diff --git a/content/text_preprocessing.py b/content/text_preprocessing.py
deleted file mode 100644
index cdfa3ad0..00000000
--- a/content/text_preprocessing.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import pandas as pd
-import argparse
-import numpy as np
-import re # (https://docs.python.org/3/library/re.html) for tokenising textual data 
-import string # (https://docs.python.org/3/library/string.html) for string operations  
-
-# Creating the random instance
-rng = np.random.default_rng()
-
-class TextPreprocess:
-    """Text Preprocessing for a Natural Language Processing model."""
-
-
-    def cleantext(self, df, text_column, remove_stopwords = True, remove_punc = True):
-        """Function to clean text data by removing stopwords, tags and punctuation.
-
-        Parameters
-        ----------
-        df : pandas dataframe 
-            The dataframe housing the input data.
-        text_column : str
-            Column in dataframe whose text is to be cleaned.
-        remove_stopwords : bool
-            if True, remove stopwords from text
-        remove_punc : bool
-            if True, remove punctuation suymbols from text
-
-        Returns
-        -------
-        Numpy array 
-            Cleaned text.
-
-        """
-        data = df
-        # converting all characters to lowercase 
-        data[text_column] = data[text_column].str.lower()
-
-        # List of common stopwords taken from https://gist.github.com/sebleier/554280
-        stopwords = [ "a", "about", "above", "after", "again", "against", "all", "am", "an", "and", "any", "are", "as", "at", "be", "because", 
-            "been", "before", "being", "below", "between", "both", "but", "by", "could", "did", "do", "does", "doing", "down", "during",
-            "each", "few", "for", "from", "further", "had", "has", "have", "having", "he", "he'd", "he'll", "he's", "her", "here", 
-            "here's", "hers", "herself", "him", "himself", "his", "how", "how's", "i", "i'd", "i'll", "i'm", "i've", "if", "in", "into",
-            "is", "it", "it's", "its", "itself", "let's", "me", "more", "most", "my", "myself", "nor", "of", "on", "once", "only", "or",
-            "other", "ought", "our", "ours", "ourselves", "out", "over", "own", "same", "she", "she'd", "she'll", "she's", "should", 
-            "so", "some", "such", "than", "that", "that's", "the", "their", "theirs", "them", "themselves", "then", "there", "there's",
-            "these", "they", "they'd", "they'll", "they're", "they've", "this", "those", "through", "to", "too", "under", "until", "up",
-            "very", "was", "we", "we'd", "we'll", "we're", "we've", "were", "what", "what's", "when", "when's", "where", "where's",
-            "which", "while", "who", "who's", "whom", "why", "why's", "with", "would", "you", "you'd", "you'll", "you're", "you've",
-            "your", "yours", "yourself", "yourselves" ]
-
-        def remove_stopwords(data, column):
-            data[f'{column} without stopwords'] = data[column].apply(lambda x : ' '.join([word for word in x.split() if word not in (stopwords)]))
-            return data
-
-        def remove_tags(string):
-            result = re.sub('<*>','',string)
-            return result
-
-        # remove html tags and brackets from text 
-        if remove_stopwords:
-            data_without_stopwords = remove_stopwords(data, text_column)
-            data_without_stopwords[f'clean_{text_column}']= data_without_stopwords[f'{text_column} without stopwords'].apply(lambda cw : remove_tags(cw))
-        if remove_punc:
-            data_without_stopwords[f'clean_{text_column}'] = data_without_stopwords[f'clean_{text_column}'].str.replace('[{}]'.format(string.punctuation), ' ', regex = True)
-
-        X = data_without_stopwords[f'clean_{text_column}'].to_numpy()
-
-        return X 
-
-    def split_data (self, X, y, split_percentile):
-        """Function to split data into training and testing data.
-
-        Parameters
-        ----------
-        X : Numpy Array
-            Contains textual data.
-        y : Numpy Array
-            Contains target data.
-        split_percentile : int
-            Proportion of training to testing data.
-         
-
-        Returns
-        -------
-        Tuple 
-            Contains numpy arrays of test and training data.
-
-        """
-        y = np.array(list(map(lambda x: 1 if x=="positive" else 0, y)))
-        arr_rand = rng.random(X.shape[0])
-        split = arr_rand < np.percentile(arr_rand, split_percentile)
-        X_train = X[split]
-        y_train = y[split]
-        X_test =  X[~split]
-        y_test = y[~split]
-
-        return (X_train, y_train, X_test, y_test)
-   
-    
-    def sent_tokeniser (self, x):
-        """Function to split text into sentences.
-
-        Parameters
-        ----------
-        x : str
-            piece of text
-
-        Returns
-        -------
-        list 
-            sentences with punctuation removed.
-
-        """
-        sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', x)
-        sentences.pop()
-        sentences_cleaned = [re.sub(r'[^\w\s]', '', x) for x in sentences]
-        return sentences_cleaned
-    
-    def word_tokeniser(self, text):
-        """Function to split text into tokens.
-
-        Parameters
-        ----------
-        x : str
-            piece of text
-
-        Returns
-        -------
-        list 
-            words with punctuation removed.
-
-        """
-        tokens = re.split(r"([-\s.,;!?])+", text)
-        words = [x for x in tokens if (x not in '- \t\n.,;!?\\' and '\\' not in x)]
-        return words
-
-    def loadGloveModel(self, emb_path):
-        """Function to read from the word embedding file.
-
-        Returns
-        -------
-        Dict 
-            mapping from word to corresponding word embedding.
-
-        """
-        print("Loading Glove Model")
-        File = emb_path
-        f = open(File,'r')
-        gloveModel = {}
-        for line in f:
-            splitLines = line.split()
-            word = splitLines[0]
-            wordEmbedding = np.array([float(value) for value in splitLines[1:]])
-            gloveModel[word] = wordEmbedding
-        print(len(gloveModel)," words loaded!")
-        return gloveModel
-
-        
-    def text_to_paras(self, text, para_len): 
-        """Function to split text into paragraphs.
-
-        Parameters
-        ----------
-        text : str
-            piece of text
-            
-        para_len : int
-            length of each paragraph
-
-        Returns
-        -------
-        list 
-            paragraphs of specified length.
-
-        """
-        # split the speech into a list of words 
-        words = text.split()
-        # obtain the total number of paragraphs
-        no_paras = int(np.ceil(len(words)/para_len))
-        # split the speech into a list of sentences 
-        sentences = self.sent_tokeniser(text)
-        # aggregate the sentences into paragraphs
-        k, m = divmod(len(sentences), no_paras)
-        agg_sentences = [sentences[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(no_paras)]
-        paras = np.array([' '.join(sents) for sents in agg_sentences])
-        
-        return paras 
-        
diff --git a/site/index.md b/site/index.md
index ad8340c2..b026eacc 100644
--- a/site/index.md
+++ b/site/index.md
@@ -19,14 +19,6 @@ local copy of the `.ipynb` files, you can either
 [clone this repository](https://docs.github.com/en/github/creating-cloning-and-archiving-repositories/cloning-a-repository)
 or use the download icon in the upper-right corner of each tutorial.
 
-### Non-executable articles
-
-```{admonition} Help improve the tutorials!
-
-Want to make a valuable contribution to the tutorials? Consider contributing to
-these existing articles to help make them fully executable and reproducible!
-```
-
 ## Useful links and resources
 
 The following links may be useful: