unk_token)
+
+    # Create the embeddings
+    embeddings = create_embeddings(
+        tokenized=token_ids, model=model, device=device, pad_token_id=tokenizer.get_vocab()[pad_token]
+    )
 
+    # Post process the embeddings by applying PCA and Zipf weighting.
+    embeddings = post_process_embeddings(np.asarray(embeddings), pca_dims, sif_coefficient=sif_coefficient)
     # Quantize the embeddings.
     embeddings = quantize_embeddings(embeddings, quantize_to)
 
@@ -227,7 +239,10 @@ def distill(
 
     """
     model: PreTrainedModel = AutoModel.from_pretrained(model_name, trust_remote_code=trust_remote_code)
-    tokenizer: PreTrainedTokenizerFast = AutoTokenizer.from_pretrained(model_name, trust_remote_code=trust_remote_code)
+    tokenizer = cast(
+        PreTrainedTokenizerFast,
+        AutoTokenizer.from_pretrained(model_name, trust_remote_code=trust_remote_code, use_fast=True),
+    )
 
     return distill_from_model(
         model=model,
@@ -241,97 +256,3 @@ def distill(
         quantize_to=quantize_to,
         use_subword=use_subword,
     )
-
-
-def _post_process_embeddings(
-    embeddings: np.ndarray, pca_dims: PCADimType, sif_coefficient: float | None = 1e-4
-) -> np.ndarray:
-    """Post process embeddings by applying PCA and SIF weighting by estimating the frequencies through Zipf's law."""
-    if pca_dims is not None:
-        if pca_dims == "auto":
-            pca_dims = embeddings.shape[1]
-        if pca_dims > embeddings.shape[1]:
-            logger.warning(
-                f"PCA dimension ({pca_dims}) is larger than the number of dimensions in the embeddings ({embeddings.shape[1]}). "
-                "Applying PCA, but not reducing dimensionality. Is this is not desired, please set `pca_dims` to None. "
-                "Applying PCA will probably improve performance, so consider just leaving it."
-            )
-            pca_dims = embeddings.shape[1]
-        if pca_dims >= embeddings.shape[0]:
-            logger.warning(
-                f"PCA dimension ({pca_dims}) is larger than the number of tokens in the vocabulary ({embeddings.shape[0]}). Not applying PCA."
-            )
-        elif pca_dims <= embeddings.shape[1]:
-            if isinstance(pca_dims, float):
-                logger.info(f"Applying PCA with {pca_dims} explained variance.")
-            else:
-                logger.info(f"Applying PCA with n_components {pca_dims}")
-
-            orig_dims = embeddings.shape[1]
-            p = PCA(n_components=pca_dims, svd_solver="full")
-            embeddings = p.fit_transform(embeddings)
-
-            if embeddings.shape[1] < orig_dims:
-                explained_variance_ratio = np.sum(p.explained_variance_ratio_)
-                explained_variance = np.sum(p.explained_variance_)
-                logger.info(f"Reduced dimensionality from {orig_dims} to {embeddings.shape[1]}.")
-                logger.info(f"Explained variance ratio: {explained_variance_ratio:.3f}.")
-                logger.info(f"Explained variance: {explained_variance:.3f}.")
-
-    if sif_coefficient is not None:
-        logger.info("Estimating word frequencies using Zipf's law, and then applying SIF.")
-        inv_rank = 1 / (np.arange(2, embeddings.shape[0] + 2))
-        proba = inv_rank / np.sum(inv_rank)
-        embeddings *= (sif_coefficient / (sif_coefficient + proba))[:, None]
-
-    return embeddings
-
-
-def _clean_vocabulary(tokenizer: Tokenizer, vocabulary: list[str], added_tokens: list[str]) -> list[str]:
-    """Cleans a vocabulary by removing duplicates and tokens that were already in the vocabulary."""
-    added_tokens_set = set(added_tokens)
-    seen_tokens = set()
-    cleaned_vocabulary = []
-    n_empty = 0
-    n_duplicates = 0
-    n_multiword = 0
-    for token in vocabulary:
-        normalizer = tokenizer.normalizer
-        if normalizer is not None:
-            token = normalizer.normalize_str(token)
-
-        if not token:
-            n_empty += 1
-            continue
-
-        pre_tokenizer = tokenizer.pre_tokenizer
-        # We need to check whether the pretokenized token is a single word or not.
-        if pre_tokenizer is not None:
-            pretokenized_tokens = pre_tokenizer.pre_tokenize_str(token)
-            if len(pretokenized_tokens) != 1:
-                n_multiword += 1
-                continue
-            new_token = pretokenized_tokens[-1][0]
-        else:
-            new_token = token
-
-        # We need to check whether the pretokenized token is in the vocabulary.
-        # But we need to return the original token, because that will be tokenized
-        # again by the tokenizer during featurization.
-        if new_token in seen_tokens or new_token in added_tokens_set:
-            n_duplicates += 1
-            continue
-
-        # Add the possibly pretokenized token to _seen_
-        seen_tokens.add(new_token)
-        # Add the original string to the vocabulary.
-        cleaned_vocabulary.append(token)
-
-    if n_duplicates:
-        logger.warning(f"Removed {n_duplicates} duplicate tokens.")
-    if n_empty:
-        logger.warning(f"Removed {n_empty} empty tokens.")
-    if n_multiword:
-        logger.warning(f"Removed {n_multiword} multiword tokens.")
-
-    return cleaned_vocabulary
diff --git a/model2vec/distill/inference.py b/model2vec/distill/inference.py
index 19f351f..cfc1c26 100644
--- a/model2vec/distill/inference.py
+++ b/model2vec/distill/inference.py
@@ -3,23 +3,23 @@
 
 import inspect
 import logging
-import re
 from pathlib import Path
-from typing import Protocol, Union
+from typing import Literal, Protocol, Union
 
 import numpy as np
 import torch
+from sklearn.decomposition import PCA
 from torch.nn.utils.rnn import pad_sequence
 from tqdm import tqdm
-from transformers import PreTrainedModel, PreTrainedTokenizerFast
+from transformers import PreTrainedModel
 from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
 
-from model2vec.distill.utils import Token, filter_vocabulary_by_regex
-
 logger = logging.getLogger(__name__)
 
 
 PathLike = Union[Path, str]
+PCADimType = Union[int, None, float, Literal["auto"]]
+
 
 _DEFAULT_BATCH_SIZE = 256
 
@@ -30,11 +30,10 @@ class ModulewithWeights(Protocol):
 
 def create_embeddings(
     model: PreTrainedModel,
-    tokenizer: PreTrainedTokenizerFast,
-    tokens: list[str],
+    tokenized: list[list[int]],
     device: str,
-    token_remove_regex: re.Pattern | None,
-) -> tuple[list[Token], np.ndarray]:
+    pad_token_id: int,
+) -> np.ndarray:
     """
     Create output embeddings for a bunch of tokens using a pretrained model.
 
@@ -42,51 +41,16 @@ def create_embeddings(
 
     :param model: The model to use.
         This should be a transformers model.
-    :param tokenizer: The tokenizer to use.
-    :param tokens: The tokens to use.
+    :param tokenized: All tokenized tokens.
     :param device: The torch device to use.
-    :param token_remove_regex: A regex pattern to remove tokens from the vocabulary.
-    :return: The tokens and output embeddings.
+    :param pad_token_id: The pad token id. Used to pad sequences.
+    :return: The output embeddings.
     """
     model = model.to(device)
 
     out_weights: np.ndarray
     intermediate_weights: list[np.ndarray] = []
 
-    out_tokens: list[Token] = []
-    tokenized: list[torch.Tensor] = []
-    pad_token = tokenizer.special_tokens_map.get("pad_token")
-    # We need to use the pad token id for padding below.
-    pad_token_id = tokenizer.convert_tokens_to_ids(pad_token)
-    unk_token = tokenizer.special_tokens_map.get("unk_token")
-
-    # Empty set if no pad or unk token is set.
-    tokens_to_keep = {pad_token, unk_token} - {None}
-
-    if token_remove_regex is not None:
-        # Sort the vocabulary by id, important for zipf.
-        sorted_vocab = sorted(tokenizer.get_vocab().items(), key=lambda x: x[1])
-        id_list = filter_vocabulary_by_regex(token_remove_regex, sorted_vocab)
-    else:
-        # If the token remove regex is None, just use all tokens.
-        id_list = list(range(len(tokenizer.get_vocab())))
-
-    added_tokens_ids = [id for token, id in tokenizer.added_tokens_encoder.items() if token not in tokens_to_keep]
-    ids = torch.Tensor(sorted(set(id_list) - set(added_tokens_ids))).long()
-
-    if ids is not None:
-        dummy_encoding = tokenizer.encode("A")
-        bos_token_id, eos_token_id = dummy_encoding[0], dummy_encoding[-1]
-
-        bos = torch.full([len(ids)], fill_value=bos_token_id)
-        eos = torch.full([len(ids)], fill_value=eos_token_id)
-
-        tokenized.extend(torch.stack([bos, ids, eos], dim=1))
-        subword_tokens = [Token(x, True) for x in tokenizer.convert_ids_to_tokens(ids.tolist())]
-        out_tokens.extend(subword_tokens)
-
-    tokenized.extend([tokenizer.encode_plus(token, return_tensors="pt")["input_ids"][0] for token in tokens])
-
     # Add token_type_ids only if the model supports it
     add_token_type_ids = "token_type_ids" in inspect.getfullargspec(model.forward).args
 
@@ -98,7 +62,7 @@ def create_embeddings(
     pbar = tqdm(total=len(sorted_tokenized), desc="Encoding tokens", unit=" tokens")
 
     for batch_idx in range(0, len(sorted_tokenized), _DEFAULT_BATCH_SIZE):
-        batch = sorted_tokenized[batch_idx : batch_idx + _DEFAULT_BATCH_SIZE]
+        batch = [torch.Tensor(x).long() for x in sorted_tokenized[batch_idx : batch_idx + _DEFAULT_BATCH_SIZE]]
 
         encoded = {}
         encoded["input_ids"] = pad_sequence(batch, batch_first=True, padding_value=pad_token_id)
@@ -113,10 +77,11 @@ def create_embeddings(
 
     # Sort the output back to the original order
     intermediate_weights = [intermediate_weights[i] for i in np.argsort(sort_order)]
-    out_tokens.extend([Token(x, False) for x in tokens])
     out_weights = np.stack(intermediate_weights)
 
-    return out_tokens, out_weights
+    out_weights = np.nan_to_num(out_weights)
+
+    return out_weights
 
 
 @torch.no_grad()
@@ -147,3 +112,47 @@ def _encode_mean_using_model(model: PreTrainedModel, encodings: dict[str, torch.
     result = torch.bmm(mask[:, None, :].float(), out).squeeze(1)
 
     return result
+
+
+def post_process_embeddings(
+    embeddings: np.ndarray, pca_dims: PCADimType, sif_coefficient: float | None = 1e-4
+) -> np.ndarray:
+    """Post process embeddings by applying PCA and SIF weighting by estimating the frequencies through Zipf's law."""
+    if pca_dims is not None:
+        if pca_dims == "auto":
+            pca_dims = embeddings.shape[1]
+        if pca_dims > embeddings.shape[1]:
+            logger.warning(
+                f"PCA dimension ({pca_dims}) is larger than the number of dimensions in the embeddings ({embeddings.shape[1]}). "
+                "Applying PCA, but not reducing dimensionality. Is this is not desired, please set `pca_dims` to None. "
+                "Applying PCA will probably improve performance, so consider just leaving it."
+            )
+            pca_dims = embeddings.shape[1]
+        if pca_dims >= embeddings.shape[0]:
+            logger.warning(
+                f"PCA dimension ({pca_dims}) is larger than the number of tokens in the vocabulary ({embeddings.shape[0]}). Not applying PCA."
+            )
+        elif pca_dims <= embeddings.shape[1]:
+            if isinstance(pca_dims, float):
+                logger.info(f"Applying PCA with {pca_dims} explained variance.")
+            else:
+                logger.info(f"Applying PCA with n_components {pca_dims}")
+
+            orig_dims = embeddings.shape[1]
+            p = PCA(n_components=pca_dims, svd_solver="full")
+            embeddings = p.fit_transform(embeddings)
+
+            if embeddings.shape[1] < orig_dims:
+                explained_variance_ratio = np.sum(p.explained_variance_ratio_)
+                explained_variance = np.sum(p.explained_variance_)
+                logger.info(f"Reduced dimensionality from {orig_dims} to {embeddings.shape[1]}.")
+                logger.info(f"Explained variance ratio: {explained_variance_ratio:.3f}.")
+                logger.info(f"Explained variance: {explained_variance:.3f}.")
+
+    if sif_coefficient is not None:
+        logger.info("Estimating word frequencies using Zipf's law, and then applying SIF.")
+        inv_rank = 1 / (np.arange(2, embeddings.shape[0] + 2))
+        proba = inv_rank / np.sum(inv_rank)
+        embeddings *= (sif_coefficient / (sif_coefficient + proba))[:, None]
+
+    return embeddings
diff --git a/model2vec/distill/tokenizer.py b/model2vec/distill/tokenizer.py
deleted file mode 100644
index 858876c..0000000
--- a/model2vec/distill/tokenizer.py
+++ /dev/null
@@ -1,197 +0,0 @@
-from __future__ import annotations
-
-import json
-import logging
-from typing import Any
-
-from tokenizers import Tokenizer
-
-from model2vec.distill.utils import Token
-
-logger = logging.getLogger(__name__)
-
-
-_DEFAULT_POST_PROCESSOR_TEMPLATE = {
-    "type": "TemplateProcessing",
-    "single": [{"Sequence": {"id": "A", "type_id": 0}}],
-    "pair": [{"Sequence": {"id": "A", "type_id": 0}}, {"Sequence": {"id": "B", "type_id": 0}}],
-    "special_tokens": {},
-}
-
-
-def _pre_tokenize_vocabulary(tokenizer: Tokenizer, tokens: list[Token]) -> list[str]:
-    """
-    Apply pre-tokenization to vocabulary tokens if a pre-tokenizer is present.
-
-    Only pre-tokenizes tokens that are not already in the tokenizer's vocabulary,
-    to avoid processing tokens twice.
-
-    :param tokenizer: The tokenizer to use.
-    :param tokens: The tokens to pre-tokenize.
-    :return: The pre-tokenized tokens.
-    """
-    pre_tokenized_tokens = []
-
-    if tokenizer.pre_tokenizer is not None:
-        for token in tokens:
-            if token.is_original:
-                # Original tokens do not need to be pre-tokenized.
-                pre_tokenized_tokens.append(token.form)
-            else:
-                # We know 100% sure that all pretokenized tokens will have length 1.
-                pretokenized_tokens, _ = zip(*tokenizer.pre_tokenizer.pre_tokenize_str(f" {token.form}"))
-                pre_tokenized_tokens.append(pretokenized_tokens[-1])
-    else:
-        pre_tokenized_tokens = [token.form for token in tokens]
-
-    return pre_tokenized_tokens
-
-
-def _remap_added_tokens(
-    special_tokens: list[dict[str, Any]],
-    vocabulary: list[str],
-) -> list[dict[str, Any]]:
-    """
-    Remap special tokens in the tokenizer.
-
-    This function updates the special tokens in the tokenizer based on a mapping provided.
-    It also ensures that the special tokens are present in the vocabulary.
-
-    :param special_tokens: The special tokens to remap.
-    :param vocabulary: The vocabulary as a list of tokens.
-    :return: The updated special tokens.
-    """
-    # Deepcopy
-    special_tokens = [{**x} for x in special_tokens]
-    for token in special_tokens:
-        token["id"] = vocabulary.index(token["content"])
-
-    return special_tokens
-
-
-def _make_new_merges_from_vocab(
-    merges: list[tuple[str, str]], tokens: list[str], special_tokens: set[str | None]
-) -> list[tuple[str, str]]:
-    """
-    Generate new merges from a vocabulary.
-
-    This function creates new merge pairs from a given vocabulary of tokens.
-    The merges are used to build or extend a tokenizer's merge table.
-
-    :param merges: The list of existing merges in the form (first, second) where first and second are tokens.
-    :param tokens: The list of tokens (vocabulary) from which to generate new merges.
-    :param special_tokens: Tokens that should not be merged.
-    :return: The list of new merges in the form (first, second) where first and second are tokens.
-    """
-    new_merges = merges.copy()
-    current_vocab = set(tokens) - special_tokens
-    already_merged = set("".join(merge) for merge in merges)
-
-    for token in tokens:
-        if token in special_tokens:
-            continue
-        if token in already_merged:
-            continue
-        if len(token) == 1:
-            continue
-        merges = []
-        for index in range(1, len(token)):
-            first, second = token[:index], token[index:]
-            if first in current_vocab and second in current_vocab:
-                merges.append((first, second))
-        if not merges:
-            logger.warning(f"Token {token} has no merges.")
-            continue
-        new_merges.extend(merges)
-
-    return new_merges
-
-
-def _process_wordpiece(
-    tokenizer_json: dict[str, Any], pre_tokenized_tokens: list[str], unk_token: str | None
-) -> dict[str, Any]:
-    """Process the WordPiece tokenizer JSON."""
-    tokenizer_json["model"]["unk_token"] = unk_token
-    tokenizer_json["model"]["vocab"] = {token: idx for idx, token in enumerate(pre_tokenized_tokens)}
-
-    return tokenizer_json
-
-
-def _process_bpe(tokenizer_json: dict[str, Any], pre_tokenized_tokens: list[str]) -> dict[str, Any]:
-    """Process the BPE tokenizer JSON."""
-    tokenizer_json = _process_wordpiece(tokenizer_json, pre_tokenized_tokens, None)
-    merges = tokenizer_json["model"]["merges"]
-    merges = _make_new_merges_from_vocab(merges, pre_tokenized_tokens, {"[UNK]", "[PAD]"})
-    tokenizer_json["model"]["merges"] = merges
-
-    return tokenizer_json
-
-
-def _process_unigram(tokenizer_json: dict[str, Any], pre_tokenized_tokens: list[str], unk_token: str) -> dict[str, Any]:
-    """Process the Unigram tokenizer JSON."""
-    current_probas = dict(tokenizer_json["model"]["vocab"])
-    avg_proba = sum(current_probas.values()) / len(current_probas)
-    new_probas = {word: current_probas.get(word, avg_proba) for word in pre_tokenized_tokens}
-    tokenizer_json["model"]["vocab"] = sorted(new_probas.items(), key=lambda x: x[1], reverse=True)
-
-    tokens, _ = zip(*tokenizer_json["model"]["vocab"])
-    tokenizer_json["model"]["unk_id"] = list(tokens).index(unk_token)
-
-    return tokenizer_json
-
-
-def replace_vocabulary(
-    tokenizer: Tokenizer, new_vocabulary: list[Token], unk_token: str | None, pad_token: str | None
-) -> Tokenizer:
-    """Replace the vocabulary of a tokenizer with a new one."""
-    tokenizer_json: dict[str, Any] = json.loads(tokenizer.to_str())
-
-    # NOTE: all tokens have been normalized before.
-    # Very careful, we need to pretokenize words before adding them to the vocabulary.
-    # But only if they are not part of the original vocabulary.
-    pre_tokenized_tokens = _pre_tokenize_vocabulary(tokenizer, new_vocabulary)
-
-    model_type = tokenizer_json["model"]["type"]
-    added_tokens: list[dict[str, Any]] = tokenizer_json["added_tokens"]
-
-    # We need to remove the added tokens but keep [UNK] and [PAD] tokens.
-    added_tokens = _rename_added_token(unk_token, "[UNK]", added_tokens, pre_tokenized_tokens)
-    added_tokens = _rename_added_token(pad_token, "[PAD]", added_tokens, pre_tokenized_tokens)
-
-    # Remove old added tokens from added tokens
-    tokenizer_json["added_tokens"] = [x for x in added_tokens if x["content"] in {"[UNK]", "[PAD]"}]
-
-    if model_type == "WordPiece":
-        tokenizer_json = _process_wordpiece(tokenizer_json, pre_tokenized_tokens, "[UNK]")
-    elif model_type == "BPE":
-        tokenizer_json = _process_bpe(tokenizer_json, pre_tokenized_tokens)
-    elif model_type == "Unigram":
-        tokenizer_json = _process_unigram(tokenizer_json, pre_tokenized_tokens, "[UNK]")
-    else:
-        raise ValueError(f"Unknown model type {model_type}")
-
-    # Remap special tokens
-    tokenizer_json["added_tokens"] = _remap_added_tokens(
-        special_tokens=tokenizer_json["added_tokens"],
-        vocabulary=pre_tokenized_tokens,
-    )
-    tokenizer_json["post_processor"] = _DEFAULT_POST_PROCESSOR_TEMPLATE
-
-    return Tokenizer.from_str(json.dumps(tokenizer_json))
-
-
-def _rename_added_token(
-    form: str | None, new_form: str, added_tokens: list[dict[str, Any]], vocabulary: list[str]
-) -> list[dict[str, Any]]:
-    """Rename special tokens in the tokenizer."""
-    if form is None:
-        return added_tokens
-
-    idx = vocabulary.index(form)
-    added_token = [x for x in added_tokens if x["content"] == form]
-    if added_token:
-        added_token[0]["id"] = idx
-        added_token[0]["content"] = new_form
-        vocabulary[idx] = new_form
-
-    return added_tokens
diff --git a/model2vec/distill/utils.py b/model2vec/distill/utils.py
index 269858c..3e81004 100644
--- a/model2vec/distill/utils.py
+++ b/model2vec/distill/utils.py
@@ -1,7 +1,5 @@
 from __future__ import annotations
 
-import re
-from dataclasses import dataclass
 from logging import getLogger
 
 import torch
@@ -9,14 +7,6 @@
 logger = getLogger(__name__)
 
 
-@dataclass
-class Token:
-    """A class to represent a token."""
-
-    form: str
-    is_original: bool
-
-
 def select_optimal_device(device: str | None) -> str:
     """
     Guess what your optimal device should be based on backend availability.
@@ -36,23 +26,3 @@ def select_optimal_device(device: str | None) -> str:
         logger.info(f"Automatically selected device: {device}")
 
     return device
-
-
-def filter_vocabulary_by_regex(token_remove_regex: re.Pattern, tokens: list[tuple[str, int]]) -> list[int]:
-    """
-    Filter a sorted vocabulary by a regex pattern and return their ids.
-
-    :param token_remove_regex: The regex pattern to filter by.
-    :param tokens: The tokens to filter. This should be a list of tuples with the token and its id.
-    :return: The ids of the tokens left after filtering.
-    :raises ValueError: If no tokens are left after filtering.
-    """
-    id_list = []
-    for token, id in tokens:
-        if not token_remove_regex.match(token):
-            id_list.append(id)
-
-    if not id_list:
-        raise ValueError("No tokens left after filtering by regex.")
-
-    return id_list
diff --git a/model2vec/model.py b/model2vec/model.py
index 200b95d..9e186e1 100644
--- a/model2vec/model.py
+++ b/model2vec/model.py
@@ -130,6 +130,8 @@ def tokenize(self, sentences: Sequence[str], max_length: int | None = None) -> l
             m = max_length * self.median_token_length
             sentences = [sentence[:m] for sentence in sentences]
 
+        max_len = max([len(sentence) for sentence in sentences])
+        # self.tokenizer.model.max_input_chars_per_word = max_len + 1
         if self._can_encode_fast:
             encodings: list[Encoding] = self.tokenizer.encode_batch_fast(sentences, add_special_tokens=False)
         else:
diff --git a/model2vec/tokenizer/__init__.py b/model2vec/tokenizer/__init__.py
new file mode 100644
index 0000000..4cfe498
--- /dev/null
+++ b/model2vec/tokenizer/__init__.py
@@ -0,0 +1,12 @@
+from model2vec.utils import importable
+
+importable("transformers", "tokenizer")
+
+from model2vec.tokenizer.tokenizer import (
+    clean_and_create_vocabulary,
+    create_tokenizer,
+    replace_vocabulary,
+    turn_tokens_into_ids,
+)
+
+__all__ = ["clean_and_create_vocabulary", "create_tokenizer", "turn_tokens_into_ids", "replace_vocabulary"]
diff --git a/model2vec/tokenizer/datamodels.py b/model2vec/tokenizer/datamodels.py
new file mode 100644
index 0000000..6aa5ecf
--- /dev/null
+++ b/model2vec/tokenizer/datamodels.py
@@ -0,0 +1,14 @@
+from dataclasses import dataclass
+
+
+@dataclass
+class Token:
+    """A class to represent a token."""
+
+    form: str
+    # The normalized and pretokenized form of the token
+    normalized_form: str
+    # Whether the word is a continuing subword.
+    is_subword: bool
+    # Whether the token is internal to the model.
+    is_internal: bool
diff --git a/model2vec/tokenizer/model.py b/model2vec/tokenizer/model.py
new file mode 100644
index 0000000..84c936a
--- /dev/null
+++ b/model2vec/tokenizer/model.py
@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+
+def process_tokenizer(
+    tokenizer_json: dict[str, Any], pre_tokenized_tokens: list[str], unk_token: str | None
+) -> dict[str, Any]:
+    """Process the WordPiece tokenizer JSON."""
+    if tokenizer_json["model"]["type"] == "Unigram":
+        return _process_unigram(tokenizer_json, pre_tokenized_tokens, unk_token)
+    tokenizer_json["model"]["type"] = "Unigram"
+    tokenizer_json["model"]["unk_id"] = pre_tokenized_tokens.index(unk_token) if unk_token else None
+
+    token_weights = np.asarray([_calculate_token_weight_for_unigram(token) for token in pre_tokenized_tokens])
+    proba = (token_weights / np.sum(token_weights)).tolist()
+    tokenizer_json["model"]["vocab"] = [(token, np.log(p)) for token, p in zip(pre_tokenized_tokens, proba)]
+
+    return tokenizer_json
+
+
+def _process_unigram(
+    tokenizer_json: dict[str, Any], pre_tokenized_tokens: list[str], unk_token: str | None
+) -> dict[str, Any]:
+    """Process the Unigram tokenizer JSON."""
+    current_probas = dict(tokenizer_json["model"]["vocab"])
+    avg_proba = sum(current_probas.values()) / len(current_probas)
+    new_probas = [[word, current_probas.get(word, avg_proba)] for word in pre_tokenized_tokens]
+    tokenizer_json["model"]["vocab"] = new_probas
+
+    tokens, _ = zip(*tokenizer_json["model"]["vocab"])
+    if unk_token is not None:
+        tokenizer_json["model"]["unk_id"] = list(tokens).index(unk_token)
+
+    return tokenizer_json
+
+
+def _calculate_token_weight_for_unigram(token: str) -> float:
+    """Calculate the token weight for Unigram."""
+    # Always prefer longer tokens.
+    return len(token) + token.count("▁")
diff --git a/model2vec/tokenizer/normalizer.py b/model2vec/tokenizer/normalizer.py
new file mode 100644
index 0000000..c54308d
--- /dev/null
+++ b/model2vec/tokenizer/normalizer.py
@@ -0,0 +1,34 @@
+from string import punctuation
+
+from tokenizers import Regex, Tokenizer
+from tokenizers.normalizers import Normalizer, Replace, Sequence, Strip
+
+
+def replace_normalizer(
+    tokenizer: Tokenizer,
+) -> Tokenizer:
+    """
+    Replace the normalizer for the tokenizer.
+
+    The new normalizer will replace punctuation with a space before and after the punctuation.
+    It will also replace multiple spaces with a single space and strip the right side of the string.
+    If the tokenizer already has a normalizer, it will be added to the new normalizer.
+    If the tokenizer does not have a normalizer, a new normalizer will be created.
+
+    :param tokenizer: The tokenizer to change.
+    :return: The tokenizer with a replaced normalizer.
+    """
+    normalizer = tokenizer.normalizer
+    new_normalizers = []
+    for char in punctuation:
+        new_normalizers.append(Replace(char, f" {char} "))
+
+    new_normalizers.append(Replace(Regex(r"\s+"), " "))
+    new_normalizers.append(Strip(right=True))
+    if normalizer is None:
+        normalizer = Sequence(new_normalizers)  # type: ignore
+    else:
+        normalizer = Sequence([normalizer] + new_normalizers)  # type: ignore
+    tokenizer.normalizer = normalizer  # type: ignore
+
+    return tokenizer
diff --git a/model2vec/tokenizer/pretokenizer.py b/model2vec/tokenizer/pretokenizer.py
new file mode 100644
index 0000000..ac7ee42
--- /dev/null
+++ b/model2vec/tokenizer/pretokenizer.py
@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from tokenizers import Tokenizer
+
+_FORBIDDEN_PRETOKENIZERS = (
+    "WhiteSpace",
+    "WhitespaceSplit",
+    "BertPreTokenizer",
+    "CharDelimiterSplit",
+    "Punctuation",
+    "Split",
+    "UnicodeScripts",
+)
+_BASIC_METASPACE = {"type": "Metaspace", "replacement": "▁", "prepend_scheme": "always", "split": False}
+
+
+def _fix_single_pretokenizer(pre_tokenizer: dict[str, Any]) -> dict[str, Any] | None:
+    """Fixes a single pretokenizer to allow multiword units."""
+    if pre_tokenizer["type"] in _FORBIDDEN_PRETOKENIZERS:
+        return None
+    if pre_tokenizer["type"] == "ByteLevel":
+        pre_tokenizer["add_prefix_space"] = True
+        pre_tokenizer["use_regex"] = False
+    if pre_tokenizer["type"] == "Metaspace":
+        pre_tokenizer["split"] = False
+        pre_tokenizer["prepend_scheme"] = "always"
+
+    return pre_tokenizer
+
+
+def replace_pretokenizer(tokenizer: Tokenizer) -> Tokenizer:
+    """Fixes a single pretokenizer to allow multiword units."""
+    tokenizer_json = json.loads(tokenizer.to_str())
+    pre_tokenizer_json = tokenizer_json.get("pre_tokenizer", None)
+
+    if pre_tokenizer_json is None:
+        pre_tokenizer_json = _BASIC_METASPACE
+
+    elif pre_tokenizer_json["type"] == "Sequence":
+        new_pretokenizers = []
+        for single_pretokenizer in pre_tokenizer_json["pretokenizers"]:
+            new_pretokenizer = _fix_single_pretokenizer(single_pretokenizer)
+            if new_pretokenizer is not None:
+                new_pretokenizers.append(new_pretokenizer)
+
+        if new_pretokenizers:
+            pre_tokenizer_json["pretokenizers"] = new_pretokenizers
+        else:
+            pre_tokenizer_json = _BASIC_METASPACE
+
+    pre_tokenizer_json = _fix_single_pretokenizer(pre_tokenizer_json) or _BASIC_METASPACE
+    tokenizer_json["pre_tokenizer"] = pre_tokenizer_json
+
+    return tokenizer.from_str(json.dumps(tokenizer_json))
diff --git a/model2vec/tokenizer/tokenizer.py b/model2vec/tokenizer/tokenizer.py
new file mode 100644
index 0000000..7e7bcc5
--- /dev/null
+++ b/model2vec/tokenizer/tokenizer.py
@@ -0,0 +1,382 @@
+from __future__ import annotations
+
+import json
+import logging
+import re
+from typing import Any, Optional, cast
+
+from tokenizers import Tokenizer
+from tokenizers.normalizers import Normalizer
+from tokenizers.pre_tokenizers import (
+    PreTokenizer,
+)
+from transformers import PreTrainedTokenizerFast
+
+from model2vec.tokenizer.datamodels import Token
+from model2vec.tokenizer.model import process_tokenizer
+from model2vec.tokenizer.normalizer import replace_normalizer
+from model2vec.tokenizer.pretokenizer import replace_pretokenizer
+
+logger = logging.getLogger(__name__)
+
+
+_DEFAULT_POST_PROCESSOR_TEMPLATE = {
+    "type": "TemplateProcessing",
+    "single": [{"Sequence": {"id": "A", "type_id": 0}}],
+    "pair": [{"Sequence": {"id": "A", "type_id": 0}}, {"Sequence": {"id": "B", "type_id": 0}}],
+    "special_tokens": {},
+}
+
+
+def _remap_added_tokens(
+    special_tokens: list[dict[str, Any]],
+    vocabulary: list[str],
+) -> list[dict[str, Any]]:
+    """
+    Remap special tokens in the tokenizer.
+
+    This function updates the special tokens in the tokenizer based on a mapping provided.
+    It also ensures that the special tokens are present in the vocabulary.
+
+    :param special_tokens: The special tokens to remap.
+    :param vocabulary: The vocabulary as a list of tokens.
+    :return: The updated special tokens.
+    """
+    # Deepcopy
+    special_tokens = [{**x} for x in special_tokens]
+    for token in special_tokens:
+        token["id"] = vocabulary.index(token["content"])
+
+    return special_tokens
+
+
+def replace_vocabulary(
+    tokenizer: Tokenizer, new_vocabulary: list[Token], unk_token: str | None, pad_token: str | None
+) -> Tokenizer:
+    """Replace the vocabulary of a tokenizer with a new one."""
+    tokenizer_json: dict[str, Any] = json.loads(tokenizer.to_str())
+    added_tokens: list[dict[str, Any]] = tokenizer_json["added_tokens"]
+
+    pre_tokenized_tokens = [x.normalized_form for x in new_vocabulary]
+
+    # We need to remove the added tokens but keep [UNK] and [PAD] tokens.
+    added_tokens = _rename_added_token(unk_token, "[UNK]", added_tokens, pre_tokenized_tokens)
+    added_tokens = _rename_added_token(pad_token, "[PAD]", added_tokens, pre_tokenized_tokens)
+
+    # Remove old added tokens from added tokens
+    tokenizer_json["added_tokens"] = [x for x in added_tokens if x["content"] in {"[UNK]", "[PAD]"}]
+    tokenizer_json = process_tokenizer(tokenizer_json, pre_tokenized_tokens, "[UNK]")
+
+    # Remap special tokens
+    tokenizer_json["added_tokens"] = _remap_added_tokens(
+        special_tokens=tokenizer_json["added_tokens"],
+        vocabulary=pre_tokenized_tokens,
+    )
+    tokenizer_json["post_processor"] = _DEFAULT_POST_PROCESSOR_TEMPLATE
+
+    return Tokenizer.from_str(json.dumps(tokenizer_json))
+
+
+def _rename_added_token(
+    form: str | None, new_form: str, added_tokens: list[dict[str, Any]], vocabulary: list[str]
+) -> list[dict[str, Any]]:
+    """Rename added tokens in the tokenizer."""
+    if form is None:
+        return added_tokens
+
+    idx = vocabulary.index(form)
+    added_token = [x for x in added_tokens if x["content"] == form]
+    if added_token:
+        added_token[0]["id"] = idx
+        added_token[0]["content"] = new_form
+        vocabulary[idx] = new_form
+
+    return added_tokens
+
+
+def clean_and_create_vocabulary(
+    tokenizer: PreTrainedTokenizerFast,
+    vocabulary: list[str],
+    token_remove_regex: re.Pattern | None,
+) -> tuple[list[Token], Tokenizer]:
+    """Cleans a vocabulary by removing duplicates and tokens that were already in the vocabulary."""
+    seen_tokens = set()
+    post_normalize_seen_tokens = set()
+    n_empty = 0
+    n_duplicates = 0
+
+    backend_tokenizer = tokenizer.backend_tokenizer
+
+    # Make a base list of tokens.
+    internal_vocab: dict[str, int] = tokenizer.get_vocab()
+    internal_tokens: list[str] = [k for k, _ in sorted(internal_vocab.items(), key=lambda x: x[1])]
+
+    # Copy the backend tokenizer to avoid modifying the original.
+    backend_tokenizer = backend_tokenizer.from_str(backend_tokenizer.to_str())
+    backend_tokenizer = replace_normalizer(backend_tokenizer)
+
+    cleaned_vocabulary = _process_internal_tokens(tokenizer, backend_tokenizer, internal_tokens, token_remove_regex)
+    internal_tokens_set = {token.form for token in cleaned_vocabulary}
+
+    normalizer: Normalizer | None = backend_tokenizer.normalizer
+    for token in vocabulary:
+        if normalizer is not None:
+            token = cast(str, normalizer.normalize_str(token))
+
+        if not token:
+            n_empty += 1
+            continue
+
+        pre_tokenizer: PreTokenizer | None = backend_tokenizer.pre_tokenizer
+        normalized_token = token
+        if pre_tokenizer is not None:
+            normalized_token = _normalize_vocabulary_token(
+                token=token,
+                pre_tokenizer=pre_tokenizer,
+            )
+
+        # We need to check whether the pretokenized token is in the vocabulary.
+        # But we need to return the original token, because that will be tokenized
+        # again by the tokenizer during featurization.
+        if normalized_token in seen_tokens or normalized_token in internal_tokens_set:
+            n_duplicates += 1
+            continue
+
+        # Add the possibly pretokenized token to seen
+        seen_tokens.add(normalized_token)
+
+        # After checking the token exists, we need to normalize it into the token
+        # it will become. For byte tokens, this means we don't do anything. For
+        # other types of tokens, we will insert a metaspace.
+        # In the case of multiword tokens, we replace any spaces with the metaspace
+        # or byte prefix token.
+        if not normalized_token.startswith(("▁", "Ġ")):
+            normalized_token = normalized_token.replace(" ", "▁")
+            normalized_token = f"▁{normalized_token}"
+        else:
+            normalized_token = normalized_token.replace(" ", normalized_token[0])
+
+        if normalized_token in post_normalize_seen_tokens:
+            n_duplicates += 1
+            continue
+
+        post_normalize_seen_tokens.add(normalized_token)
+        # Add the original string to the vocabulary.
+        cleaned_vocabulary.append(
+            Token(form=token, normalized_form=normalized_token, is_subword=False, is_internal=False)
+        )
+
+    if n_duplicates:
+        logger.warning(f"Removed {n_duplicates} duplicate tokens.")
+    if n_empty:
+        logger.warning(f"Removed {n_empty} empty tokens.")
+
+    return cleaned_vocabulary, replace_pretokenizer(backend_tokenizer)
+
+
+def _process_internal_tokens(
+    tokenizer: PreTrainedTokenizerFast,
+    backend_tokenizer: Tokenizer,
+    internal_tokens: list[str],
+    token_remove_regex: re.Pattern | None,
+) -> list[Token]:
+    """Clean internal tokens."""
+    # Get the pad and unk token from the tokenizer.
+    pad_token: str | None = tokenizer.special_tokens_map.get("pad_token")  # type: ignore[assignment]
+    unk_token: str | None = tokenizer.special_tokens_map.get("unk_token")  # type: ignore[assignment]
+    # Empty set if no pad or unk token is set.
+    added_tokens_to_keep: set[str] = {x for x in (pad_token, unk_token) if x is not None}
+    added_tokens_to_remove = set(tokenizer.added_tokens_encoder) - added_tokens_to_keep
+    cleaned_internal_tokens: list[Token] = []
+
+    # Figure out whether token is a subword or not.
+    encoded = backend_tokenizer.encode(f" {'a' * 25}", add_special_tokens=False)
+    first_token, second_token, *_ = encoded.tokens
+    # Isolate the prefix. We can't do first_token[0] because we don't know
+    # how long the prefix is.
+    # e.g., "Ġaaaa" -> "Ġ"
+    a_index = None if "a" not in first_token else first_token.index("a")
+    word_prefix = first_token[:a_index]
+    is_byte_prefix = word_prefix == "Ġ"
+    second_token = encoded.tokens[1]
+    # The second token is the first subword token.
+    # If a tokenizer uses subwords, this token will have been prefixed.
+    # We don't know how long the prefix is.
+    a_index = None if "a" not in second_token else second_token.index("a")
+    subword_prefix = second_token[:a_index]
+
+    pre_tokenizer: PreTokenizer | None = backend_tokenizer.pre_tokenizer
+
+    for token in internal_tokens:
+        # Create the token objects. If this returns None, it was unsucessful for some reason.
+        if token_object := _create_single_internal_token(
+            token=token,
+            subword_prefix=subword_prefix,
+            word_prefix=word_prefix,
+            pre_tokenizer=pre_tokenizer,
+            is_byte_prefix=is_byte_prefix,
+            token_remove_regex=token_remove_regex,
+            added_tokens_to_keep=added_tokens_to_keep,
+            added_tokens_to_remove=added_tokens_to_remove,
+        ):
+            cleaned_internal_tokens.append(token_object)
+
+    if len(cleaned_internal_tokens) != len(internal_tokens):
+        logger.info(
+            f"Removed {len(internal_tokens) - len(cleaned_internal_tokens)} internal tokens from the vocabulary."
+        )
+
+    return cleaned_internal_tokens
+
+
+def _create_single_internal_token(
+    token: str,
+    subword_prefix: str,
+    word_prefix: str,
+    pre_tokenizer: PreTokenizer | None,
+    is_byte_prefix: bool,
+    token_remove_regex: re.Pattern | None,
+    added_tokens_to_keep: set[str],
+    added_tokens_to_remove: set[str],
+) -> Token | None:
+    """Create a token object from a string."""
+    if token in added_tokens_to_remove:
+        # We remove any tokens that are added tokens that aren't [UNK] or [PAD].
+        return None
+    if token in added_tokens_to_keep:
+        # Don't put added tokens through the regular motions.
+        return Token(form=token, normalized_form=token, is_subword=False, is_internal=True)
+    if token_remove_regex and token_remove_regex.match(token):
+        # If the regex matches, remove the token.
+        return None
+
+    # A token is a subword if there is a subword prefix and the word
+    # starts with a subword prefix, or if there is a WORD prefix, and the word
+    # does not start with this prefix. For metaspace tokenizers, for example:
+    # "doghouse" -> ["_dog", "house"]
+    # So we can only tell that "house" is a subword by knowing that it is not prefixed
+    # and word-initial tokens are.
+    is_subword = False
+    if subword_prefix:
+        is_subword = bool(token.startswith(subword_prefix))
+    if word_prefix:
+        is_subword = not bool(token.startswith(word_prefix))
+
+    # Byte prefixed tokenizers don't need to be checked.
+    if pre_tokenizer is not None and not is_byte_prefix:
+        # We need to check the thing without prefixes. If we have a word prefix,
+        # we need to check tokens that have are subwords. Other way around for subword
+        # prefixes.
+        if (subword_prefix and not is_subword) or (word_prefix and is_subword):
+            # If this is True, the token is unreachable, even though it is a subword token.
+            if len(pre_tokenizer.pre_tokenize_str(token)) > 1:
+                return None
+
+    # Turn a token into a normalized form for later processing.
+    normalized_form = _create_normalized_form(token, subword_prefix, word_prefix, is_byte_prefix, is_subword)
+
+    return Token(form=token, normalized_form=normalized_form, is_subword=is_subword, is_internal=True)
+
+
+def _create_normalized_form(
+    token: str, subword_prefix: str, word_prefix: str, is_byte_prefix: bool, is_subword: bool
+) -> str:
+    """Turn an internal token string into a normalized form."""
+    # We don't need to check byte prefixed strings.
+    if is_byte_prefix:
+        return token
+    # We need to check if the token is a subword or not and remove the prefix.
+    if is_subword:
+        return token.removeprefix(subword_prefix)
+    # If the token is not a subword, we need to remove the word prefix, and add metaspace.
+    return f"▁{token.removeprefix(word_prefix)}"
+
+
+def turn_tokens_into_ids(
+    tokens: list[Token], tokenizer: PreTrainedTokenizerFast, unk_token: str | None
+) -> list[list[int]]:
+    """
+    Convert a list of Token objects to their corresponding token ID sequences.
+
+    :param tokens: List of Token objects to convert
+    :param tokenizer: The tokenizer to use for converting tokens to IDs
+    :param unk_token: The string form of the unk token.
+    :return: List of token IDs corresponding to the input tokens
+    :raises ValueError: If the tokenizer returns an unexpected number of tokens for a single token
+    """
+    unk_id = None if unk_token is None else tokenizer.convert_tokens_to_ids(unk_token)
+
+    encoding = tokenizer.encode("a", add_special_tokens=True)
+
+    if len(encoding) != 3:
+        raise ValueError(f"Tokenizer returned {len(encoding)} tokens for a single token. This is not supported.")
+    bos, _, eos = encoding
+
+    token_ids: list[list[int]] = []
+    for token in tokens:
+        if token.is_internal:
+            # Careful. Any incorrect tokens will just get `[UNK]``, so this could go horribly wrong
+            # Cast because return type is wrong.
+            token_id: int = cast(int, tokenizer.convert_tokens_to_ids(token.form)) or 0
+            # Explicitly check and warn if `unk_id` appears, but don't crash.
+            if unk_id is not None and token_id == unk_id and token.form != unk_token:
+                logger.warning(f"Token {token.form} was set to unk. This is wrong.")
+            token_ids.append([bos, token_id, eos])
+        else:
+            token_ids.append(tokenizer.encode(token.form))
+
+    return token_ids
+
+
+def _normalize_vocabulary_token(token: str, pre_tokenizer: PreTokenizer) -> str:
+    """Normalize a token that is not in the initial token vocabulary."""
+    # Add prefix space for byte tokenizers.
+    prefixed_token = f" {token}"
+    pretokenized_tokens: tuple[str, ...]
+    pretokenized_tokens, offsets = zip(*pre_tokenizer.pre_tokenize_str(prefixed_token))
+    # The first item is always the start of the token.
+    new_token = [pretokenized_tokens[0]]
+    # Loop over the subtokens and offsets.
+    for t, (s, _) in zip(pretokenized_tokens[1:], offsets[1:]):
+        # Do not prefix the token with a space if it starts with a metaspace.
+        if t.startswith("▁"):
+            new_token.append(t)
+        # If the character before the subtoken is a space, we have a
+        # multiword token. e.g., "room for the moon", which is split into
+        # ["room", "for", "the", "moon"].
+        # If it doesn't have a space, it is part of a complex multiword token,
+        # e.g., "chat-gpt", which is split into ["chat", "-", "gpt"].
+        elif prefixed_token[s - 1] == " ":
+            new_token.append(f" {t}")
+        else:
+            new_token.append(t)
+    normalized_token = "".join(new_token)
+
+    return normalized_token
+
+
+def create_tokenizer(
+    tokenizer: PreTrainedTokenizerFast,
+    vocabulary: list[str],
+    token_remove_regex: re.Pattern | None = None,
+) -> PreTrainedTokenizerFast:
+    """
+    Create a tokenizer by adding tokens to the vocabulary.
+
+    This function turns any tokenizer into a supertoken tokenizer. It does the following:
+    1. Turns the tokenizer model into a unigram model.
+    2. Adds a new pretokenizer, splitting on punctuation.
+    3. Adds all tokens in vocabulary to the model.
+    4. Removes any internal tokens that conform to the regex.
+
+    :param tokenizer: The tokenizer to use.
+    :param vocabulary: The vocabulary to use.
+    :param token_remove_regex: The regex to use to remove tokens from the vocabulary.
+    :return: The created tokenizer.
+    """
+    unk_token = cast(Optional[str], tokenizer.special_tokens_map.get("unk_token"))
+    pad_token = cast(Optional[str], tokenizer.special_tokens_map.get("pad_token"))
+    cleaned_vocabulary, backend_tokenizer = clean_and_create_vocabulary(tokenizer, vocabulary, token_remove_regex)
+    new_tokenizer = replace_vocabulary(backend_tokenizer, cleaned_vocabulary, unk_token, pad_token)
+
+    return PreTrainedTokenizerFast(tokenizer_object=new_tokenizer)
diff --git a/pyproject.toml b/pyproject.toml
index e6a1ae8..42ef2c9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,6 +64,7 @@ onnx = ["onnx", "torch"]
 # train also installs inference
 train = ["torch", "lightning", "scikit-learn", "skops"]
 inference = ["scikit-learn", "skops"]
+tokenizer = ["transformers"]
 
 [project.urls]
 "Homepage" = "https://github.com/MinishLab"
diff --git a/tests/conftest.py b/tests/conftest.py
index 670a9b5..bfb539e 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Any
+from typing import Any, cast
 
 import numpy as np
 import pytest
@@ -8,7 +8,7 @@
 from tokenizers import Tokenizer
 from tokenizers.models import BPE, Unigram, WordPiece
 from tokenizers.pre_tokenizers import Whitespace
-from transformers import AutoModel, AutoTokenizer
+from transformers import AutoModel, AutoTokenizer, PreTrainedTokenizerFast
 
 from model2vec.inference import StaticModelPipeline
 from model2vec.train import StaticModelForClassification
@@ -25,7 +25,9 @@ def mock_tokenizer(request: pytest.FixtureRequest) -> Tokenizer:
     tokenizer_type = request.param
 
     if tokenizer_type == "wordpiece":
-        model = WordPiece(vocab={token: idx for idx, token in enumerate(vocab)}, unk_token=unk_token)
+        model = WordPiece(
+            vocab={token: idx for idx, token in enumerate(vocab)}, unk_token=unk_token, max_input_chars_per_word=100
+        )
     elif tokenizer_type == "bpe":
         model = BPE(
             vocab={token: idx for idx, token in enumerate(vocab)},
@@ -35,17 +37,19 @@ def mock_tokenizer(request: pytest.FixtureRequest) -> Tokenizer:
             ignore_merges=True,
         )
     elif tokenizer_type == "unigram":
-        model = Unigram(vocab=[(token, 0.0) for token in vocab], unk_id=0)
+        model = Unigram(vocab=[(token, 0.0) for token in vocab], unk_id=0, byte_fallback=False)
+    else:
+        raise ValueError(f"Unsupported tokenizer type: {tokenizer_type}")
     tokenizer = Tokenizer(model)
-    tokenizer.pre_tokenizer = Whitespace()
+    tokenizer.pre_tokenizer = Whitespace()  # type: ignore  # Tokenizer issue
 
     return tokenizer
 
 
 @pytest.fixture(scope="function")
-def mock_berttokenizer() -> AutoTokenizer:
+def mock_berttokenizer() -> PreTrainedTokenizerFast:
     """Load the real BertTokenizerFast from the provided tokenizer.json file."""
-    return AutoTokenizer.from_pretrained("tests/data/test_tokenizer")
+    return cast(PreTrainedTokenizerFast, AutoTokenizer.from_pretrained("tests/data/test_tokenizer"))
 
 
 @pytest.fixture
diff --git a/tests/test_distillation.py b/tests/test_distillation.py
index a5ddac5..23f4680 100644
--- a/tests/test_distillation.py
+++ b/tests/test_distillation.py
@@ -9,7 +9,12 @@
 from pytest import LogCaptureFixture
 from transformers import AutoModel, BertTokenizerFast
 
-from model2vec.distill.distillation import _clean_vocabulary, _post_process_embeddings, distill, distill_from_model
+from model2vec.distill.distillation import (
+    clean_and_create_vocabulary,
+    distill,
+    distill_from_model,
+    post_process_embeddings,
+)
 from model2vec.model import StaticModel
 
 try:
@@ -17,7 +22,7 @@
     from huggingface_hub.errors import RepositoryNotFoundError
 except ImportError:
     # For huggingface_hub<0.25.0
-    from huggingface_hub.utils._errors import RepositoryNotFoundError
+    from huggingface_hub.utils._errors import RepositoryNotFoundError  # type: ignore
 
 rng = np.random.default_rng()
 
@@ -94,8 +99,8 @@ def test_distill_removal_pattern(
     # mock_auto_tokenizer.return_value = mock_berttokenizer
     mock_auto_model.return_value = mock_transformer
 
-    # The vocab size is 30522, but we remove 3 tokens: [CLS], [SEP], and [MASK]
-    expected_vocab_size = mock_berttokenizer.vocab_size - 3
+    # The vocab size is 30522, but we remove 998 tokens: [CLS], [SEP], and [MASK], and all [unused] tokens.
+    expected_vocab_size = mock_berttokenizer.vocab_size - 998
 
     static_model = distill_from_model(
         model=mock_transformer,
@@ -128,31 +133,21 @@ def test_distill_removal_pattern(
             token_remove_pattern="[...papapa",
         )
 
-    # Remove all tokens
-    with pytest.raises(ValueError):
-        static_model = distill_from_model(
-            model=mock_transformer,
-            tokenizer=mock_berttokenizer,
-            vocabulary=None,
-            device="cpu",
-            token_remove_pattern=".*",
-        )
-
 
 @pytest.mark.parametrize(
     "vocabulary, pca_dims, apply_zipf, sif_coefficient, expected_shape",
     [
-        (None, 256, True, None, (29525, 256)),  # Output vocab with subwords, PCA applied
-        (None, "auto", False, None, (29525, 768)),  # Subword, PCA set to 'auto'
-        (None, "auto", True, 1e-4, (29525, 768)),  # Subword, PCA set to 'auto'
-        (None, "auto", False, 1e-4, (29525, 768)),  # Subword, PCA set to 'auto'
+        (None, 256, True, None, (29524, 256)),  # Output vocab with subwords, PCA applied
+        (None, "auto", False, None, (29524, 768)),  # Subword, PCA set to 'auto'
+        (None, "auto", True, 1e-4, (29524, 768)),  # Subword, PCA set to 'auto'
+        (None, "auto", False, 1e-4, (29524, 768)),  # Subword, PCA set to 'auto'
         (None, "auto", True, 0, None),  # Sif too low
         (None, "auto", True, 1, None),  # Sif too high
-        (None, "auto", False, 0, (29525, 768)),  # Sif too low, but apply_zipf is False
-        (None, "auto", False, 1, (29525, 768)),  # Sif too high, but apply_zipf is False
-        (None, 1024, False, None, (29525, 768)),  # Subword, PCA set to high number.
-        (["wordA", "wordB"], 4, False, None, (29527, 4)),  # Custom vocab with subword, PCA applied
-        (None, None, True, None, (29525, 768)),  # No PCA applied
+        (None, "auto", False, 0, (29524, 768)),  # Sif too low, but apply_zipf is False
+        (None, "auto", False, 1, (29524, 768)),  # Sif too high, but apply_zipf is False
+        (None, 1024, False, None, (29524, 768)),  # Subword, PCA set to high number.
+        (["wordA", "wordB"], 4, False, None, (29526, 4)),  # Custom vocab with subword, PCA applied
+        (None, None, True, None, (29524, 768)),  # No PCA applied
     ],
 )
 @patch.object(import_module("model2vec.distill.distillation"), "model_info")
@@ -240,9 +235,9 @@ def test__post_process_embeddings(
     # Test that the function raises an error if the PCA dims are larger than the number of dimensions
     if pca_dims and pca_dims > embeddings.shape[1]:
         with pytest.raises(ValueError):
-            _post_process_embeddings(embeddings, pca_dims, None)
+            post_process_embeddings(embeddings, pca_dims, None)
 
-    processed_embeddings = _post_process_embeddings(embeddings, pca_dims, sif_coefficient)
+    processed_embeddings = post_process_embeddings(embeddings, pca_dims, sif_coefficient)
 
     # Assert the shape is correct
     assert processed_embeddings.shape == expected_shape
@@ -261,21 +256,18 @@ def test__post_process_embeddings(
 
 
 @pytest.mark.parametrize(
-    "preprocessed_vocabulary, added_tokens, expected_output, expected_warnings",
+    "added_tokens, expected_output, expected_warnings",
     [
-        # Case: duplicates ("word2") and an empty token ("")
-        (["word1", "word2", "word2", "word3", ""], ["word3"], ["word1", "word2"], ["Removed", "duplicate", "empty"]),
+        # Case: duplicates ("2010", "government") and an empty token ("")
+        (["2010", "government", "nerv", ""], ["nerv"], ["Removed", "duplicate", "empty"]),
         # Case: No duplicates, no empty tokens
-        (["wordA", "wordB", "wordC"], [], ["worda", "wordb", "wordc"], []),
-        # Case: Duplicate "wordB" and "wordA" already in added_tokens
-        (["wordA", "wordB", "wordC", "wordB"], ["worda"], ["wordb", "wordc"], ["Removed", "duplicate"]),
+        (["worda", "wordb", "wordc"], ["worda", "wordb", "wordc"], []),
         # Case: Only empty token (""), should return an empty list
-        ([""], [], [], ["Removed", "empty"]),
+        ([""], [], ["Removed", "empty"]),
     ],
 )
-def test__clean_vocabulary(
+def test_clean_and_create_vocabulary(
     mock_berttokenizer: BertTokenizerFast,
-    preprocessed_vocabulary: list[str],
     added_tokens: list[str],
     expected_output: list[str],
     expected_warnings: list[str],
@@ -283,8 +275,9 @@ def test__clean_vocabulary(
 ) -> None:
     """Test the _clean_vocabulary function."""
     with caplog.at_level("WARNING"):
-        cleaned_vocab = _clean_vocabulary(mock_berttokenizer.backend_tokenizer, preprocessed_vocabulary, added_tokens)
+        tokens, _ = clean_and_create_vocabulary(mock_berttokenizer, added_tokens, None)
 
+        cleaned_vocab = [token.form for token in tokens if not token.is_internal]
         # Check the cleaned vocabulary matches the expected output
         assert cleaned_vocab == expected_output
 
diff --git a/tests/test_tokenizer.py b/tests/test_tokenizer.py
new file mode 100644
index 0000000..8684ead
--- /dev/null
+++ b/tests/test_tokenizer.py
@@ -0,0 +1,123 @@
+import json
+
+import pytest
+from transformers import PreTrainedTokenizerFast
+
+from model2vec.tokenizer.model import _calculate_token_weight_for_unigram, _process_unigram, process_tokenizer
+from model2vec.tokenizer.normalizer import replace_normalizer
+from model2vec.tokenizer.pretokenizer import _FORBIDDEN_PRETOKENIZERS, _fix_single_pretokenizer, replace_pretokenizer
+from model2vec.tokenizer.tokenizer import _rename_added_token, create_tokenizer
+
+
+def test_fix_single_pretokenizer() -> None:
+    """Test the _fix_single_pretokenizer function."""
+    result = _fix_single_pretokenizer({"type": "ByteLevel", "add_prefix_space": False, "use_regex": True})
+    assert result == {"type": "ByteLevel", "add_prefix_space": True, "use_regex": False}
+
+    for tokenizer_type in _FORBIDDEN_PRETOKENIZERS:
+        result = _fix_single_pretokenizer({"type": tokenizer_type})
+        assert result is None
+
+    result = _fix_single_pretokenizer(
+        {"type": "Metaspace", "split": True, "prepend_scheme": "never", "replacement": "▁"}
+    )
+    assert result == {"type": "Metaspace", "replacement": "▁", "prepend_scheme": "always", "split": False}
+
+
+def test_replace_pretokenizer(mock_berttokenizer: PreTrainedTokenizerFast) -> None:
+    """Test the replace_pretokenizer function."""
+    tokenizer = replace_pretokenizer(mock_berttokenizer.backend_tokenizer)
+    assert tokenizer.pre_tokenizer is not None
+    assert tokenizer.pre_tokenizer.__class__.__name__ == "Metaspace"
+    assert tokenizer.pre_tokenizer.replacement == "▁"
+    assert tokenizer.pre_tokenizer.prepend_scheme == "always"
+    assert not tokenizer.pre_tokenizer.split
+
+    tokenizer.pre_tokenizer = None  # type: ignore
+    tokenizer = replace_pretokenizer(tokenizer)
+    assert tokenizer.pre_tokenizer is not None
+    assert tokenizer.pre_tokenizer.__class__.__name__ == "Metaspace"
+    assert tokenizer.pre_tokenizer.replacement == "▁"
+    assert tokenizer.pre_tokenizer.prepend_scheme == "always"
+    assert tokenizer.pre_tokenizer.split is False
+
+
+def test_replace_normalizer(mock_berttokenizer: PreTrainedTokenizerFast) -> None:
+    """Test the replace_normalizer function."""
+    tokenizer = replace_normalizer(mock_berttokenizer.backend_tokenizer)
+    assert tokenizer.normalizer is not None
+    assert tokenizer.normalizer.__class__.__name__ == "Sequence"
+
+    assert tokenizer.normalizer.normalize_str("Hello, World!") == "hello , world !"
+
+    tokenizer.normalizer = None  # type: ignore
+    tokenizer = replace_normalizer(tokenizer)
+    assert tokenizer.normalizer.normalize_str("Hello, World!") == "Hello , World !"
+
+
+@pytest.mark.parametrize(
+    "word,weight",
+    [
+        ("dog", 3),
+        ("cat", 3),
+        ("▁longer▁word", 14),
+        ("▁word", 6),
+        ("▁", 2),  # Single underscore
+        ("", 0),  # Empty string
+        ("▁a" * 100, 300),  # Long word with underscores
+    ],
+)
+def test_calculate_token_weight_for_unigram(word: str, weight: int) -> None:
+    """Test the _calculate_token_weight_for_unigram function."""
+    assert _calculate_token_weight_for_unigram(word) == weight
+
+
+def test_process_tokenizer(mock_berttokenizer: PreTrainedTokenizerFast) -> None:
+    """Test the process_tokenizer function."""
+    vocab = ["dog", "cat", "longer_word", "word", "a" * 100, "[UNK]"]
+    tokenizer_json = json.loads(mock_berttokenizer.backend_tokenizer.to_str())
+    tokenizer_json = process_tokenizer(tokenizer_json=tokenizer_json, pre_tokenized_tokens=vocab, unk_token="[UNK]")
+
+    assert tokenizer_json["model"]["type"] == "Unigram"
+    assert tokenizer_json["model"]["unk_id"] == 5  # Index of "[UNK]"
+    assert len(tokenizer_json["model"]["vocab"]) == 6
+    assert all(isinstance(token, tuple) and len(token) == 2 for token in tokenizer_json["model"]["vocab"])
+    for (x, _), y in zip(tokenizer_json["model"]["vocab"], vocab):
+        assert x == y, f"Expected {y}, but got {x}"
+
+
+def test_process_unigram() -> None:
+    """Test the _process_unigram function."""
+    vocab = ["dog", "cat", "longer_word", "word", "a" * 100, "[UNK]"]
+    orig_vocab = [("dog", 0), ("cat", 0)]
+    model = {"model": {"type": "Unigram", "vocab": orig_vocab}}
+    processed_model = _process_unigram(model, vocab, "[UNK]")
+    assert processed_model["model"]["type"] == "Unigram"
+    assert processed_model["model"]["unk_id"] == 5  # Index of "[UNK]"
+    assert len(processed_model["model"]["vocab"]) == 6
+    assert all(isinstance(token, list) and len(token) == 2 for token in processed_model["model"]["vocab"])
+
+    for (x, score), y in zip(processed_model["model"]["vocab"], vocab):
+        assert x == y, f"Expected {y}, but got {x}"
+        if x in orig_vocab:
+            assert score == 0
+
+    assert process_tokenizer(model, vocab, "[UNK]") == processed_model
+
+
+def test_rename_added_token() -> None:
+    """Test the _rename_added_token function."""
+    # Invalid input
+    result = _rename_added_token(None, "a", [{"content": "a", "id": 0}], ["a"])
+    assert result == [{"content": "a", "id": 0}]
+
+    # Rename 'a' to 'c'
+    result = _rename_added_token("a", "c", [{"content": "a"}], ["a"])
+    assert result == [{"content": "c", "id": 0}]
+
+
+def test_create_tokenizer(mock_berttokenizer: PreTrainedTokenizerFast) -> None:
+    """Test the create_tokenizer function."""
+    tokenizer = create_tokenizer(tokenizer=mock_berttokenizer, vocabulary=["dog", "catssssss"], token_remove_regex=None)
+    assert tokenizer.backend_tokenizer.get_vocab_size() == 29525
+    assert tokenizer.encode("catssssss") == [29524]
diff --git a/uv.lock b/uv.lock
index a6eed37..0a1cd3d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,22 +2,23 @@ version = 1
 requires-python = ">=3.9"
 resolution-markers = [
     "python_full_version >= '3.12'",
-    "python_full_version >= '3.10' and python_full_version < '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version == '3.10.*'",
     "python_full_version < '3.10'",
 ]
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.4"
+version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/7f/55/e4373e888fdacb15563ef6fa9fa8c8252476ea071e96fb46defac9f18bf2/aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745", size = 21977 }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b9/74/fbb6559de3607b3300b9be3cc64e97548d55678e44623db17820dbd20002/aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8", size = 14756 },
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
 ]
 
 [[package]]
 name = "aiohttp"
-version = "3.11.11"
+version = "3.11.18"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -29,83 +30,88 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/fe/ed/f26db39d29cd3cb2f5a3374304c713fe5ab5a0e4c8ee25a0c45cc6adf844/aiohttp-3.11.11.tar.gz", hash = "sha256:bb49c7f1e6ebf3821a42d81d494f538107610c3a705987f53068546b0e90303e", size = 7669618 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/75/7d/ff2e314b8f9e0b1df833e2d4778eaf23eae6b8cc8f922495d110ddcbf9e1/aiohttp-3.11.11-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a60804bff28662cbcf340a4d61598891f12eea3a66af48ecfdc975ceec21e3c8", size = 708550 },
-    { url = "https://files.pythonhosted.org/packages/09/b8/aeb4975d5bba233d6f246941f5957a5ad4e3def8b0855a72742e391925f2/aiohttp-3.11.11-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:4b4fa1cb5f270fb3eab079536b764ad740bb749ce69a94d4ec30ceee1b5940d5", size = 468430 },
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-    { name = "ipython", version = "8.31.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
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     { name = "mypy" },
     { name = "pre-commit" },
     { name = "pytest" },
@@ -829,6 +931,9 @@ onnx = [
     { name = "onnx" },
     { name = "torch" },
 ]
+tokenizer = [
+    { name = "transformers" },
+]
 train = [
     { name = "lightning" },
     { name = "scikit-learn" },
@@ -864,6 +969,7 @@ requires-dist = [
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     { name = "tqdm" },
     { name = "transformers", marker = "extra == 'distill'", specifier = "<=4.52.1" },
+    { name = "transformers", marker = "extra == 'tokenizer'" },
 ]
 
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