diff --git a/src/delphi/eval/token_labelling.py b/src/delphi/eval/token_labelling.py
new file mode 100644
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+++ b/src/delphi/eval/token_labelling.py
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+"""
+This script creates labels for tokens in a sentence. 
+It takes the context of the token into account. 
+Additionally, it can visualize the sentences and their poart-of-speech (POS) tags.
+"""
+
+from typing import List
+
+import spacy  # pylint: disable=import-error
+from spacy.tokens import Doc  # pylint: disable=import-error
+
+
+# make sure the english language model capabilities are installed by the equivalent of:
+# python -m spacy download en_core_web_sm
+# Should be run once, initially. Download only starts if not already installed.
+spacy.cli.download("en_core_web_sm", False, False, "-q")
+
+
+CATEGORIES = {
+    "Starts with space": (lambda token: token.text.startswith(" ")),  # bool
+    "Capitalized": (lambda token: token.text[0].isupper()),  # bool
+    "POS Tag": (lambda token: token.pos_),  # 'NOUN', 'VB', ..
+    "Is Noun": (lambda token: token.pos_ == "NOUN"),  # redundant
+    "Is Verb": (lambda token: "VB" in token.tag_),  # redundant
+    "Is Adjective": (lambda token: token.pos_ == "ADJ"),  # redundant
+    "Is Adverb": (lambda token: token.pos_ == "ADV"),  # redundant
+    "Named Entity Type": (
+        lambda token: token.ent_type_ if token.ent_type_ != "" else token.ent_type_
+    ),  # False, 'PERSON', 'ORG', 'GPE', ..
+    "Dependency": (lambda token: token.dep_),  # 'nsubj', 'ROOT', 'dobj', ..
+}
+
+
+def label_tokens(sentences: List, tokenized: bool=True, verbose: bool = False) -> List[List]:
+    """
+    Labels tokens in a sentence. Takes the context of the token into account.
+
+    Parameters
+    ----------
+    sentences : List
+        A batch/list of sentences, each being a list of tokens.
+    tokenized : bool, optional
+        Whether the sentences are already tokenized, by default True. If the sentences 
+        are full strings and not lists of tokens, then set to False.
+    verbose : bool, optional
+        Whether to print the tokens and their labels to the console, by default False.  
+
+    Returns
+    -------
+    List[List]
+        Returns a list of sentences. Each sentence contains a list of its 
+        corresponding token length where each entry provides the labels/categories 
+        for the token. Sentence -> Token -> Labels
+    """
+    assert isinstance(sentences, list)
+    # Load english language model
+    nlp = spacy.load("en_core_web_sm")
+    # labelled tokens, List holding sentences holding tokens holding corresponding token labels
+    labelled_sentences = list()
+
+    for sentence in sentences:
+        if tokenized:
+            # sentence is a list of tokens
+            doc = Doc(nlp.vocab, words=sentence)
+            # Apply the spaCy pipeline, except for the tokenizer
+            for name, proc in nlp.pipeline:
+                if name != "tokenizer":
+                    doc = proc(doc)
+        else:
+            # sentence is a single string
+            doc = nlp(sentence)
+
+        labelled_tokens = list()  # List holding labels for all tokens of sentence
+
+        for token in doc:
+            labels = list()  #  The list holding labels of a single token
+            for _, category_check in CATEGORIES.items():
+                label = category_check(token)
+                labels.append(label)
+            # add current token's to the list
+            labelled_tokens.append(labels)
+
+            # print the token and its labels to console
+            if verbose is True:
+                print(f"Token: {token.text}")
+                print(" | ".join(list(CATEGORIES.keys())))
+                printable = [
+                    str(l).ljust(len(cname))
+                    for l, cname in zip(labels, CATEGORIES.keys())
+                ]
+                printable = " | ".join(printable)
+                print(printable)
+                print("---")
+        # add current sentence's tokens' labels to the list
+        labelled_sentences.append(labelled_tokens)
+
+        if verbose is True:
+            print("\n")
+
+    return labelled_sentences
+
+
+if __name__ == "__main__":
+    # result = label_tokens(
+    #     ["Hi, my name is Joshua.".split(" "), "The highway is full of car s, Peter.".split(" ")],
+    #     tokenized=True,
+    #     verbose=True,
+    # )
+    result = label_tokens(
+        ["Hi, my name is Joshua.", "The highway is full of car s, Peter."],
+        tokenized=False,
+        verbose=True,
+    )
+    print(result)