diff --git a/experiments/yousef/python_files/data.py b/experiments/yousef/python_files/data.py
new file mode 100644
index 0000000..5e35d0e
--- /dev/null
+++ b/experiments/yousef/python_files/data.py
@@ -0,0 +1,112 @@
+from torch.utils.data import DataLoader
+import pandas as pd
+import os
+import time
+import numpy as np
+
+class KaggleDataLoader:
+    """
+    Class for loading data in batches after it has been processed
+    """
+    def __init__(self, path_to_data):
+        pass
+
+
+class DataProcessor:
+    """
+    Class for standardising and processing data from raw form
+    """
+    def __init__(self, docs):
+        pass
+
+    def preprocess(self):
+        """ This is pretokenisation cleaning"""
+        pass
+
+    def process(self):
+        """ This is usually the tokeniser"""
+        pass
+
+    def postprocess(self):
+        """ This is post tokenisation cleaning"""
+        pass
+
+def generate_class_to_ent_map(unique_classes):
+    # sort classes
+    pass
+
+def df_from_text_files(path_to_dir):
+    filenames = [filename for filename in os.listdir(path_to_dir)]
+    records = [(filename.rstrip('.txt'), open(os.path.join(path_to_dir, filename), 'r').read()) for filename in filenames]
+    df = pd.DataFrame.from_records(records, columns=['id', 'text'])
+    return df
+
+def create_labels_doc_level(path_to_text_dir, path_to_ground_truth):
+    s = time.time()
+    df_ground_truth = pd.read_csv(path_to_ground_truth)
+    unique_labels = list(df_ground_truth.discourse_type.unique())
+    unique_labels = [f'{start_letter}-{label}' for label in unique_labels for start_letter in ['B', 'I']]
+    label_to_id_map = {
+        label: i for i, label in enumerate(
+            ['O'] + unique_labels
+        )
+    }
+
+    df_ground_truth.predictionstring = df_ground_truth.predictionstring.str.split()
+    df_ground_truth['label_ids'] = df_ground_truth.predictionstring.apply(lambda x: [int(x[0]), int(x[-1])])
+    df_ground_truth['labels'] = df_ground_truth[['discourse_type', 'label_ids']].apply(
+        lambda x: [f'B-{x.discourse_type}'] + [f'I-{x.discourse_type}']*(x.label_ids[-1] - x.label_ids[0]),
+        axis=1
+    )
+    df_ground_truth['range'] = df_ground_truth.label_ids.apply(lambda x: np.arange(x[0], x[1]+1))
+    df_ground_truth['labels'] = df_ground_truth.labels.apply(lambda x: [label_to_id_map[label] for label in x])
+
+    # this is kind of wrong?
+
+    df_texts = df_from_text_files(path_to_text_dir)
+    """df_texts.text = df_texts.text.str.replace('\n', ' ')
+    df_texts.text = df_texts.text.str.replace('\s+', ' ')
+    df_texts.text = df_texts.text.str.replace('(?<=\w) (?=[.,\/#!$%\^&\*;:{}=\-_`~()])', '')"""
+    df_texts.text = df_texts.text.str.strip()
+    df_texts['text_split'] = df_texts.text.str.split()
+    df_texts['labels'] = df_texts.text_split.apply(lambda x: len(x)*[label_to_id_map['O']])
+    df_texts = df_texts.merge(
+        df_ground_truth.groupby('id').agg({
+            'range': lambda x: np.concatenate(list(x)),
+            'labels': lambda x: np.concatenate(list(x))
+        }).rename(columns={'labels':'labels_temp'}),
+        on='id'
+    )
+    def update_inplace(x):
+        ids = x.range
+        new_labels = x.labels_temp
+        labels = np.array(x.labels, dtype=new_labels.dtype)
+        assert len(ids) == len(new_labels)
+        labels[ids] = new_labels
+        return list(labels)
+
+
+    df_texts.labels = df_texts.apply(lambda x: update_inplace(x), axis=1)
+
+
+    """ids = np.stack(df_texts.range)
+    print(ids.shape)
+    labels = np.stack(df_texts.labels)
+    print(labels.shape)
+    new_labels = np.stack(df_texts.labels)
+    print(new_labels.shape)
+
+    labels[ids] = new_labels
+
+    df_texts.labels = labels"""
+
+    print(time.time() - s)
+    print(df_texts.labels[df_texts.id == '0A0AA9C21C5D'])
+
+
+
+if __name__ == '__main__':
+    PATH = '../../../data/kaggle/feedback-prize-2021/train'
+    FILE_PATH = '../../../data/kaggle/feedback-prize-2021/train.csv'
+
+    create_labels_doc_level(PATH, FILE_PATH)