diff --git a/asreviewcontrib/semantic_clustering/semantic_clustering.py b/asreviewcontrib/semantic_clustering/semantic_clustering.py
index 18bf9ae..f9913aa 100644
--- a/asreviewcontrib/semantic_clustering/semantic_clustering.py
+++ b/asreviewcontrib/semantic_clustering/semantic_clustering.py
@@ -12,22 +12,63 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# import
+# import ASReview
+from tqdm import tqdm
 from asreview.data import ASReviewData
 
+# import numpy
+import numpy as np
 
-class SemanticClustering():
-    def __init__(self, data: ASReviewData):
-        self.data = data
+# import transformer autotokenizer and automodel
+from transformers import AutoTokenizer, AutoModel
 
-    # create ASReview data object
+# disable transformer warning
+from transformers import logging
+logging.set_verbosity_error()
 
+#import tqdm
 
-def load_data(ASReviewDataObject):
 
-    data = ASReviewDataObject.df[['title', 'abstract']].copy()
+def SemanticClustering(asreview_data_object):
+
+    # load data
+    print("Loading data...")
+    data = load_data(asreview_data_object)
+
+    # cut data for testing
+    data = data.iloc[:10, :]
+
+    # load scibert transformer
+    print("Loading scibert transformer...")
+    transformer = 'allenai/scibert_scivocab_uncased'
+
+    # load transformer and tokenizer
+    print("Loading tokenizer and model...")
+    tokenizer = AutoTokenizer.from_pretrained(transformer)
+    model = AutoModel.from_pretrained(transformer)
+
+    # tokenize abstracts and add to data
+    print("Tokenizing abstracts...")
+    data['tokenized'] = data['abstract'].apply(lambda x: tokenizer.encode(
+        x,
+        padding='longest',
+        add_special_tokens=True,
+        return_tensors="pt"))
+
+    print(data)
+
+
+def load_data(asreview_data_object):
+
+    # extract title and abstract, drop empty abstracts and reset index
+    data = asreview_data_object.df[['title', 'abstract']].copy()
     data['abstract'] = data['abstract'].replace('', np.nan, inplace=False)
     data.dropna(subset=['abstract'], inplace=True)
     data = data.reset_index(drop=True)
 
     return data
+
+
+if __name__ == "__main__":
+    filepath = "https://raw.githubusercontent.com/asreview/systematic-review-datasets/master/datasets/van_de_Schoot_2017/output/van_de_Schoot_2017.csv"
+    SemanticClustering(ASReviewData.from_file(filepath))